Neuromorphic Neuromodulation: A Low-Power Edge-Training Framework for the Future of Personalized and Closed-Loop Neurostimulation

Neuromorphic Neuromodulation: A Low-Power Edge-Training Framework for the Future of Personalized and Closed-Loop Neurostimulation | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Neuromorphic Neuromodulation: A Low-Power Edge-Training Framework for the Future of Personalized and Closed-Loop Neurostimulation View ORCID Profile Luis Fernando Herbozo Contreras , View ORCID Profile Leping Yu , View ORCID Profile Zhaojing Huang , View ORCID Profile Isabelle Aguilar , Armin Nikpour , View ORCID Profile Omid Kavehei doi: https://doi.org/10.1101/2025.09.22.25336341 Luis Fernando Herbozo Contreras 1 School of Biomedical Engineering, The University of Sydney , Sydney, 2006, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luis Fernando Herbozo Contreras For correspondence: luis.herbozocontreras{at}sydney.edu.au omid.kavehei{at}sydney.edu.au Leping Yu 1 School of Biomedical Engineering, The University of Sydney , Sydney, 2006, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Leping Yu Zhaojing Huang 1 School of Biomedical Engineering, The University of Sydney , Sydney, 2006, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zhaojing Huang Isabelle Aguilar 1 School of Biomedical Engineering, The University of Sydney , Sydney, 2006, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Isabelle Aguilar Armin Nikpour 2 Comprehensive Epilepsy Service and Department of Neurology, Royal Prince Alfred Hospital , Sydney, 2050, New South Wales, Australia 3 Faculty of Medicine and Health, The University of Sydney , Sydney, 2006, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Omid Kavehei 1 School of Biomedical Engineering, The University of Sydney , Sydney, 2006, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Omid Kavehei For correspondence: luis.herbozocontreras{at}sydney.edu.au omid.kavehei{at}sydney.edu.au Abstract Full Text Info/History Metrics Preview PDF Abstract Epilepsy affects approximately 1% of the global population, with 30-40% of cases resistant to conventional pharmacological treatments. Current neurostimulation offers a different perspective by sending electrical stimulation to the brain. However, these devices rely on cloud-dependent AI, which introduces significant challenges, including high false positive rates, latency issues, and privacy concerns. Here we present a neuromorphic framework for real-time seizure detection and prediction, directly implemented on a neuromorphic SOC with on-chip learning capabilities. By leveraging spiking neural networks and few-shot edge learning, our system enables continual, patient-specific adaptation without requiring data transmission or cloud connectivity. For a detection framework, we pre-train a model using the TUH dataset and deploy it on the BrainChip Akida neuromorphic processor, enabling few-shot personalization on long-term recordings from the EPILEPSIAE dataset. For seizure prediction, we demonstrate robust performance on both scalp and intracranial EEG from Children’s Hospital Boston (CHB-MIT) and Freiburg (FB) datasets using a leave-one-seizure-out approach for training at the edge. For all the datasets, we achieved commendable and superior performance to state-of-the-art neural networks in metrics as AUROC, Sensitivity, and False Positive Rates (FPR), with a memory footprint drastically reduced through quantization and energy consumption orders of magnitude below conventional processors. This work represents a foundational advance toward autonomous, closed-loop neurostimulation systems capable of learning and adapting at the edge for newer generation of precision neurotechnology. Epilepsy is a chronic neurological disorder affecting more than 65 million people globally, characterized by recurrent seizures arising from aberrant electrical discharges in the brain. Clinical manifestations span from transient lapses of awareness to prolonged convulsive episodes, all of which impose profound burdens on the patient’s quality of life. Although advances in pharmacotherapy have improved outcomes, approximately 30–40% of individuals remain refractory to medication, highlighting the need for alternative interventions [ 1 , 2 ]. Neurostimulation modalities such as the Responsive Neurostimulation System (RNS) have demonstrated efficacy in reducing seizure frequency in drug-resistant cohorts by sensing and sending electrical stimulation to the brain. Nevertheless, these systems exhibit several inherent limitations. First, existing neurostimulation platforms rely on pre-trained detection models that often inadequately represent the considerable inter-patient heterogeneity in seizure morphology and dynamics, yielding elevated rates of both false positives and false negatives. Second, dependence on cloud-based inference pipelines leads to communication latency during acute seizure events and introduces potential privacy vulnerabilities [ 3 ]. Third, power and bandwidth constraints on implantable devices restrict intracranial electroencephalography (iEEG) telemetry to approximately one hour per day. While extending this window of data could enhance model training, it would accelerate battery depletion and compromise device longevity. Although model generalization measured via performance on distinct out-of-sample datasets is the prevailing metric for assessing adaptability to novel data, it does not necessarily predict performance on the continuously evolving biosignals of a single patient [ 4 ]. Accordingly, we advocate shifting the focus from population-level generalization to individual-level personalization, wherein the detection algorithm continuously refines itself to an individual’s unique neural signature. 