Design and Simulation of a Wireless EEG-Based Control System with Alpha Wave Extraction for Human Hand Prothesis Actuation

Design and Simulation of a Wireless EEG-Based Control System with Alpha Wave Extraction for Human Hand Prothesis Actuation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Design and Simulation of a Wireless EEG-Based Control System with Alpha Wave Extraction for Human Hand Prothesis Actuation Jules Ngono Mvondo, Léandre Nneme Nneme This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8176336/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper proposes the design and simulation of an electronic system for the real-time extraction of alpha waves (8–13 Hz) from electroencephalographic (EEG) signals to control a human hand prosthesis. The EEG signals are acquired non-invasively and shaped through analog filtering and amplification stages modeled in NI Multisim . These signals are digitized via the ADC of a primary Arduino Uno microcontroller and transmitted wirelessly using Bluetooth HC-05 modules to a secondary Arduino Uno. The slave unit interprets the alpha wave activity to drive five HS-311 servomotors that simulate finger movements in a prosthetic hand. A touch sensor enhances contextual control, allowing the prosthesis to distinguish between grasping and releasing actions. LED indicators are used for feedback during signal detection and command execution. Preliminary simulations and experimental validations demonstrate the feasibility and responsiveness of the system in detecting mental intention and performing basic prosthetic movements. This work lays the foundation for a cost-effective, brain-controlled assistive device using accessible hardware. EEG signal processing Alpha wave extraction Arduino Uno HS-311 servomotor Human hand prosthesis Bluetooth HC-05 Touch sensor Multisim simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. INTRODUCTION In the field of biomechanics, a clear distinction exists between orthotics and prosthetics. While orthotics assist or support existing limbs, prosthetics are artificial devices designed to replace missing body parts, typically lost due to trauma, disease, or congenital conditions. The loss of a limb has profound emotional, physical, and financial implications for an individual. Prosthetic devices thus play a critical role in restoring partial motor function and improving the quality of life of amputees. Among various types of prosthetic limbs, transradial prostheses replacing the forearm and hand are particularly important due to the functional complexity of the human hand. Designing such prostheses involves interdisciplinary integration of biomechanics, neuroscience, and mechatronics, giving rise to the field of biomechatronics. This discipline seeks to merge mechanical systems with human neuromuscular and skeletal structures to restore or enhance lost motor functions. Recent research has focused on improving the functionality, aesthetics, and control strategies of prosthetic hands. Studies on grip force distribution and task-oriented prosthesis design have emphasized the importance of intuitive and adaptive control mechanisms. Traditionally, electromyographic (EMG) signals have been employed for control; however, these methods often suffer from low repeatability due to inter-subject variability and lack of standardized acquisition protocols. In parallel, significant progress has been made in brain-computer interface (BCI) technologies, notably with the emergence of invasive systems such as Neuralink . However, such systems raise ethical, medical, and accessibility concerns, particularly in low-resource settings such as Cameroon. There is thus a growing interest in non-invasive control approaches that are affordable, safe, and user-friendly for populations with limited access to advanced healthcare. This work proposes a non-invasive, EEG-based control system for actuating a transradial prosthetic hand. The study is guided by the following research hypotheses: What non-invasive control algorithm can effectively activate a prosthetic hand based on EEG signals? Which hardware platforms are best suited for implementing this control architecture? Can the resulting prosthetic hand achieve precise and functional movement patterns in real-world scenarios? By addressing these questions, this paper aims to contribute to the development of low-cost, accessible neuroprosthetic technologies tailored to the needs of people with disabilities in developing countries. 2. METHODS AND TOOLS To address the research hypotheses outlined in the introduction, a structured methodology combining system modeling, electronic simulation, and experimental prototyping was adopted. The objective was to design a non-invasive neuroprosthetic control system based on EEG signal acquisition, signal shaping, wireless communication, and prosthetic actuation. 2.1. METHODS The methodology adopted in this study is based primarily on computer-aided simulation and system-level modeling. Two major schematic representations are used to guide the development process: The block diagram, which defines the main functional components and signal flow of the proposed EEG-based control system. The operational (flow) diagram, which illustrates the sequential logic governing signal processing and actuator control. The block diagram of the system is presented in Fig. 1 . The process begins with the acquisition of EEG signals using non-invasive electrodes. These signals are then transmitted to Arduino Uno 1, which acts as the master controller. The microcontroller also receives contextual input from a push button or touch sensor that provides user intent confirmation. Arduino Uno 1 processes the analog EEG signal and transmits the digital data wirelessly using a Bluetooth HC-05 module configured in master mode. This signal is received by a second microcontroller, Arduino Uno 2, which is equipped with an HC-05 module in slave mode. Arduino Uno 2 interprets the received data and activates the appropriate servomotors (HS-311) that drive the human hand prosthesis, resulting in either opening or closing motions depending on the EEG signal content and button status. 2.2. Tools (Hardware and Software) The design and implementation of the proposed EEG-controlled prosthetic hand system required the integration of various hardware and software components. The selection was guided by affordability, accessibility, and compatibility with embedded systems used in biomedical engineering. 2.2.1. Hardware Components EEG Signal Source : EEG signals are acquired non-invasively using surface electrodes placed on the scalp . These raw signals are typically low in amplitude and require analog signal conditioning before digital conversion. Arduino Uno Microcontrollers (×2) : Two Arduino Uno boards are employed. The first Arduino Uno (master) handles the acquisition and processing of the EEG signal. The second Arduino Uno (slave) receives the processed signal and controls the prosthetic actuators accordingly. HC-05 Bluetooth Modules (×2) : These modules enable wireless communication between the two microcontrollers using serial (UART) protocol. The module connected to the master Arduino is configured in master mode, and the one on the slave Arduino is set in slave mode. Touch Sensor / Push Button : A binary input device that provides intent validation. It ensures that motor actuation occurs only when the user explicitly desires it, thus reducing false activations due to background EEG noise. HS-311 Servo Motors (×5) : Each of the five servomotors corresponds to one finger of the prosthetic hand. The motors respond to the control signals generated based on the EEG signal interpretation. LED Indicators : Used as visual feedback elements to indicate system status such as signal reception, valid command detection, or execution of motor movements. 2.2.2. Software Tools NI Multisim : This software was used to simulate the analog signal conditioning circuits, including amplification and band-pass filtering to isolate alpha wave activity (typically in the 8–13 Hz range) from the EEG signals. Arduino IDE : The main platform for programming the Arduino Uno boards in C/C++. It supports code development, serial monitoring, and real-time testing. Proteus : Used to simulate the behavior of embedded systems and validate component interactions in a virtual environment before hardware implementation. It assists in visualizing microcontroller responses to simulated inputs. 