{"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/recurrent-neural-networks-for-p300-based-bci","title":"Recurrent Neural Networks for P300-based BCI","arxiv_id":"1901.10798","date":"2019-01-30","proceeding":null,"authors":["Ori Tal","Doron Friedman"],"abstract":"P300-based spellers are one of the main methods for EEG-based brain-computer\ninterface, and the detection of the P300 target event with high accuracy is an\nimportant prerequisite. The rapid serial visual presentation (RSVP) protocol is\nof high interest because it can be used by patients who have lost control over\ntheir eyes. In this study we wish to explore the suitability of recurrent\nneural networks (RNNs) as a machine learning method for identifying the P300\nsignal in RSVP data. We systematically compare RNN with alternative methods\nsuch as linear discriminant analysis (LDA) and convolutional neural network\n(CNN). Our results indicate that LDA performs as well as the neural network\nmodels or better on single subject data, but a network combining CNN and RNN\nhas advantages when transferring learning among subejcts, and is significantly\nmore resilient to temporal noise than other methods.","url_abs":"http://arxiv.org/abs/1901.10798v1","url_pdf":"http://arxiv.org/pdf/1901.10798v1.pdf","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":"recurrent-neural-networks-for-p300-based-bci","repo_url":"https://github.com/Ori226/p300_lstm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"brain-computer-interface","task_name":"Brain Computer Interface"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"}],"methods":[{"method_slug":"lda","method_name":"LDA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}