0.1 System on a chip (SoC) for Seizure Monitoring Most SoC-based epilepsy management systems rely on patient-specific learning. A recent design [ 5 ] adopts a patient-independent approach, an important step toward real-world deployment, though its evaluation reports only sensitivity, specificity, and accuracy rather than AUROC. The first ASIC with on-chip ADMM-based Sup- port Vector Machine (SVM) training enables real-time adaptation to EEG dynamics through optimizations such as AEO, a Newton–Raphson divider, and efficient matrix operations [ 6 ], achieving 92% sensitivity and 99.1% specificity with 0.57 false alarms per hour (98.6%/99.7%, 0.18/h in detection) while consuming 2.31 mW, up to 299 × more energy-efficient than an ARM Cortex-M3. More recently, energy-efficient SoCs for closed-loop epilepsy management have emerged. One design incorporating one-shot learning and online tuning achieved 0.97 µ J/class, 97.8% sensitivity, 99.5% specificity, sub-1 s latency, and a 1.8 × sensitivity boost [ 7 ]. The SOUL classifier, an unsupervised online learning system, achieves 97.5% sensitivity and 95% specificity on scalp and intracranial EEG while consuming 1.5 nJ per classification [ 8 ]. The NeuralTree SoC supports 256-channel recording and closed-loop neuromodulation with a tree-structured neural network, achieving 0.227 µ J per classification with 95.6%/94% sensitivity and 96.8%/96.9% specificity, and integrates a high-voltage neurostimula- tor for EEG/iEEG recordings and in vivo experiments, including Parkinson’s tremor detection [ 9 ]. BioAIP offers a scalable, reconfigurable platform for personalized health monitoring, achieving 99.84% accuracy at 32.1 µ W, highlighting its potential for wear- able devices [ 10 ]. A closed-loop neuromodulation chipset with 2-level classification achieves 97.8% sensitivity, tolerates 1.5 Vpp common-mode interference, rejects stimulation artifacts by 35 dB within 0.5 ms, and operates at low power, making it suitable for reliable, real-time, long-term implantable applications [ 11 ]. A common limitation of previous SoC-based seizure detection studies is their reliance on the CHB-MIT scalp EEG dataset. While widely used, this dataset is relatively short and lacks long interictal periods or intracranial EEG recordings, both critical for assessing long-term detection and prediction performance. Personalized medicine, which aligns therapeutic strategies to an individual’s physiological profile, has demonstrated superior clinical utility across numerous domains. In epilepsy management, real-time, patient-specific adaptation of seizure detection and forecasting algorithms promises to revolutionize neurostimulation efficacy. However, realizing such personalization necessitates overcoming challenges in real-time signal processing, ultra-low-power operation, and resource-constrained computation [ 12 ]. 1 Background Neuromorphic computing offers a compelling pathway to meet these demands by replicating the brain’s event-driven, energy-efficient processing paradigm. Compared to conventional digital architectures, neuromorphic systems can reduce computational energy consumption by several orders of magnitude [ 13 ], while supporting on-chip learning for continuous model adaptation without cloud reliance [ 14 , 15 ]. Moreover, integrating continual learning strategies enables algorithms to assimilate new data incrementally without catastrophic forgetting, ensuring sustained performance during physiological drift, whether due to electrode repositioning, evolution of seizures, or pharmacological effects. This capability is crucial for long-term monitoring applications such as EEG-based brain–computer interfaces, where signal characteristics evolve over time [ 16 , 17 ]. In this study, we present a personalized neuromorphic framework for on-device seizure detection and prediction that trains models directly on low-power implantable hardware with SNNs to adapt individual neural dynamics, and employs few-shot learning to maintain robust performance over extended patients. This approach addresses the critical limitations of current neurostimulation devices, offering a scalable solution for privacy-preserving, low-latency, low-power, and adaptive seizure interventions. 2 Novelty and Significance This is the first study that demonstrates low-power and low-computational personalization at the edge on a large long-term epilepsy dataset for seizure detection and prediction at the edge on 3 widely used datasets. The following statements are highlights of our studies: Continual learning drives sustained performance gains as new data arrive flow into detection and prediction models. Edge adaptation leads to reduction in false positive rates while incrementing sensitivity. Robust personalization is achieved with as little as three minutes of patient-specific seizure data (15-shot learning). All streaming data remain strictly on-device, emulating an implantable system that adapts in real time while preserving privacy and eliminating the need for external communication. Training restricted to the final layer on BrainChip Akida hardware enables drastic reductions in computational cost across all datasets. 