2.3. Operational Diagram and Algorithm To ensure proper coordination between the various stages of EEG signal acquisition , preprocessing, transmission, and actuation, the system operates according to a structured algorithm. This algorithm is described in the form of a flowchart (see Fig. 2 ) , which provides a high-level view of the control logic governing the prosthetic hand system. The process begins with the acquisition of raw EEG signals via surface electrodes placed on the user's scalp. These analog signals are then monitored by the master Arduino Uno, which is also connected to a push button or touch sensor. The sensor serves as a validation mechanism, confirming the user's intention to trigger movement. This dual-input approach improves the system’s robustness by preventing false activations due to ambient EEG noise . If the push button is pressed, the Arduino performs analog-to-digital conversion (ADC) on the incoming EEG signal, transforming it into a format suitable for digital processing and transmission. The resulting data is sent via a Bluetooth HC-05 module configured in master mode. The signal is received by a second Arduino Uno (the slave unit), which is paired with a corresponding HC-05 Bluetooth module in slave mode. This unit performs signal interpretation, particularly focusing on the detection of alpha wave activity (8–13 Hz). Based on the presence or absence of significant alpha wave components, the Arduino executes a decision: either to open or close the prosthetic hand by activating a set of HS-311 servomotors. To provide the user with immediate system feedback, a set of LEDs are used to reflect the execution status, whether a command was successfully received, processed, and executed. 2.4. Implementation of the Electronic Control Device Based on the block diagram presented in Fig. 1 , the operational flowchart in Fig. 2 , and the technical specifications of hardware (such as Arduino Uno, HC-05 modules, servo motors, electrodes push buttons, and LEDs) and software tools (such as NI Multisim ,Arduino IDE, Proteus, and VSPE), the final conceptual step is the practical implementation of the EEG-based wireless control system. This section focuses on the integration of electronic components to create a functional prototype capable of interpreting EEG signals and controlling a prosthetic hand accordingly. The system implementation is centered around two Arduino Uno microcontrollers configured for wireless serial communication via Bluetooth. One microcontroller is responsible for EEG signal acquisition and transmission, while the other handles signal interpretation and actuator control. Safety and intention confirmation mechanisms are introduced using push buttons and a touch sensor to ensure reliable command execution. 2.4.1. Arduino-Based Wireless Control System with EEG Signal Monitoring To validate the proposed system architecture, a simulation model was first developed using NI Multisim , where EEG signals were emulated and conditioned using analog filtering and amplification circuits. The processed analog signals representing alpha wave activity in the 8–13 Hz range were applied to the analog input A0 of the first Arduino Uno microcontroller, which acts as the master node of the system. In addition to acquiring the EEG signal, Arduino Uno 1 includes a push button that serves as a signal validation input. This mechanism ensures that data transmission occurs only when explicitly triggered by the user, reducing the likelihood of unintentional activation due to environmental or biological noise. Upon pressing the button, the analog EEG signal is converted to digital format using the Arduino’s internal ADC. The digitized data is then transmitted wirelessly using an HC-05 Bluetooth module operating in master mode. On the receiving side, Arduino Uno 2, configured with an HC-05 Bluetooth module in slave mode, receives the data and performs signal analysis to determine the appropriate motor control action. Arduino Uno 2 is also connected to a touch sensor or secondary push button, which acts as a secondary confirmation layer before any physical action is executed. This ensures the system acts only when the user intends to move the prosthetic hand. Once validated, Arduino Uno 2 sends PWM control signals to five HS-311 servomotors, each corresponding to a finger of the prosthetic hand. Depending on the EEG signal content (representing "open" or "close" commands), the servomotors adjust their positions accordingly to replicate natural hand movements. The overall layout of the system, including the EEG headset,prosthetic hand, and integrated touch sensor,is illustrated in Fig. 3 . This virtual 3D model highlights the interaction between the brain computer interfaces and the electromechanical system. 3. System Architecture The architecture of the proposed EEG-controlled prosthetic hand is designed to ensure modularity, reliability, and non-invasive user interaction. It incorporates two main embedded control units, wireless serial communication, safety validation mechanisms, and electromechanical actuation of a virtual hand model. This section presents the functional organization of the system, including data flow, signal processing layers, and hardware integration. The complete configuration is illustrated in Fig. 4 , which summarizes the signal flow from EEG acquisition to prosthetic actuation. 3.1. Functional Overview The system is composed of four main functional layers: EEG Acquisition and Preprocessing Layer EEG signals are acquired non-invasively using scalp electrodes. These weak analog signals are simulated in NI Multisim , conditioned by amplification and filtering circuits , and fed into the analog input A0 of the first Arduino Uno (master). The signal is validated via a push button to ensure that only intentional commands are transmitted. Master Controller and Transmission Layer The first Arduino Uno (Master) digitizes the EEG signal using its built-in 10-bit ADC. It is connected to an HC-05 Bluetooth module configured in master mode. Upon push button validation, the signal is wirelessly transmitted to the second Arduino. Slave Controller and Decision Layer The second Arduino Uno (Slave) receives the transmitted data via an HC-05 module in slave mode. It includes a touch sensor or a second push button that acts as an additional validation layer before actuation. Upon confirmation, it interprets the data and determines whether to open or close the hand based on alpha wave detection. Actuation and Feedback Layer Five HS-311 servomotors are connected to the slave Arduino, each representing a finger of the prosthetic hand. Depending on the interpreted signal, the servomotors receive PWM signals to actuate the fingers in synchronized motion. LED indicators provide visual feedback about signal status, system activation, and command execution. 3.2. Communication and Signal Flow The system uses UART communication over Bluetooth to establish a wireless link between the two Arduino boards. The direction of communication is unidirectional, from the master (EEG signal acquisition) to the slave (decision-making and actuation). This structure allows the system to be lightweight and fast, while ensuring user safety and signal integrity. All interactions between analog and digital components are isolated and properly conditioned, ensuring that the transition from brain signal to motor action is both stable and secure. 3.3. System Reliability Features To reduce false activations caused by signal artifacts or unintended neural activity: Dual validation (push button and touch sensor) is implemented. LED indicators act as a user-friendly visual interface for monitoring command acknowledgment. The system remains in standby mode unless both EEG data and user confirmation are valid. 4. Result and Discussion This section presents the results obtained from the simulation of the EEG-based wireless control system for a human hand prosthesis. The simulation was carried out using Proteus 8 Professional, in coordination with NI Multisim for EEG signal preprocessing and VSPE (Virtual Serial Port Emulator) for wireless communication emulation between the master and slave Arduino microcontrollers. 4.1. EEG Signal Processing in NI Multisim The first step of the simulation consisted of importing EEG brain signals into the NI Multisim environment. These signals were filtered and amplified to isolate the alpha frequency band (8–13 Hz), which corresponds to relaxed mental states typically used for control in non-invasive brain–computer interfaces. The analog EEG signal was designed using standard biomedical amplifier circuits, simulated and validated to ensure the required gain and noise reduction. Figure 5 illustrates the EEG signal conditioning and amplification process implemented in NI Multisim. Once validated, the analog output was virtually exported to Proteus for further integration. 