{"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/a-1d-cnn-for-high-accuracy-classification-and","title":"A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface","arxiv_id":null,"date":"2022-01-06","proceeding":"Journal of Neural Engineering 2022 1","authors":["F. Mattioli","C. Porcaro","G. Baldassarre"],"abstract":"Objective. Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor imagery (MI) seem to have a great potential for future applications. These approaches rely on the extraction of EEG distinctive patterns during imagined movements. Techniques able to extract patterns from raw signals represent an important target for BCI as they do not need labor-intensive data pre-processing. Approach. We propose a new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. In addition, we present a transfer learning method used to extract critical features from the EEG group dataset and then to customize the model to the single individual by training its late layers with only 12-min individual-related data. Main results. The model tested with the 'EEG Motor Movement/Imagery Dataset' outperforms the current state-of-the-art models by achieving a $\\mathbf{99.38\\%}$ accuracy at the group level. In addition, the transfer learning approach we present achieves an average accuracy of $\\mathbf{99.46\\%}$. Significance. The proposed methods could foster the development of future BCI applications relying on few-channel portable recording devices and individual-based training.","url_abs":"https://iopscience.iop.org/article/10.1088/1741-2552/ac4430","url_pdf":"https://iopscience.iop.org/article/10.1088/1741-2552/ac4430/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":"a-1d-cnn-for-high-accuracy-classification-and","repo_url":"https://github.com/Kubasinska/MI-EEG-1D-CNN","is_official":0,"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":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"motor-imagery","task_name":"Motor Imagery"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[{"slug":"eeg-motor-movement-imagery-dataset","name":"EEG Motor Movement/Imagery Dataset","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}