3 Datasets This study employs four datasets (1 iEEG and 3 Scalp-EEG). For the detection frame- work, we used the two largest EEG datasets: the Temple University Hospital EEG (TUH-EEG) Corpus and the European EPILEPSIAE dataset. The TUH-EEG Corpus, comprising over 642 patient recordings, serves as the principal training resource due to its extensive variety of seizure types and recording conditions. From this dataset, we partitioned 592 patients for model training and reserved 50 independent sessions for inference validation. Due to its large scale and heterogeneity, the TUH-EEG Cor- pus provides a robust foundation for initial model development. Subsequently, we utilized the EPILEPSIAE dataset, which is comprised of continuous, long-term scalp EEG recordings from 30 patients, to train and create personalized models via few-shot transfer learning. Given the rarity of ictal events (seizure events) in real-world monitoring, the EPILEPSIAE dataset exhibits a highly imbalanced task, with inter-ictal (non-seizure) segments vastly outnumbering ictal segments. This imbalance presents both a challenge and an opportunity: it reflects clinical monitoring conditions while forcing our adaptive algorithms to contend with realistic data distributions during on-device personalization. Both datasets present the same number of channels, which makes it practical for this study. For the Prediction Framework, we used two datasets: The Boston Children’s Hospital-MIT (CHB-MIT) and the Freiburg Hospital (FB) dataset iEEG. The CHB- MIT dataset comprises scalp EEG (sEEG) recordings from 23 pediatric patients, totaling 844 h of continuous data and 163 seizures [ 18 ]. Recordings were acquired using 22 electrodes at a sampling rate of 256 Hz. Interictal periods are defined as the intervals starting at least 4 h before a seizure onset and ending at least 4 h after seizure termination. In cases where multiple seizures occur in close succession, specifically within 30 minutes of each other, they are treated as a single event, with the onset time of the earliest seizure taken as the combined seizure onset. For the seizure prediction task, we focus only on patients experiencing fewer than 10 seizures per day, as predicting seizures for individuals averaging one every 2 h is less clinically relevant. Applying these criteria yields 13 patients with adequate data defined as having at least three leading seizures and 3 h of interictal recordings. The FB dataset comprises intracranial EEG (iEEG) recordings from 21 patients diagnosed with refractory epilepsy. Due to restricted availability, recordings from 13 patients were utilized in this study. Signals were acquired at a sampling rate of 256 Hz. For each patient, six channels were selected from intracranial contacts, including three located within epileptogenic regions and three from non-epileptogenic (remote) areas. The dataset provides at least 50 minutes of pre-ictal activity and 24 hours of interictal recordings per patient. All the datasets used in this study are described in Table 1 . View this table: View inline View popup Download powerpoint Table 1. Summary of EEG datasets used in this study. 4 Methodology Fig. 1 illustrates our proposed edge-based personalized seizure detection and prediction framework. Unlike conventional epilepsy management methods that rely on cloud- or server-based AI, our approach integrates a neuromorphic system-on-chip with existing closed-loop neurostimulation devices to enable on-device intelligence without exceeding stringent power budgets. The framework employs spike-encoded continuous STFT representations of EEG signals, processed through an offline-trained convolutional feature extractor on the BrainChip Akida, with only the final classifier adapted online to patient-specific data. For seizure detection, a CNN trained on the TUH-EEG corpus is quantized, converted to a spiking neural network, and edge-refined using the EPILEP- SIAE dataset. For seizure prediction, a similar pipeline is applied to the Freiburg iEEG and CHB-MIT dataset, with models pre-trained on a GPU and then updated at the edge, ensuring adaptability and independence from cloud connectivity. Download figure Open in new tab Fig. 1. Edge-based personalized seizure detection and prediction frameworks. Conventional Methods for treating epilepsy rely on AI outside the device, while our proposed method envisions an efficient neuromorphic-AI system-on-chip closed-loop neurostimulation device without compromising power consumption (A). Continuous STFT of EEG images is spike-encoded and passed through a frozen, pre-trained convolutional feature extractor at the edge. Only the final classifier layer is updated on-chip via online few-shot learning, reducing compute and memory overheads (B). For the detection framework, a CNN is trained on a large-scale dataset (TUH), quantized to mixed precision and converted to a spiking neural network (SNN), and edge-adapted with patient-specific data from a long-term recording dataset (EPILEPSIAE) for patient-specific (C). For the prediction pipeline, the same methodology is applied to the Freiburg and CHB-MIT datasets. Because of limited patient numbers, individualized models are pre-trained rather than a global model and subsequently refined on previously unseen patient data at the edge, yielding an adaptable cloud-independent approach (D). 4.1 Pre-Processing To address challenges inherent in raw EEG signals, we employed two preprocess- ing techniques: Independent Component Analysis (ICA) and the Short-Time Fourier Transform (STFT). EEG recordings were first segmented into 12-second intervals, after which ICA was applied to perform Blind Source Separation (BSS), decomposing the signal into 19 statistically independent components. This process is represented in Eq. (1) , where T denotes the EEG signal matrix, M captures the temporal dynamics, and A contains the spatial weights corresponding to the topographic maps. To identify components related to ocular artifacts, we computed the Pearson correlation between each independent source and the ‘FP1’ and ‘FP2’ EEG channels. Components exhibiting strong correlations with eye movements were removed, yielding artifact-reduced EEG signals. Following this cleaning step, we applied the STFT with a 1-second window (250 samples) and 50% overlap, while also discarding the DC component. This transformation resulted in data shaped as ( N × 23 × 125), where N is the number of electrodes, 23 corresponds to the time steps, and 125 represents the frequency bins. 4.2 Detection vs Prediction This study