4.2. Integration in Proteus and VSPE-Based Communication After EEG signal generation, the next step was to create a simulation architecture in Proteus that included: Two Arduino Uno boards (Master and Slave) HC-05 Bluetooth modules Push buttons and a touch sensor Five HS-311 servomotors (for prosthetic fingers) LEDs for system state indication To simulate the wireless transmission of data, VSPE (Virtual Serial Port Emulator) was used. It created a virtual serial link between Proteus and the Arduino IDE, allowing real-time code execution and bidirectional data flow, even in the absence of physical Bluetooth modules. Figure 6 shows the configuration of the virtual serial ports in VSPE for establishing communication between Proteus and the Arduino IDE. 4.3. Custom Arduino C + + Implementation The firmware for both Arduino Uno units was developed in C++, compiled using avr-g++, and uploaded via the Arduino IDE. To ensure precise control and full transparency of logic, no external libraries were used. Instead, custom functions were written to perform the following core tasks: Acquisition and digitization of analog EEG signals on the master Arduino Validation of control commands using push button inputs UART-based transmission of EEG data via Bluetooth (HC-05) from master to slave Reception and interpretation of signals on the slave Arduino PWM signal generation to control five HS-311 servomotors based on user intent In the simulation setup, COM1 represented the HC-05 master module, associated with the transmission signal i, while COM2 represented the HC-05 slave module, linked to the reception signal j. To avoid conflicts and ensure reliable transmission, i ≠ j was maintained as a strict condition. Both modules operated at a common baud rate of 9600 bps, ensuring synchronous data flow. Once the communication between the two embedded control units was established using VSPE (Virtual Serial Port Emulator), the complete command-control cycle was tested through three distinct use cases: Opening of the prosthetic hand Closing of the prosthetic hand Load handling by the prosthetic hand 4.3.1. Opening of the Prosthetic Hand in the Presence of the Touch Sensor The opening action of the prosthetic hand was triggered under the following logical conditions: The a nalog EEG signal had a voltage within the range [0.0V, 4.0V] The touch sensor was active at LOW logic level All push buttons were in HIGH logic state Upon satisfying these conditions, the five HS-311 servomotors were commanded to rotate through an angle of − 180° , causing the prosthetic hand to release any held object. In parallel, two LED indicators were activated: The blue LED indicated an active Bluetooth connection between the EEG headset and the prosthetic controller. The red LED confirmed that the hand was executing the opening action. Figure 7 shows the simulation output in Proteus, where the prosthetic hand opens in response to the EEG signal and touch sensor activation. This condition simulates a real-world scenario in which the user, through relaxed mental activity (alpha waves), intentionally commands the prosthetic hand to open upon tactile validation. 4.3.2. Closing of the Prosthetic Hand in the Presence of the Touch Sensor The closing mechanism of the human hand prosthesis is activated when the following logical conditions are satisfied: The EEG signal voltage remains within the acceptable control range of [0.0 V, 4.0 V] The touch sensor is active at the HIGH logic level , indicating that the prosthetic hand is in physical contact with an object All push buttons within the system are also maintained at the HIGH logic level Upon meeting these conditions, the five HS-311 servomotors are commanded to rotate by + 180° , effectively causing the prosthetic fingers to grasp the object firmly. In this operational state, two LEDs serve as system status indicators: The blue LED confirms a stable Bluetooth communication link between the EEG headset and the prosthetic controller The green LED indicates that the hand closure action is actively being executed Figure 8 illustrates the simulation results in Proteus, showing the prosthetic hand closing upon EEG signal reception and HIGH touch sensor status. This logic ensures that the grasping action is only performed when the user is intentionally engaged, and the system verifies tactile contact before initiating the motion, thus enhancing safety and precision in manipulation tasks. 4.3.3. Load –Holding Behavior of the Prosthetic Hand Once the prosthetic hand has successfully grasped an object through the coordinated action of the servomotors, the system enters a load-holding state. This state represents the ability of the prosthetic hand to maintain a grip on an object with a stable mechanical configuration, even in the absence of continuous EEG stimulation. The behavior is defined as follows: The EEG signal remains within the operational range [0.0 V, 4.0 V] The touch sensor stays in the HIGH logic level, confirming continued contact with the object The push buttons are continuously monitored to remain at the HIGH state In this condition, the five HS-311 servomotors hold their position at + 180° , effectively maintaining a closed grip. No additional motion commands are issued unless a new EEG signal or user validation is detected. This ensures energy efficiency and mechanical stability, while minimizing the risk of unintentional release. To signal that the prosthesis is in a load-holding state, the following visual indicators are used: The blue LED remains ON, confirming continuous Bluetooth communication The green LED also remains ON, indicating that the hand remains closed and is actively holding an object Figure 9 shows the Proteus simulation illustrating the load-holding condition, with all servos locked in position and both status LEDs active. This behavior demonstrates the system's capability to simulate semi-autonomous manipulation by maintaining the grasp until an explicit opening command is received from the user. 5. CONCLUSION AND PERSPECTIVES In this study, a novel wireless computer-based control system was successfully designed and simulated to actuate a human hand prosthesis using non-invasive EEG signals. The EEG signals were first processed in NI Multisim, amplified and filtered to extract alpha wave patterns, and then digitized by an Arduino Uno microcontroller. The entire system was implemented in simulation using Proteus 8.13 SP0, with wireless communication emulated using VSPE (Virtual Serial Port Emulator), allowing reliable UART communication between the master and slave microcontrollers. Five HS-311 servomotors were employed to represent the fingers of a prosthetic hand, enabling grasping and releasing actions based on the user’s brain activity, validated through tactile and button sensors. A key part of the signal processing involved conditioning the analog EEG voltage values within a defined operating range of: y(x) = 0.00488759*x with x € [0, 1023] ( 1 ) This equation corresponds to the conversion from the Arduino's 10-bit ADC scale (0–1023) to actual voltage values in the range [0.0 V, 5.0 V] , though the system was designed to operate within a functional subset [0.0 V, 4.0 V] to ensure safety and robustness. The simulation results confirmed that: Brain signals, when validated with button and touch sensor inputs, can reliably trigger prosthetic movements. Communication between modules remained stable throughout the simulation. Servomotor response was immediate and consistent, validating the effectiveness of the control algorithm. This work opens multiple avenues for future research and physical prototyping: Integration of real EEG acquisition modules (e.g., MindWave, OpenBCI) to replace simulated signals. Noise-resilient digital filters for real-world EEG signal conditioning. Miniaturization and energy optimization for real-time wearable applications. Machine learning classification of brainwave patterns to allow more complex gesture control. Clinical testing in rehabilitation scenarios to evaluate usability for amputees or individuals with neuromuscular disorders. In conclusion, the proposed system demonstrates that non-invasive thought-controlled prosthetics are both technically feasible and promising, especially for resource-constrained environments such as sub-Saharan Africa. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of Data and material The EEG signal data used in this study were generated via simulation using NI-Multisim for academic research purposes. The datasets are not publicly available but may be obtained from the corresponding author upon reasonable request. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this article. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors. Author Contributions Ngono Mvondo Jules Adrien William * conceptualized the system, designed the architecture, conducted the simulations, and drafted the manuscript. Léandre Nneme Nneme contributed to the integration of hardware components, supported the Bluetooth communication setup, and assisted with system testing. Both authors critically reviewed and approved the final version of the manuscript. Acknowledgements The authors would like to express their sincere gratitude to the Laboratory of Computer Science, Engineering, and Automation at the University of Douala for providing technical support and access to simulation tools throughout this research. References A. Ecofin, “Kenya: David Gathu et Moses Kinuya démontrent qu’il est possible de fabriquer des prothèses commandées par le cerveau,” Agence Ecofin, Sept. 10, 2022. G. Fichtinger et al., “Anser EMT: the first open-source electromagnetic tracking platform for image-guided interventions,” Int. J. Comput. Assist. Radiol. 