An Analysis of EEG Signal Classification for Digit Dataset

An Analysis of EEG Signal Classification for Digit Dataset | 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 An Analysis of EEG Signal Classification for Digit Dataset Asif Iqbal, Arpit Bhardwaj, Ashok Kumar Suhag, Manoj Diwakar, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4325000/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 Artificial Intelligence (AI) and Machine Learning has brought significant atten- tion to the human brain, making it a prominent research area in engineering and technology and other non-medical sciences. Electroencephalogram (EEG) are one of many biological signals that are produced by human brain. EEG signals contain electrical properties and has frequency ranging between 0-100Hz. Fea- tures are the various attributes of the recorded signals which are associated with the state of the human brain. The data comprises values that correspond to the frequencies of EEG signals, specifically delta, theta, alpha, beta, and gamma. Additionally, it includes information about the level of attention, level of medita- tion, and the frequency of eye blinking of the subject. This research has given a notion of how a imagined digit is classified from an EEG signal by using machine learning algorithms. We have done the analysis by using models like k-Nearest Neighbor (kNN), Convolutional Neural Network (CNN) and Genetic Program- ming (GP). An original EEG data set is created for digit from 0–9 by using a non invasive single electrode (channel) EEG device. The obtained accuracy for kNN is 66.8%, for Convolutional Neural Network it is 73.1% and that for GP it is calculated equal to 82%. If the calculated accuracy of lower channel device is improved and further achieved more then one day they may replace higher chan- nel bulky devices. As single channel or lower channel EEG device are portable and easy to use therefore implementation of this work in future may meet a variety of applications in biomedical engineering, smart health care, personal assistance and automation. BCI System Single Channel Non-Invasive EEG Devices Feature Extraction Classification kNN CNN Genetic Programing Full Text 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4325000","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":296788258,"identity":"f737444d-a622-4440-a3e6-d5eff33c8efb","order_by":0,"name":"Asif Iqbal","email":"","orcid":"","institution":"SOET, BML Munjal University","correspondingAuthor":false,"prefix":"","firstName":"Asif","middleName":"","lastName":"Iqbal","suffix":""},{"id":296788267,"identity":"cca57a0d-4f89-4eb5-acc3-a235bee2efc1","order_by":1,"name":"Arpit Bhardwaj","email":"","orcid":"","institution":"SCSET, Bennett University","correspondingAuthor":false,"prefix":"","firstName":"Arpit","middleName":"","lastName":"Bhardwaj","suffix":""},{"id":296788268,"identity":"1b526c40-070f-49aa-a8ba-22f05c895aa9","order_by":2,"name":"Ashok Kumar Suhag","email":"","orcid":"","institution":"SOET, BML Munjal University","correspondingAuthor":false,"prefix":"","firstName":"Ashok","middleName":"Kumar","lastName":"Suhag","suffix":""},{"id":296788270,"identity":"4e50d5dc-8f06-47e9-9f30-730cb1f438be","order_by":3,"name":"Manoj Diwakar","email":"","orcid":"","institution":"DCSE, Graphic Era Deemed to be Uni","correspondingAuthor":false,"prefix":"","firstName":"Manoj","middleName":"","lastName":"Diwakar","suffix":""},{"id":296788271,"identity":"50d5ddb0-e904-4335-b67f-7bd2ed9b3587","order_by":4,"name":"Anchit Bijalwan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie3PsWqDQBjA8U8KN9m6XhbvFS4U2qGBvsqFgC4aAoVMHYSAU2m3IvQpfAPLB2aRzIIZzOKcqbik9M5Q7ODVtdD74+Dp/bj7AEymvxkBASAIZFYmV+73xxEiOgKKXPdEz+QfoQ6CjsyjMeK8bZr60MLyysIa7ce9//JacDiuERwWDRK6z2+5vNgDgZyjnTdhUgXcSnYIk1hzrTIgVJJ5DBnHgGAYSXJxGSPwfFiwnmyPGHyizxQ5SXKvIbwnBccwRsEVsdQpmvGnpXdDhUflLMUKT884TStv9f60822ai0Hilotm0s5mS5Zs00PygcytFmndru9cZ5Np5u+i8vmxQb3av+0/50Tje0wmk+l/9gU+S2QPqeM1BQAAAABJRU5ErkJggg==","orcid":"","institution":"Arba Minch University","correspondingAuthor":true,"prefix":"","firstName":"Anchit","middleName":"","lastName":"Bijalwan","suffix":""},{"id":296788272,"identity":"df8db90c-4a4a-4ef5-8083-2744b86759bb","order_by":5,"name":"Aditi Bhardwaj","email":"","orcid":"","institution":"ASET, Amity University","correspondingAuthor":false,"prefix":"","firstName":"Aditi","middleName":"","lastName":"Bhardwaj","suffix":""},{"id":296788273,"identity":"9f2c44a5-d9bc-4afc-aeb3-314a30859c64","order_by":6,"name":"Madhushi Verma","email":"","orcid":"","institution":"SCSET, Bennett University","correspondingAuthor":false,"prefix":"","firstName":"Madhushi","middleName":"","lastName":"Verma","suffix":""}],"badges":[],"createdAt":"2024-04-25 15:11:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4325000/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4325000/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58095017,"identity":"1c7ea8a6-696f-4579-97e0-951291a09b8d","added_by":"auto","created_at":"2024-06-11 05:23:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":627581,"visible":true,"origin":"","legend":"","description":"","filename":"AnAnalysisofEEGSignalClassificationforDigitDataset.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4325000/v1_covered_c0663591-64ee-4d86-9060-5456e92a65e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Analysis of EEG Signal Classification for Digit Dataset","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"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":"BCI System, Single Channel Non-Invasive EEG Devices, Feature Extraction, Classification, kNN, CNN, Genetic Programing","lastPublishedDoi":"10.21203/rs.3.rs-4325000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4325000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial Intelligence (AI) and Machine Learning has brought significant atten- tion to the human brain, making it a prominent research area in engineering and technology and other non-medical sciences. Electroencephalogram (EEG) are one of many biological signals that are produced by human brain. EEG signals contain electrical properties and has frequency ranging between 0-100Hz. Fea- tures are the various attributes of the recorded signals which are associated with the state of the human brain. The data comprises values that correspond to the frequencies of EEG signals, specifically delta, theta, alpha, beta, and gamma. Additionally, it includes information about the level of attention, level of medita- tion, and the frequency of eye blinking of the subject. This research has given a notion of how a imagined digit is classified from an EEG signal by using machine learning algorithms. We have done the analysis by using models like k-Nearest Neighbor (kNN), Convolutional Neural Network (CNN) and Genetic Program- ming (GP). An original EEG data set is created for digit from 0–9 by using a non invasive single electrode (channel) EEG device. The obtained accuracy for kNN is 66.8%, for Convolutional Neural Network it is 73.1% and that for GP it is calculated equal to 82%. If the calculated accuracy of lower channel device is improved and further achieved more then one day they may replace higher chan- nel bulky devices. As single channel or lower channel EEG device are portable and easy to use therefore implementation of this work in future may meet a variety of applications in biomedical engineering, smart health care, personal assistance and automation.\u003c/p\u003e","manuscriptTitle":"An Analysis of EEG Signal Classification for Digit Dataset","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 02:40:33","doi":"10.21203/rs.3.rs-4325000/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":"6965d3f7-f5b2-4435-91c7-60c7239b97bb","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-11T04:59:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-02 02:40:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4325000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4325000","identity":"rs-4325000","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}