addresses two distinct tasks: seizure detection and seizure prediction (fore- casting). As illustrated in Fig. 2(A) , the EEG data are categorized into three distinct states for analysis: interictal, pre-ictal, and ictal. In detection, the objective is to dis- criminate between background activity (interictal) and seizure activity (ictal) at the time the seizure occurs. Here, the model is essentially classifying whether the signal currently belongs to a seizure event or not. In contrast, prediction focuses on anticipating seizures before onset. The task involves distinguishing background activity from two additional states within the pre-ictal State: Seizure Onset Period (SOP) and the Seizure Prediction Horizon (SPH). The SOP refers to the time window in which a seizure is expected to begin. The interval between an alarm and the start of the SOP is defined as the SPH. A prediction is considered correct if the seizure onset occurs after the SPH and within the SOP. Conversely, a false alarm arises when the system triggers a positive prediction but no seizure occurs during the SOP. Once triggered, an alarm remains active until the SOP concludes. Download figure Open in new tab Fig. 2. Results for the Initial Training and Prediction Stage on GPU. When analyzing seizure events, three phases are particularly critical: interictal, preictal, and ictal. These phases form the basis of two distinct tasks: Detection (discriminating between interictal and ictal states) and forecasting (discriminating between interictal and preictal states) (A). We applied STFT to the EEG data to extract two-class representations (0 and 1), and Grad-CAM visualizations revealed distinct activa- tion patterns associated with non and seizure dynamics (B). Across all datasets, our models achieved commendable AUROC scores under floating-point precision, with only slight reductions after quanti- zation, while still yielding up to 8 times reduction in memory (C). During quantization, lowering the learning rate relative to the initial training phase proved essential, as it allowed more effective con- solidation of previously learned neuronal activations before transitioning to edge learning. Notably, proper initialization with an appropriately tuned learning rate boosted per class neuron activations from 100 to over 600 times(D). 4.3 ANN Structure We designed a compact Convolutional Neural Network (CNN) tailored for EEG spec- trograms [ 19 ]. The model comprises three convolutional blocks: each includes a 2D convolution (filters: 16, 32, 64), batch normalization, ReLU activation, and max pooling. The first layer uses a kernel across all EEG channels ( N × 3) to capture spatial-frequency patterns where N is the number of channels. The output is flattened and passed through a dense layer (128 units) with ReLU and dropout (0.5), followed by a softmax classifier. An important distinction of Akida is that it only allows odd kernels to be computed. For the EPILEPSIAE Dataset, the channels are odd numbers, but for the CHB-MIT and FB, the channels are even. In order to fit the neuromorphic requirements, we select one channel (0) of the CHB-MIT and FB datasets where the magnitudes of the STFT were very different from other channels to ensure fitting on-device by processing the channels as odd kernels. 4.4 Quantization Aware Training The BrainChip Akida architecture was used to run our study for personalization at the edge. However, Akida supports only integer arithmetic for both inference and on-chip learning, ruling out any floating-point operations. To address these hardware constraints, we adopted Quantization-Aware Training (QAT) with an 8–4–4 scheme (8-bit input quantization, 4-bit weight quantization, and 4-bit activation quantization) for all network layers except the final one. In the output layer, we further reduce precision to a single bit, which aligns with Akida’s built-in training mechanism and allows efficient weight updates directly on chip. This tailored quantization approach maintains the model’s representational capacity while fully complying with the restrictions of the Akida Processor. 4.5 SNN Structure The Spiking Neural Network (SNN) is mapped from the quantized ANN, where the conventional activation functions (ReLU) are replaced with spiking event representations. The architecture works in a manner analogous to an event-based vision sensor, converting pixel activity into discrete events through Rank Order Coding (ROC) for input encoding. Each Akida core is capable of supporting tens of thousands of neurons. This exact number that can be instantiated depends on the model architecture and mapping strategy. 4.6 Edge Learning 4.6.1 Last Layer Training The Edge Learning mechanism in Akida enables efficient on-chip adaptation through a two-phase process. In the first phase, a pre-trained and quantized backbone is deployed to the hardware, with all weights frozen to preserve the extracted feature representations. In the second phase, only the final classification layer remains trainable directly on the device. By confining parameter updates to this layer, the computational and memory demands of the training process are substantially reduced. This approach allows rapid and energy-efficient personalization at the edge, eliminating the need for full network retraining and making the system particularly well-suited for resource-constrained environments. 4.6.2 Neuron Selection An important hyperparameter in the edge learning process was the selection of neurons in the final training layer. Rather than fixing the number of neurons to match the number of target classes, this layer acts as a clustering mechanism in which the neuron count itself becomes a tunable parameter. To optimize this, we employed a function that reserved approximately 10-20% of the training dataset as an estimation set for at least one class (i.e., the seizure class). Based on this, we evaluated a range of neuron counts: 100, 200, 300, 400, 500, 750, 1000, 1250, 1500, 2000, and 3000. 