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Llantoy Sánchez, “Diseño e implementación del sistema electrónico para una prótesis transradial mioeléctrica,” B.Sc. thesis, Pontificia Univ. Católica del Perú, Dec. 2020. A. H. Zaidan, M. K. Wail, and A. A. Yaseen, “Design and Implementation of Upper Prosthetic Controlled Remotely by Flexible Sensor Glove,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 1105, no. 1, p. 012080, Jun. 2021. J. D. Setiawan et al., “Flexion and Extension Motion for Prosthetic Hand Controlled by Single‑Channel EEG,” in Proc. ICITACEE 2021, pp. 47–52, Sep. 2021. N. Kleczinski, “Un lycéen de 17 ans invente une prothèse de bras contrôlée par la pensée…”, NeozOne, May 5, 2022. “Arduino,” Wikipedia, Sept. 10, 2022. “EEG Mind Controlled Smart Prosthetic Arm – A Comprehensive Study,” ASTESJ, 2022. “A comprehensive study of EEG‑based control of artificial arms,” Redalyc, 2021. “A low‑cost robotic hand prosthesis with apparent haptic sense,” PMC, 2023. D. Elstob and E. L. Secco, “A low cost EEG based BCI prosthetic using motor imagery,” IJITCS, vol. 6, no. 1, Feb. 2016. M. F. Carballo Turcios and M. A. Gamero‑V., “Development of a hand prosthesis controlled by electroencephalographic signals using Arduino and Mindflex technology,” LACCEI Conf., 2024. “EEG‑Based Brain Computer Interface Prosthetic Hand using …,” The Scientific World Journal, 2021. “Mind Controlled Prosthetic Limb,” IJAEM, 2021. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":35349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBlock diagram of the EEG-based control system for human hand prosthesis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8176336/v1/4051cfd953ef1b76f088a93c.png"},{"id":98513916,"identity":"d5e9abf3-49e8-416e-ba1b-cdc7e0a1c7c7","added_by":"auto","created_at":"2025-12-18 12:15:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOperational flowchart of the EEG-based prosthetic hand control algorithm\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8176336/v1/6169c1e592886792ea9357a5.png"},{"id":98513922,"identity":"d52fe946-eb1c-4a27-bbd6-fb558eb11811","added_by":"auto","created_at":"2025-12-18 12:15:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVirtual model of the human hand prosthesis and the EEG headset in the presence of the touch sensor\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8176336/v1/328ceeadb4e8989e2cc7a009.png"},{"id":98513918,"identity":"924a1c31-99e5-4bd6-995b-7341fe8998a6","added_by":"auto","created_at":"2025-12-18 12:15:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall system architecture showing the flow from EEG signal acquisition to wireless communication and prosthetic hand actuation.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8176336/v1/1587601774dcf39dcdda8e58.png"},{"id":98624996,"identity":"856d1041-d99a-48a3-9877-27f3937eb156","added_by":"auto","created_at":"2025-12-19 17:08:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25974,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEEG signal simulation and conditioning in NI Multisim\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8176336/v1/73090f18f846e2d806171bed.png"},{"id":98625119,"identity":"f90b75a4-2e22-42bd-b614-f4378547eed6","added_by":"auto","created_at":"2025-12-19 17:08:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":16647,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVirtual serial port configuration using VSPE for communication between Proteus and Arduino IDE.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8176336/v1/0cf7573f9549e650361ac96c.png"},{"id":98513929,"identity":"e0cb20bf-413f-4e35-8492-dafbc0ceedd5","added_by":"auto","created_at":"2025-12-18 12:15:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":83503,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSimulation output in Proteus showing hand opening in response to EEG signal and touch sensor activation.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8176336/v1/bd9d122fb91b55bd797c68a1.png"},{"id":98624973,"identity":"3079fad4-bdf6-4f03-85cf-caf7c82f6d65","added_by":"auto","created_at":"2025-12-19 17:08:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":83897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteus simulation result showing prosthetic hand closure upon EEG signal reception and HIGH touch sensor status.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8176336/v1/f9e871afc0f6129e373b8140.png"},{"id":98513925,"identity":"3272ccf2-d295-423d-af16-d192ce483bdf","added_by":"auto","created_at":"2025-12-18 12:15:23","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":77301,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteus simulation illustrating load-holding state with active LEDs and stable servo positions.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8176336/v1/f5d2e5ee2bf83b9c37a8c139.png"},{"id":101304786,"identity":"11adad37-e856-42c1-a23b-0494fc765e5a","added_by":"auto","created_at":"2026-01-28 10:03:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1981481,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8176336/v1/4820cb00-b5c7-4a9c-98b6-36ba81c7a20e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Design and Simulation of a Wireless EEG-Based Control System with Alpha Wave Extraction for Human Hand Prothesis Actuation","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eIn the field of biomechanics, a clear distinction exists between orthotics and prosthetics. While orthotics assist or support existing limbs, prosthetics are artificial devices designed to replace missing body parts, typically lost due to trauma, disease, or congenital conditions. The loss of a limb has profound emotional, physical, and financial implications for an individual. Prosthetic devices thus play a critical role in restoring partial motor function and improving the quality of life of amputees.\u003c/p\u003e \u003cp\u003eAmong various types of prosthetic limbs, transradial prostheses replacing the forearm and hand are particularly important due to the functional complexity of the human hand. Designing such prostheses involves interdisciplinary integration of biomechanics, neuroscience, and mechatronics, giving rise to the field of biomechatronics. This discipline seeks to merge mechanical systems with human neuromuscular and skeletal structures to restore or enhance lost motor functions.\u003c/p\u003e \u003cp\u003eRecent research has focused on improving the functionality, aesthetics, and control strategies of prosthetic hands. Studies on grip force distribution and task-oriented prosthesis design have emphasized the importance of intuitive and adaptive control mechanisms. Traditionally, electromyographic \u003cb\u003e(EMG)\u003c/b\u003e signals have been employed for control; however, these methods often suffer from low repeatability due to inter-subject variability and lack of standardized acquisition protocols.\u003c/p\u003e \u003cp\u003eIn parallel, significant progress has been made in brain-computer interface \u003cb\u003e(BCI)\u003c/b\u003e technologies, notably with the emergence of invasive systems such as \u003cb\u003eNeuralink\u003c/b\u003e. However, such systems raise ethical, medical, and accessibility concerns, particularly in low-resource settings such as Cameroon. There is thus a growing interest in non-invasive control approaches that are affordable, safe, and user-friendly for populations with limited access to advanced healthcare.\u003c/p\u003e \u003cp\u003eThis work proposes a non-invasive, \u003cb\u003eEEG-based\u003c/b\u003e control system for actuating a transradial prosthetic hand. The study is guided by the following research hypotheses:\u003c/p\u003e \u003cp\u003eWhat \u003cb\u003enon-invasive control algorithm\u003c/b\u003e can effectively activate a prosthetic hand based on \u003cb\u003eEEG\u003c/b\u003e signals?\u003c/p\u003e \u003cp\u003eWhich hardware platforms are best suited for implementing this control architecture?\u003c/p\u003e \u003cp\u003eCan the resulting prosthetic hand achieve precise and functional movement patterns in real-world scenarios?\u003c/p\u003e \u003cp\u003eBy addressing these questions, this paper aims to contribute to the development of low-cost, accessible neuroprosthetic technologies tailored to the needs of people with disabilities in developing countries.\u003c/p\u003e"},{"header":"2. METHODS AND TOOLS","content":"\u003cp\u003eTo address the research hypotheses outlined in the introduction, a structured methodology combining system modeling, electronic simulation, and experimental prototyping was adopted. The objective was to design a non-invasive neuroprosthetic control system based on \u003cb\u003eEEG\u003c/b\u003e signal acquisition, signal shaping, wireless communication, and prosthetic actuation.