{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/imagined-speech-classification-using-eeg","title":"Imagined speech classification using EEG","arxiv_id":null,"date":"2014-12-01","proceeding":"ADVANCES IN BIOMEDICAL SCIENCE AND ENGINEERING 2014 12","authors":["Kamalakkannan Ravi","Rajkumar R.","Madan Raj. M.","Shenbaga Devi. S."],"abstract":"The objective of this work is to assess the possibility of using (Electroencephalogram) EEG for communication between different subjects. Here EEG signals are recorded from 13 subjects by inducing the subjects to imagine the English vowels ‘a’, ‘e’, ‘i’, ‘o’ and ‘u’ through visual stimulus. These recorded signals are then processed to remove artifacts and noise. Common features: Average power, Mean, Variance and Standard deviation are computed and classified using bipolar neural network. This method yields maximum classification accuracy of 44%. The result shows that EEG has some distinctive information for across subject classification.","url_abs":"https://www.researchgate.net/publication/309967859_Imagined_Speech_Classification_using_EEG","url_pdf":"https://www.researchgate.net/publication/309967859_Imagined_Speech_Classification_using_EEG","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"imagined-speech-classification-using-eeg","repo_url":"https://github.com/kamalravi/Imagined-Speech-Classification-using-EEG-","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg-signal-classification","task_name":"EEG Signal Classification"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"vowel-classification","task_name":"Vowel Classification"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"feature-selection","method_name":"Feature Selection"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/eeg-signal-classification-on","task":"EEG Signal Classification","dataset":".","model":"Bipolar Neural Network","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy (% )":"44"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}