4.6.3 Few Shot Learning Few-shot learning is a machine learning paradigm where models are trained to recognize new classes or patterns using only a very small number of labeled examples per class. Unlike conventional supervised learning, which relies on large datasets and extensive retraining, few-shot methods typically leverage prior knowledge encoded in a pre-trained backbone. In the case of BrainChip Akida, few-shot learning is implemented directly at the edge through its last-layer training mechanism. In our framework, 1 shot is equivalent to approximately 12 seconds of seizure activity. By providing just one or a few such examples, the final trainable layer of Akida was able to cluster and adapt to the new seizure representations. This approach is particularly advantageous for seizure detection and prediction, where large amounts of labeled data are often difficult to obtain and seizure events may be rare or highly variable across patients. Few-shot learning allows the system to personalize rapidly to an individual patient’s seizure characteristics directly on-device, without the need for cloud connectivity or full retraining of the backbone network. 4.7 Performance Metrics The dataset used for model training and evaluation includes labeled data for each 12- second time window, as previously used in papers as a baseline reference [ 14 , 20 – 23 ]. A seizure is identified when the model accurately predicts an ictal event (class 1) based on the ground truth, while non-seizure events are labeled as class 0. Seizure datasets are often imbalanced, with a much higher number of non-seizure data points, as seizures are rare and brief. Over a day, seizure occurrences represent only a small fraction of the data within a 12-second window. Using traditional metrics like accuracy is misleading in this scenario, as it tends to favor the majority class and does not effectively measure the model’s ability to detect seizures, which is the primary goal. For this reason, we rely on the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), which evaluates both sensitivity and specificity in a threshold-independent manner. We also provided additional metrics such as Sensitivity and False Positive Rate (FPR). 4.8 Implementation Details For detection, we first trained the model on the TUH dataset for 200 epochs, with a learning rate of 0.005. Adam was selected as the optimizer. To distinguish between the seizure (1) and non-seizure (0) classes, we used Mean Squared Error (MSE) as the loss function. We then quantized the model as previously described with a learning rate much smaller than the initial learning training rate. We transformed the ANN model to an SNN model by replacing the RELU activations with the SNN activations and transferring the pre-trained weights. We trained the quantized SNN on the EPILEP- SIAE dataset in a few-shot learning manner. This is also an out-of-sample task as the dataset used does not belong to the same cohort as the Pre-Trained dataset. 30 independent models were created by only training the last layer of the SNN in the BrainChip Akida. We evaluated the impact of few-shot learning on it, from shots ranging between 1 and 15, to see the impact of long-term data. Each shot is composed of a seizure and a non-seizure class. Similarly, for the prediction task, we followed the same overall approach. The key difference lies in the initialization: for both the FB and CHB-MIT datasets, models were trained individually per patient rather than as a global model, reflecting the limited number of patients and seizures available. For edge learning, we adopted a leave-one-seizure-out strategy, ensuring robust evaluation while preserving personalization. An overview of the whole process can be found in Fig. 1(C-D) . 5 Results 5.1 Feature Extraction An essential preliminary step for enabling edge learning is to ensure that the backbone models capture physiologically meaningful neural dynamics. We first demonstrate the STFT representations of a dataset for the non-seizure and seizure classes. We then extracted saliency maps from the trained model using Grad-CAM for both classes, which reveal consistent patterns of model attention, with a pronounced emphasis on lower frequency regions. Results are shown in Fig. 2(B) . This observation aligns with prior findings that low-frequency activity and slow-wave components are key biomarkers associated with seizures in scalp EEG recordings [ 24 ]. 5.2 Pre-Post Quantization In the initial stage, we evaluated model performance on the TUH, FB, and CHB-MIT datasets using AUROC, recall, and precision. Model weights with validation scores exceeding or achieving 0.80 (0.80, 0.82, and 0.93, respectively) were selected for weight transfer. Following quantization, performance declined only marginally for the TUH and FB datasets (1% and 3%, respectively), whereas the CHB-MIT dataset exhibited a more pronounced reduction of 9%. Despite this, memory requirements were reduced by approximately 8 times across all datasets as an outcome of the adopted quantization scheme. Comparative AUROC results for both non-quantized and quantized models, alongside memory consumption (MB), are presented in Fig. 2(C) . 