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. METHODS\u003c/h2\u003e \u003cp\u003eThe methodology adopted in this study is based primarily on computer-aided simulation and system-level modeling. Two major schematic representations are used to guide the development process:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe block diagram, which defines the main functional components and signal flow of the proposed EEG-based control system.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe operational (flow) diagram, which illustrates the sequential logic governing signal processing and actuator control.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe block diagram of the system is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The process begins with the acquisition of \u003cb\u003eEEG\u003c/b\u003e signals using non-invasive electrodes. These signals are then transmitted to Arduino Uno 1, which acts as the master controller. The microcontroller also receives contextual input from a push button or touch sensor that provides user intent confirmation.\u003c/p\u003e \u003cp\u003eArduino Uno 1 processes the analog \u003cb\u003eEEG\u003c/b\u003e signal and transmits the digital data wirelessly using a Bluetooth \u003cb\u003eHC-05\u003c/b\u003e module configured in master mode. This signal is received by a second microcontroller, Arduino Uno 2, which is equipped with an \u003cb\u003eHC-05\u003c/b\u003e module in slave mode. Arduino Uno 2 interprets the received data and activates the appropriate servomotors \u003cb\u003e(HS-311)\u003c/b\u003e that drive the human hand prosthesis, resulting in either opening or closing motions depending on the EEG signal content and button status.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Tools (Hardware and Software)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe design and implementation of the proposed EEG-controlled prosthetic hand system required the integration of various hardware and software components. The selection was guided by affordability, accessibility, and compatibility with embedded systems used in biomedical engineering.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Hardware Components\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEEG Signal Source\u003c/b\u003e: EEG signals are acquired non-invasively using surface electrodes placed on the \u003cb\u003escalp\u003c/b\u003e. These raw signals are typically low in amplitude and require analog signal conditioning before digital conversion.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eArduino Uno Microcontrollers (\u0026times;2)\u003c/b\u003e: Two Arduino Uno boards are employed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe first Arduino Uno (master)\u003c/b\u003e handles the acquisition and processing of the EEG signal.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe second Arduino Uno (slave)\u003c/b\u003e receives the processed signal and controls the prosthetic actuators accordingly.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHC-05 Bluetooth Modules (\u0026times;2)\u003c/b\u003e: These modules enable wireless communication between the two microcontrollers using serial \u003cb\u003e(UART)\u003c/b\u003e protocol. The module connected to the master Arduino is configured in master mode, and the one on the slave Arduino is set in slave mode.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTouch Sensor / Push Button\u003c/b\u003e: A binary input device that provides intent validation. It ensures that motor actuation occurs only when the user explicitly desires it, thus reducing false activations due to background EEG noise.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHS-311 Servo Motors (\u0026times;5)\u003c/b\u003e: Each of the five servomotors corresponds to one finger of the prosthetic hand. The motors respond to the control signals generated based on the \u003cb\u003eEEG\u003c/b\u003e signal interpretation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLED Indicators\u003c/b\u003e: Used as visual feedback elements to indicate system status such as signal reception, valid command detection, or execution of motor movements.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Software Tools\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNI Multisim\u003c/b\u003e: This software was used to simulate the analog signal conditioning circuits, including amplification and band-pass filtering to isolate alpha wave activity (typically in the 8\u0026ndash;13 Hz range) from the EEG signals.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eArduino IDE\u003c/b\u003e: The main platform for programming the Arduino Uno boards in C/C++. It supports code development, serial monitoring, and real-time testing.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eProteus\u003c/b\u003e: Used to simulate the behavior of embedded systems and validate component interactions in a virtual environment before hardware implementation. It assists in visualizing microcontroller responses to simulated inputs.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Operational Diagram and Algorithm\u003c/h2\u003e \u003cp\u003e \u003cb\u003eTo ensure proper coordination between the various stages of EEG signal acquisition\u003c/b\u003e, preprocessing, transmission, and actuation, the system operates according to a structured algorithm. This algorithm is described in the form of a flowchart \u003cb\u003e(see\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, which provides a high-level view of the control logic governing the prosthetic hand system.\u003c/p\u003e \u003cp\u003eThe process begins with the acquisition of raw EEG signals via surface electrodes placed on the user's scalp. These analog signals are then monitored by the master Arduino Uno, which is also connected to a push button or touch sensor. The sensor serves as a validation mechanism, confirming the user's intention to trigger movement. This dual-input approach improves the system\u0026rsquo;s robustness by preventing false activations due to ambient \u003cb\u003eEEG noise\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eIf the push button is pressed, the Arduino performs analog-to-digital conversion \u003cb\u003e(ADC)\u003c/b\u003e on the incoming EEG signal, transforming it into a format suitable for digital processing and transmission. The resulting data is sent via a Bluetooth \u003cb\u003eHC-05\u003c/b\u003e module configured in master mode.\u003c/p\u003e \u003cp\u003eThe signal is received by a second Arduino Uno (the slave unit), which is paired with a corresponding HC-05 Bluetooth module in slave mode. This unit performs signal interpretation, particularly focusing on the detection of alpha wave activity \u003cb\u003e(8\u0026ndash;13 Hz).\u003c/b\u003e Based on the presence or absence of significant alpha wave components, the Arduino executes a decision: either to open or close the prosthetic hand by activating a set of \u003cb\u003eHS-311\u003c/b\u003e servomotors.\u003c/p\u003e \u003cp\u003eTo provide the user with immediate system feedback, a set of LEDs are used to reflect the execution status, whether a command was successfully received, processed, and executed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Implementation of the Electronic Control Device\u003c/h2\u003e \u003cp\u003eBased on the block diagram presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the operational flowchart in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and the technical specifications of hardware (such as Arduino Uno, HC-05 modules, servo motors, electrodes push buttons, and LEDs) and software tools (such as NI Multisim ,Arduino IDE, Proteus, and VSPE), the final conceptual step is the practical implementation of the EEG-based wireless control system. This section focuses on the integration of electronic components to create a functional prototype capable of interpreting EEG signals and controlling a prosthetic hand accordingly.\u003c/p\u003e \u003cp\u003eThe system implementation is centered around two Arduino Uno microcontrollers configured for wireless serial communication via Bluetooth. One microcontroller is responsible for EEG signal acquisition and transmission, while the other handles signal interpretation and actuator control. Safety and intention confirmation mechanisms are introduced using push buttons and a touch sensor to ensure reliable command execution.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Arduino-Based Wireless Control System with EEG Signal Monitoring\u003c/h2\u003e \u003cp\u003eTo validate the proposed system architecture, a simulation model was first developed using \u003cb\u003eNI Multisim\u003c/b\u003e, where EEG signals were emulated and conditioned using analog filtering and amplification circuits. The processed analog signals representing alpha wave activity in the 8\u0026ndash;13 Hz range were applied to the analog input \u003cb\u003eA0\u003c/b\u003e of the first Arduino Uno microcontroller, which acts as the master node of the system.\u003c/p\u003e \u003cp\u003eIn addition to acquiring the EEG signal, Arduino Uno 1 includes a push button that serves as a signal validation input. This mechanism ensures that data transmission occurs only when explicitly triggered by the user, reducing the likelihood of unintentional activation due to environmental or biological noise.