5.2.1 Quantization Learning Rate effects on Neuron Activations We set up the learning rate to be 10 times lower than the original learning rate that was used to compile in the training GPU phase. Through our observations, this has the biggest impact on the edge deployability. A bad learning rate (to be similar to the stage 1 of training) leads to fewer neurons been learned per class, as observed in Fig. 2(D) for non-seizure and seizure classes, respectively. We observed as the number we input, with a good initialization of LR there was neurons learned for both classes. As well, this behavior also gives insights that when performance improvements became marginal beyond a certain point, the number of neurons was capped. In this way, resources were allocated efficiently, reducing memory usage on the BrainChip Akida hardware without compromising performance. For instance, as illustrated in Fig. 2(D) , neuron activation becomes marginal beyond 1000 neurons, at which point the neuron for the last layer is capped to 1000. 5.3 Detection Out-Of-Sample Framework 5.3.1 EPILEPSIAE Dataset Using the transferred weights, we further trained the model on the EPILEPSIAE dataset directly at the edge. To provide a comprehensive assessment of performance, we introduce a Figure of Merit (FoM) that combines multiple evaluation metrics into a single score. The FoM is defined as: where (1-FPR) is choosen to penalizes false alarms. Sensitivity reflects a True Positive Rate. This formulation enables a balanced evaluation across competing objectives of accuracy, reliability, and robustness. Fig. 3 presents the FoM ( Eq. 2 ) as a function of the number of shots, with corresponding latency measurements plotted in parallel in all datasets. As the number of shots increases, all performance metrics exhibit consistent improvement. By the 15th shot, the AUROC, sensitivity, and FPR attained values of 0.85, 0.87, and 0.20, respectively. These personalized outcomes were subsequently compared against a generalization baseline, which reflects performance under inference-only conditions without further adaptation and with floating precision (float32). The personalized models demonstrated superior performance, yielding relative gains of 2% in AUROC, 4% in sensitivity, and a 6% reduction in FPR. These findings demonstrate that continuous, on-device learning with patient-specific data is both beneficial and critical for achieving effective, personalized seizure detection. The dataset holds particular significance as it comprises long-term continuous recordings in which seizure events constitute less than 1% of the total duration, with interictal segments comprising nearly the entirety of the dataset. The results for all patients against a baseline are shown in Table 2 (See Results of Few-Shot Learning across all Patients in Supplementary Fig. S2). View this table: View inline View popup Download powerpoint Table 2. Comparison of CNN and our work for seizure detection on the EPILEPSIAE dataset. Download figure Open in new tab Fig. 3. Figure of Merit (FoM) and latency vs number of shots for the three datasets. FoM is defined as AUROC × Sensitivity × (1 − FPR ). Higher shot counts yield consistent FoM gains, while latency rises marginally, indicating improved performance without meaningful computational cost. 5.4 Prediction Framework For the prediction task, we employed two datasets to establish comparisons across different baselines: the scalp EEG dataset (CHB-MIT) and the iEEG dataset (FB). The inclusion of the FB dataset also aligns with the vision of self-sustaining AI at the edge for future implantable devices. The experimental setup differed from the TUH-EPILEPSIAE framework, where models were first trained on TUH using a high- performance GPU and subsequently adapted at the edge on the EPILEPSIAE dataset. In contrast, for both CHB-MIT and FB, models were trained initially on a GPU, and then a leave-one-seizure-out approach was adopted for edge learning. This strategy reserved one seizure per patient for on-chip training while leaving a sufficient number of samples available for post-training evaluation, ensuring robust assessment of edge adaptation in both datasets. For preprocessing, we followed the same pipelines in two studies [ 19 , 25 ]. 5.4.1 CHB-MIT For the CHB-MIT dataset, we observed notable improvements and smaller degradation during the GPU training phase ( Fig. 2(C) ). Using the pre-trained individual weights, we subsequently trained patient-specific models at the edge. As the number of shots increased up to 15, performance stabilized with AUROC, sensitivity, and FPR reaching 0.87, 0.89, and 0.16, respectively (see Supplementary Fig S1). To benchmark our approach, we compared performance against two state-of-the-art methods. The first baseline was a widely cited CNN architecture for seizure prediction [ 19 ], trained in full-precision and evaluated only at inference. This model achieved AUROC and sensitivity scores of 0.84 and 0.80, respectively, whereas our method improved upon these results by 3% in AUROC and 9% in sensitivity. The second baseline was the recently proposed Future-Guided Learning (FGL) framework [ 25 ], which integrates detection models to support prediction training and has demonstrated competitive performance against established benchmarks. Notably, our approach outperformed FGL across all metrics, achieving relative improvements of 3% in AUROC, 1% in sensitivity, and 7% in FPR. Results are shown in Table 3 (See Results of Few-Shot Learning across all Patients in Supplementary Fig. S3(B)). View this table: View inline View popup Download powerpoint Table 3. Comparison of CNN, Future Guided Learning (FGL), and Our Work on CHB-MIT dataset. 