\u003c/p\u003e \u003cp\u003eUpon pressing the button, the analog EEG signal is converted to digital format using the Arduino\u0026rsquo;s internal ADC. The digitized data is then transmitted wirelessly using an HC-05 Bluetooth module operating in master mode. On the receiving side, Arduino Uno 2, configured with an HC-05 Bluetooth module in slave mode, receives the data and performs signal analysis to determine the appropriate motor control action.\u003c/p\u003e \u003cp\u003eArduino Uno 2 is also connected to a touch sensor or secondary push button, which acts as a secondary confirmation layer before any physical action is executed. This ensures the system acts only when the user intends to move the prosthetic hand.\u003c/p\u003e \u003cp\u003eOnce validated, Arduino Uno 2 sends PWM control signals to five HS-311 servomotors, each corresponding to a finger of the prosthetic hand. Depending on the EEG signal content (representing \"open\" or \"close\" commands), the servomotors adjust their positions accordingly to replicate natural hand movements.\u003c/p\u003e \u003cp\u003eThe overall layout of the system, including the EEG headset,prosthetic hand, and integrated touch sensor,is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This virtual 3D model highlights the interaction between\u003c/p\u003e \u003cp\u003ethe brain computer interfaces and the electromechanical system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. System Architecture","content":"\u003cp\u003eThe architecture of the proposed EEG-controlled prosthetic hand is designed to ensure modularity, reliability, and non-invasive user interaction. It incorporates two main embedded control units, wireless serial communication, safety validation mechanisms, and electromechanical actuation of a virtual hand model.\u003c/p\u003e \u003cp\u003eThis section presents the functional organization of the system, including data flow, signal processing layers, and hardware integration. The complete configuration is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, which summarizes the signal flow from EEG acquisition to prosthetic actuation.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Functional Overview\u003c/h2\u003e \u003cp\u003eThe system is composed of four main functional layers:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEEG Acquisition and Preprocessing Layer\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEEG signals\u003c/b\u003e are acquired non-invasively using scalp electrodes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThese weak analog signals are simulated in \u003cb\u003eNI Multisim\u003c/b\u003e, conditioned by \u003cb\u003eamplification and filtering circuits\u003c/b\u003e, and fed into the analog input A0 of the first Arduino Uno (master).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe signal is validated via a push button to ensure that only intentional commands are transmitted.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMaster Controller and Transmission Layer\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe first Arduino Uno (Master) digitizes the EEG signal using its built-in 10-bit ADC.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIt is connected to an HC-05 Bluetooth module configured in master mode.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUpon push button validation, the signal is wirelessly transmitted to the second Arduino.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSlave Controller and Decision Layer\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe second Arduino Uno (Slave) receives the transmitted data via an HC-05 module in slave mode.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIt includes a touch sensor or a second push button that acts as an additional validation layer before actuation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUpon confirmation, it interprets the data and determines whether to open or close the hand based on alpha wave detection.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eActuation and Feedback Layer\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFive HS-311 servomotors are connected to the slave Arduino, each representing a finger of the prosthetic hand.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDepending on the interpreted signal, the servomotors receive PWM signals to actuate the fingers in synchronized motion.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLED indicators provide visual feedback about signal status, system activation, and command execution.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Communication and Signal Flow\u003c/h2\u003e \u003cp\u003eThe system uses UART communication over Bluetooth to establish a wireless link between the two Arduino boards. The direction of communication is unidirectional, from the master (EEG signal acquisition) to the slave (decision-making and actuation). This structure allows the system to be lightweight and fast, while ensuring user safety and signal integrity.\u003c/p\u003e \u003cp\u003eAll interactions between analog and digital components are isolated and properly conditioned, ensuring that the transition from brain signal to motor action is both stable and secure.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3. System Reliability Features\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo reduce false activations caused by signal artifacts or unintended neural activity:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDual validation (push button and touch sensor) is implemented.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLED indicators act as a user-friendly visual interface for monitoring command acknowledgment.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe system remains in standby mode unless both EEG data and user confirmation are valid.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Result and Discussion","content":"\u003cp\u003eThis section presents the results obtained from the simulation of the EEG-based wireless control system for a human hand prosthesis. The simulation was carried out using Proteus 8 Professional, in coordination with NI Multisim for EEG signal preprocessing and VSPE (Virtual Serial Port Emulator) for wireless communication emulation between the master and slave Arduino microcontrollers.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1. EEG Signal Processing in NI Multisim\u003c/h2\u003e \u003cp\u003eThe first step of the simulation consisted of importing EEG brain signals into the NI Multisim environment. These signals were filtered and amplified to isolate the alpha frequency band (8\u0026ndash;13 Hz), which corresponds to relaxed mental states typically used for control in non-invasive brain\u0026ndash;computer interfaces.\u003c/p\u003e \u003cp\u003eThe analog EEG signal was designed using standard biomedical amplifier circuits, simulated and validated to ensure the required gain and noise reduction. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the EEG signal conditioning and amplification process implemented in NI Multisim. Once validated, the analog output was virtually exported to Proteus for further integration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Integration in Proteus and VSPE-Based Communication\u003c/h2\u003e \u003cp\u003eAfter EEG signal generation, the next step was to create a simulation architecture in Proteus that included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTwo Arduino Uno boards (Master and Slave)\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHC-05 Bluetooth modules\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePush buttons and a touch sensor\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFive HS-311 servomotors (for prosthetic fingers)\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLEDs for system state indication\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo simulate the wireless transmission of data, VSPE (Virtual Serial Port Emulator) was used. It created a virtual serial link between Proteus and the Arduino IDE, allowing real-time code execution and bidirectional data flow, even in the absence of physical Bluetooth modules.