5.4.2 FB For the FB dataset, results shows a similar trend to the previous model, although with smaller performance gains, but with more stability in metrics over shots (see Supplementary Fig. S1). This limitation can be attributed in part to the requirement of the BrainChip Akida architecture to employ odd kernel sizes, which led us to mask one channel in order to conform to the hardware constraints. As discussed earlier, the first convolutional kernel spans the available channels, and given that the FB dataset includes only six intracranial channels, this restriction likely contributed to the diminished improvement. Despite these constraints, our edge-trained model achieved AUROC, sensitivity, and FPR values of 0.84, 0.90, and 0.23, respectively, at 15 shots. Our sensitivity increased by nearly 10% compared to a state-of-the-art CNN for seizure prediction. However, our false positive rate remained higher (0.23 vs. 0.06), and AUROC was approximately 4–5% lower than the CNN baseline. It is important to note, though, that the baseline CNN was trained in full precision (float32) and utilized all available intracranial channels, whereas our approach was implemented under the quantization and one less channel constraints of the Akida neuromorphic hard- ware. Results are demonstrated in Table 4 (See Results of Few-Shot Learning across all Patients in Supplementary Fig. S3(A)). View this table: View inline View popup Download powerpoint Table 4. Comparison of CNN and our Work for Seizure Prediction on the FB Dataset. 5.5 Power, Memory and Latency Analysis The power consumption and inference efficiency of the Akida system were systematically evaluated using the MetaTF power profiling API during the inference stage. The floor power, representing the dynamic baseline power consumption of the device while idle, was measured at 900.5 mW. During active inference, the system achieved an average throughput of 70.41 frames per second (fps). Each frame corresponds to the Short-Time Fourier Transform (STFT) representation of a 12-second EEG window. The inference latency per frame was 14.2 ms, which equates to approximately 1.18 ms per second of raw EEG data. The energy consumption per inference was measured at 12.86 mJ per frame of a 12-second EEG window, which is equivalent to 1.07 mJ per 1 second of EEG data, reflecting the low-power profile of the neuromorphic hard- ware. These results demonstrate the suitability of the Akida platform for real-time, energy-efficient processing of EEG data in edge-based seizure detection and prediction applications. Results can be found in detail in Fig. 4 , where we compare running the CNN backbone in a conventional CPU architecture and our approach in the Akida to see the latency and energy per inference. We demonstrate that our neuromorphic approach enables low power consumption by two-three orders of magnitude. Download figure Open in new tab Fig. 4. Pareto plot of energy consumption versus Memory Usage and Latency for three datasets (EPILEPSIAE, CHB-MIT, and FB). Filled markers represent the results for metrics in CPU, while non-filled markers represent those at the Edge with Akida. 5.6 Robustness A critical validation in this study was to examine the robustness of our model in the spiking domain under conditions of reduced input information. We evaluated this on the EPILEPSIAE and CHB-MIT datasets, each comprising 19 EEG channels, by systematically blacking out 20%, 40%, and 60% of the channels. As illustrated in Fig. 5 , the CHB-MIT dataset demonstrated stable performance despite increasing channel loss and further benefited from a higher number of shots. In contrast, the EPILEP- SIAE dataset showed a more pronounced degradation as blackout levels increased. Nevertheless, resilience improved considerably with additional shots. We attribute this effect to the long-term nature of EPILEPSIAE recordings, which represent a more realistic and challenging benchmark compared with CHB-MIT. Overall, our model achieved strong performance across both datasets, highlighting its robustness under conditions of channel dropout. Download figure Open in new tab Fig. 5. Robustness test of our approach for seizure detection and prediction across datasets under progressive channel blackout with three repetitions. Performance is shown for the EPILEPSIAE dataset (blue, long-term clinical data) and the CHB-MIT dataset (orange) across different training shots (1, 5, 10, 15) as increasing proportions of EEG channels were set to zero, highlighting resilience to missing-channel conditions. 5.7 Complexity: Training and Inference Our analysis of computational complexity highlights the efficiency of the pro- posed Akida-based edge learning framework compared to conventional deep learning approaches. Traditional CNNs and RNNs exhibit high training costs due to their dependence on large parameter updates and, in the case of recurrent models, sequential operations that limit parallelization[ 20 ]. Similarly, SNNs trained with surrogate gradients require costly backpropagation through time, while even local learning rules still scale linearly with sequence length [ 26 ]. SVMs, though non-sequential at inference, demand prohibitively expensive training with cubic dependence on the number of samples [ 6 , 7 ]. By contrast, our proposed edge learning approach reduces training as a cluster with linear complexity in the feature dimension, while the inference scales with the number of neurons and the feature extractor. Crucially, this approach enables efficient, low-latency operation. These findings demonstrate that EDGE-AI on neuro- morphic hardware offers a favorable trade-off between performance and computational cost, positioning neuromorphic as a viable solution for real-time, resource-constrained biomedical applications. The complexity formulas can be found in Table 5 . We bench- mark our approach against representative state-of-the-art seizure detection/prediction models whose architectures and hyperparameters are documented in the literature and evaluated on at least three EEG datasets. In Fig. 6 , we demonstrate that our approach is computationally efficient for both training and inference as it occupies the lower-left region of the plot. View this table: View inline View popup Download powerpoint Table 5. Training complexity per model Download figure Open in new tab Fig. 6. Log–log scatter plot of training vs inference computational complexity for representative EEG seizure detection/prediction models. Each point reflects hyperparameters reported in prior work. Conventional method of training these networks are BP (CNN) and BPTT (LSTM, SnnConvLSTM, ConvLSTM). For inference complexity, the feature extractor is taken into account as a ConvSNN. 6 Limitations In this study, we do not consider different structures of different neural networks, which can lead to better results. Nonetheless, by using a simple architecture, we are able to see the increase in performance, compared to more complex models[ 12 , 19 ]. BrainChip Akida only allows certain models and layers to run at the Edge for training. It can be more beneficial to incorporate more advanced algorithms that can run on the edge [ 22 , 26 – 29 ]. Different encoding mechanism where not explored in this study. Studies have shown that different techniques in processing the EEG data into spikes is fundamental to capture temporal dynamics on seizure data [ 30 ] and also a reduction in power consumption and continual learning [ 31 ]. 7 Conclusion This work demonstrates the effectiveness of a novel framework for seizure detection and prediction directly on the edge, enabling learning with lower computational complexity, reduced energy consumption, and in an unsupervised manner while processing streaming data. Looking ahead, if the long-term vision is neuromorphic neuromodulation, this framework establishes a strong foundation toward that goal. Future work will focus on designing architectures capable of better capturing temporal and spatial dependencies, as shown in recent studies [ 23 ], thereby achieving higher performance without increasing memory or computational requirements. Overall, these results pave the way for the next generation of AI-driven electroceuticals. Declarations Author Contribution Statement LFHC developed the methodology, performed experiments, prepared the figures, and wrote the manuscript draft. LY, ZH, and IA contributed to the manuscript drafting, review of results, and revision of methodology design. AN and OK supervised experiments, manuscript preparation, reviewed methodology design and development, and reviewed results and manuscript drafts. OK developed the concept, framework conceptualization and served as the principal supervisor. All authors read and approved the final manuscript. Code Availability All source code will be made publicly available upon acceptance of this manuscript. Data Availability The TUH dataset can be freely obtained here . Access to the EPILEPSIAE dataset requires payment and is accessible here . The FB data used in this study was previously available openly via this link , but it seems it no longer accepts registration. The Children’s Hospital Boston dataset is publicly available here . Competing Interests The authors have no competing interests to declare relevant to this article’s content. Supplementary information This article contains Supplementary Information (3 Figures). Download figure Open in new tab Fig. 7. Average AUROC, FPR, and Sensitivity across all patients and datasets (EPILEPSIAE, CHB- MIT, Freiburg) Download figure Open in new tab Fig. 8. Performance metrics influenced as a function of shots for each patient in the EPILEPSIAE dataset. All results are averaged over 3 independent runs. Download figure Open in new tab Fig. 9. Performance metrics in the prediction framework as a function of shots for each patient in the FB (A) and CHB-MIT (B) datasets. All results are averaged over 3 independent runs. Acknowledgements Luis Fernando Herbozo Contreras would like to acknowledge the partial support of the Faculty of Engineering Research Scholarship provided by The University of Sydney. Zhaojing Huang would like to acknowledge the support of the Research Training Program (RTP) provided by the Australian Government. The research is supported by the Australian Research Council under Project DP230100019. References [1]. ↵ Fisher , R. S. et al. ILAE official report: a practical clinical definition of epilepsy . Epilepsia 55 , 475 – 482 ( 2014 ). OpenUrl CrossRef PubMed Web of Science [2]. ↵ Banerjee , P. N. , Filippi , D. & Hauser , W. A. The descriptive epidemiology of epilepsy–A review . Epilepsy Research 85 , 31 – 45 ( 2009 ). OpenUrl CrossRef PubMed Web of Science [3]. ↵ Jarosiewicz , B. & Morrell , M. 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Share Neuromorphic Neuromodulation: A Low-Power Edge-Training Framework for the Future of Personalized and Closed-Loop Neurostimulation Luis Fernando Herbozo Contreras , Leping Yu , Zhaojing Huang , Isabelle Aguilar , Armin Nikpour , Omid Kavehei medRxiv 2025.09.22.25336341; doi: https://doi.org/10.1101/2025.09.22.25336341 Share This Article: Copy Citation Tools Neuromorphic Neuromodulation: A Low-Power Edge-Training Framework for the Future of Personalized and Closed-Loop Neurostimulation Luis Fernando Herbozo Contreras , Leping Yu , Zhaojing Huang , Isabelle Aguilar , Armin Nikpour , Omid Kavehei medRxiv 2025.09.22.25336341; doi: https://doi.org/10.1101/2025.09.22.25336341 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neurology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4425) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15221) Forensic Medicine (30) Gastroenterology (1123) Genetic and Genomic Medicine (6588) Geriatric Medicine (667) Health Economics (997) Health Informatics (4524) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6588) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5440) Public and Global Health (9220) Radiology and Imaging (2195) Rehabilitation Medicine and Physical Therapy (1369) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (710) Sports Medicine (529) Surgery (710) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffd71d5be4509d6',t:'MTc3OTQ2OTkzNQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();