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the configuration of the virtual serial ports in VSPE for establishing communication between Proteus and the Arduino IDE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Custom Arduino C\u0026thinsp;+\u0026thinsp;+\u0026thinsp;Implementation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe firmware for both Arduino Uno units was developed in C++, compiled using avr-g++, and uploaded via the Arduino IDE. To ensure precise control and full transparency of logic, no external libraries were used. Instead, custom functions were written to perform the following core tasks:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAcquisition and digitization of analog EEG signals on the \u003cb\u003emaster Arduino\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eValidation of control commands using push button inputs\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUART-based transmission of EEG data via Bluetooth (HC-05) from master to slave\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eReception and interpretation of signals on the \u003cb\u003eslave Arduino\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePWM signal generation to control five \u003cb\u003eHS-311 servomotors\u003c/b\u003e based on user intent\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn the simulation setup, COM1 represented the HC-05 master module, associated with the transmission signal i, while COM2 represented the HC-05 slave module, linked to the reception signal j. To avoid conflicts and ensure reliable transmission, i\u0026thinsp;\u0026ne;\u0026thinsp;j was maintained as a strict condition.\u003c/p\u003e \u003cp\u003eBoth modules operated at a common baud rate of 9600 bps, ensuring synchronous data flow. Once the communication between the two embedded control units was established using VSPE (Virtual Serial Port Emulator), the complete command-control cycle was tested through three distinct use cases:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eOpening of the prosthetic hand\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eClosing of the prosthetic hand\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLoad handling by the prosthetic hand\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1. Opening of the Prosthetic Hand in the Presence of the Touch Sensor\u003c/h2\u003e \u003cp\u003eThe opening action of the prosthetic hand was triggered under the following logical conditions:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe \u003cb\u003ea\u003c/b\u003enalog \u003cb\u003eEEG\u003c/b\u003e signal had a voltage within the range \u003cb\u003e[0.0V, 4.0V]\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe \u003cb\u003etouch sensor\u003c/b\u003e was active at \u003cb\u003eLOW\u003c/b\u003e logic level\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAll \u003cb\u003epush buttons\u003c/b\u003e were in \u003cb\u003eHIGH logic state\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eUpon satisfying these conditions, the five HS-311 servomotors were commanded to rotate through an angle of \u003cb\u003e\u0026minus;\u0026thinsp;180\u0026deg;\u003c/b\u003e, causing the prosthetic hand to release any held object.\u003c/p\u003e \u003cp\u003eIn parallel, two LED indicators were activated:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe blue LED indicated an active Bluetooth connection between the EEG headset and the prosthetic controller.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe red LED confirmed that the hand was executing the opening action.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the simulation output in Proteus, where the prosthetic hand opens in response to the EEG signal and touch sensor activation.\u003c/p\u003e \u003cp\u003eThis condition simulates a real-world scenario in which the user, through relaxed mental activity (alpha waves), intentionally commands the prosthetic hand to open upon tactile validation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2. Closing of the Prosthetic Hand in the Presence of the Touch Sensor\u003c/h2\u003e \u003cp\u003eThe closing mechanism of the human hand prosthesis is activated when the following logical conditions are satisfied:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe \u003cb\u003eEEG signal voltage\u003c/b\u003e remains within the acceptable control range of \u003cb\u003e[0.0 V, 4.0 V]\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe \u003cb\u003etouch sensor\u003c/b\u003e is active at the \u003cb\u003eHIGH logic level\u003c/b\u003e, indicating that the prosthetic hand is in physical contact with an object\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAll \u003cb\u003epush buttons\u003c/b\u003e within the system are also maintained at the \u003cb\u003eHIGH logic level\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eUpon meeting these conditions, the five HS-311 servomotors are commanded to rotate by \u003cb\u003e+\u0026thinsp;180\u0026deg;\u003c/b\u003e, effectively causing the prosthetic fingers to grasp the object firmly.\u003c/p\u003e \u003cp\u003eIn this operational state, two LEDs serve as system status indicators:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe blue LED confirms a stable Bluetooth communication link between the EEG headset and the prosthetic controller\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe green LED indicates that the hand closure action is actively being executed\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates the simulation results in Proteus, showing the prosthetic hand closing upon EEG signal reception and HIGH touch sensor status.\u003c/p\u003e \u003cp\u003eThis logic ensures that the grasping action is only performed when the user is intentionally engaged, and the system verifies tactile contact before initiating the motion, thus enhancing safety and precision in manipulation tasks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3. Load \u0026ndash;Holding Behavior of the Prosthetic Hand\u003c/h2\u003e \u003cp\u003eOnce the prosthetic hand has successfully grasped an object through the coordinated action of the servomotors, the system enters a load-holding state. This state represents the ability of the prosthetic hand to maintain a grip on an object with a stable mechanical configuration, even in the absence of continuous EEG stimulation.\u003c/p\u003e \u003cp\u003eThe behavior is defined as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe EEG signal remains within the operational range \u003cb\u003e[0.0 V, 4.0 V]\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe touch sensor stays in the HIGH logic level, confirming continued contact with the object\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe push buttons are continuously monitored to remain at the HIGH state\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn this condition, the five HS-311 servomotors hold their position at \u003cb\u003e+\u0026thinsp;180\u0026deg;\u003c/b\u003e, effectively maintaining a closed grip. No additional motion commands are issued unless a new EEG signal or user validation is detected. This ensures energy efficiency and mechanical stability, while minimizing the risk of unintentional release.\u003c/p\u003e \u003cp\u003eTo signal that the prosthesis is in a load-holding state, the following visual indicators are used:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe blue LED remains ON, confirming continuous Bluetooth communication\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe green LED also remains ON, indicating that the hand remains closed and is actively holding an object\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows the Proteus simulation illustrating the load-holding condition, with all servos locked in position and both status LEDs active.\u003c/p\u003e \u003cp\u003eThis behavior demonstrates the system's capability to simulate semi-autonomous manipulation by maintaining the grasp until an explicit opening command is received from the user.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. CONCLUSION AND PERSPECTIVES","content":"\u003cp\u003eIn this study, a novel wireless computer-based control system was successfully designed and simulated to actuate a human hand prosthesis using non-invasive EEG signals. The EEG signals were first processed in NI Multisim, amplified and filtered to extract alpha wave patterns, and then digitized by an Arduino Uno microcontroller.\u003c/p\u003e \u003cp\u003eThe entire system was implemented in simulation using Proteus 8.13 SP0, with wireless communication emulated using VSPE (Virtual Serial Port Emulator), allowing reliable UART communication between the master and slave microcontrollers. Five HS-311 servomotors were employed to represent the fingers of a prosthetic hand, enabling grasping and releasing actions based on the user\u0026rsquo;s brain activity, validated through tactile and button sensors.\u003c/p\u003e \u003cp\u003eA key part of the signal processing involved conditioning the analog EEG voltage values within a defined operating range of:\u003c/p\u003e \u003cp\u003e \u003cb\u003ey(x)\u0026thinsp;=\u0026thinsp;0.00488759*x\u003c/b\u003e with x \u0026euro; [0, 1023] (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis equation corresponds to the conversion from the Arduino's 10-bit ADC scale (0\u0026ndash;1023) to actual voltage values in the range \u003cb\u003e[0.0 V, 5.0 V]\u003c/b\u003e, though the system was designed to operate within a functional subset \u003cb\u003e[0.0 V, 4.0 V]\u003c/b\u003eto ensure safety and robustness.\u003c/p\u003e \u003cp\u003eThe simulation results confirmed that:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eBrain signals, when validated with button and touch sensor inputs, can reliably trigger prosthetic movements.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCommunication between modules remained stable throughout the simulation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eServomotor response was immediate and consistent, validating the effectiveness of the control algorithm.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis work opens multiple avenues for future research and physical prototyping:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIntegration of real EEG acquisition modules (e.g., MindWave, OpenBCI) to replace simulated signals.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNoise-resilient digital filters for real-world EEG signal conditioning.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMiniaturization and energy optimization for real-time wearable applications.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMachine learning classification of brainwave patterns to allow more complex gesture control.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eClinical testing in rehabilitation scenarios to evaluate usability for amputees or individuals with neuromuscular disorders.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn conclusion, the proposed system demonstrates that non-invasive thought-controlled prosthetics are both technically feasible and promising, especially for resource-constrained environments such as sub-Saharan Africa.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe EEG signal data used in this study were generated via simulation using NI-Multisim for academic research purposes. The datasets are not publicly available but may be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNgono Mvondo Jules Adrien William\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e conceptualized the system, designed the architecture, conducted the simulations, and drafted the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eL\u0026eacute;andre Nneme Nneme\u003c/strong\u003e contributed to the integration of hardware components, supported the Bluetooth communication setup, and assisted with system testing.\u003c/p\u003e\n\u003cp\u003eBoth authors critically reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to the Laboratory of Computer Science, Engineering, and Automation at the University of Douala for providing technical support and access to simulation tools throughout this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eA. Ecofin, \u0026ldquo;Kenya: David Gathu et Moses Kinuya d\u0026eacute;montrent qu\u0026rsquo;il est possible de fabriquer des proth\u0026egrave;ses command\u0026eacute;es par le cerveau,\u0026rdquo; Agence Ecofin, Sept. 10, 2022.\u003c/li\u003e\n\u003cli\u003eG. Fichtinger et al., \u0026ldquo;Anser EMT: the first open-source electromagnetic tracking platform for image-guided interventions,\u0026rdquo; Int. J. Comput. Assist. Radiol. Surg., vol. 13, pp. 919\u0026ndash;926, 2018.\u003c/li\u003e\n\u003cli\u003eK. Arun et al., \u0026ldquo;Design and Finite Element Analysis of Prosthetic Hand Controlled by Wireless Gestures for Differently-abled People,\u0026rdquo; Int. J. Eng. Adv. Technol., 2022.\u003c/li\u003e\n\u003cli\u003eGoogle, \u0026ldquo;Module Bluetooth HC‑05 \u0026ndash; Recherche,\u0026rdquo; Google Recherche, Jan. 12, 2023.\u003c/li\u003e\n\u003cli\u003eM. A. Rashid et al., \u0026ldquo;Preliminary Findings on EEG‑Controlled Prosthetic Hand for Stroke Patients Based on Motor Control,\u0026rdquo; in Lecture Notes in Electrical Engineering, vol. 979, Springer, 2022.\u003c/li\u003e\n\u003cli\u003eM. C. Mohan and M. Purushothaman, \u0026ldquo;Design and fabrication of prosthetic human hand using EEG and force sensor with Arduino microcontroller,\u0026rdquo; in Proc. ICONSTEM 2017, pp. 1083\u0026ndash;1086, Mar. 2017, doi: 10.1109/ICONSTEM.2017.8261367.\u003c/li\u003e\n\u003cli\u003eM. Furqan and M. Rathi, \u0026ldquo;Industrial Robotic Claw for Cottage Industries,\u0026rdquo; in Proc. iCoMET 2019, pp. 1\u0026ndash;6, Jan. 2019, doi: 10.1109/ICOMET.2019.8673426.\u003c/li\u003e\n\u003cli\u003eM. Dupont‑Besnard, \u0026ldquo;Cette proth\u0026egrave;se bionique peut \u0026ecirc;tre contr\u0026ocirc;l\u0026eacute;e par la pens\u0026eacute;e,\u0026rdquo; Numerama, Mar. 5, 2020.\u003c/li\u003e\n\u003cli\u003eA. J. Llantoy S\u0026aacute;nchez, \u0026ldquo;Dise\u0026ntilde;o e implementaci\u0026oacute;n del sistema electr\u0026oacute;nico para una pr\u0026oacute;tesis transradial mioel\u0026eacute;ctrica,\u0026rdquo; B.Sc. thesis, Pontificia Univ. Cat\u0026oacute;lica del Per\u0026uacute;, Dec. 2020.\u003c/li\u003e\n\u003cli\u003eA. H. Zaidan, M. K. Wail, and A. A. Yaseen, \u0026ldquo;Design and Implementation of Upper Prosthetic Controlled Remotely by Flexible Sensor Glove,\u0026rdquo; IOP Conf. Ser.: Mater. Sci. Eng., vol. 1105, no. 1, p. 012080, Jun. 2021.\u003c/li\u003e\n\u003cli\u003eJ. D. Setiawan et al., \u0026ldquo;Flexion and Extension Motion for Prosthetic Hand Controlled by Single‑Channel EEG,\u0026rdquo; in Proc. ICITACEE 2021, pp. 47\u0026ndash;52, Sep. 2021.\u003c/li\u003e\n\u003cli\u003eN. Kleczinski, \u0026ldquo;Un lyc\u0026eacute;en de 17 ans invente une proth\u0026egrave;se de bras contr\u0026ocirc;l\u0026eacute;e par la pens\u0026eacute;e\u0026hellip;\u0026rdquo;, NeozOne, May 5, 2022.\u003c/li\u003e\n\u003cli\u003e\u0026ldquo;Arduino,\u0026rdquo; Wikipedia, Sept. 10, 2022.\u003c/li\u003e\n\u003cli\u003e\u0026ldquo;EEG Mind Controlled Smart Prosthetic Arm \u0026ndash; A Comprehensive Study,\u0026rdquo; ASTESJ, 2022. \u003c/li\u003e\n\u003cli\u003e\u0026ldquo;A comprehensive study of EEG‑based control of artificial arms,\u0026rdquo; Redalyc, 2021. \u003c/li\u003e\n\u003cli\u003e\u0026ldquo;A low‑cost robotic hand prosthesis with apparent haptic sense,\u0026rdquo; PMC, 2023. \u003c/li\u003e\n\u003cli\u003eD. Elstob and E. L. Secco, \u0026ldquo;A low cost EEG based BCI prosthetic using motor imagery,\u0026rdquo; IJITCS, vol. 6, no. 1, Feb. 2016. \u003c/li\u003e\n\u003cli\u003eM. F. Carballo Turcios and M. A. Gamero‑V., \u0026ldquo;Development of a hand prosthesis controlled by electroencephalographic signals using Arduino and Mindflex technology,\u0026rdquo; LACCEI Conf., 2024. \u003c/li\u003e\n\u003cli\u003e\u0026ldquo;EEG‑Based Brain Computer Interface Prosthetic Hand using \u0026hellip;,\u0026rdquo; The Scientific World Journal, 2021. \u003c/li\u003e\n\u003cli\u003e\u0026ldquo;Mind Controlled Prosthetic Limb,\u0026rdquo; IJAEM, 2021.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"EEG signal processing, Alpha wave extraction, Arduino Uno, HS-311 servomotor, Human hand prosthesis, Bluetooth HC-05, Touch sensor, Multisim simulation","lastPublishedDoi":"10.21203/rs.3.rs-8176336/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8176336/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper proposes the design and simulation of an electronic system for the real-time extraction of alpha waves \u003cb\u003e(8\u0026ndash;13 Hz)\u003c/b\u003e from electroencephalographic \u003cb\u003e(EEG)\u003c/b\u003e signals to control a human hand prosthesis. The \u003cb\u003eEEG\u003c/b\u003e signals are acquired non-invasively and shaped through analog filtering and amplification stages modeled in \u003cb\u003eNI Multisim\u003c/b\u003e. These signals are digitized via the \u003cb\u003eADC\u003c/b\u003e of a primary Arduino Uno microcontroller and transmitted wirelessly using Bluetooth \u003cb\u003eHC-05\u003c/b\u003e modules to a secondary Arduino Uno. \u003cb\u003eThe slave\u003c/b\u003e unit interprets the alpha wave activity to drive five \u003cb\u003eHS-311\u003c/b\u003e servomotors that simulate finger movements in a prosthetic hand. A touch sensor enhances contextual control, allowing the prosthesis to distinguish between grasping and releasing actions. \u003cb\u003eLED\u003c/b\u003e indicators are used for \u003cb\u003efeedback\u003c/b\u003e during signal detection and command execution. Preliminary simulations and experimental validations demonstrate the feasibility and responsiveness of the system in detecting mental intention and performing basic prosthetic movements. This work lays the foundation for a cost-effective, brain-controlled assistive device using accessible hardware.\u003c/p\u003e","manuscriptTitle":"Design and Simulation of a Wireless EEG-Based Control System with Alpha Wave Extraction for Human Hand Prothesis Actuation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 12:15:18","doi":"10.21203/rs.3.rs-8176336/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"42896988-276e-4b35-92c3-e2c84db9bbe1","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T09:55:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 12:15:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8176336","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8176336","identity":"rs-8176336","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}