{"ID":2831966,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06725","arxiv_id":"2512.06725","title":"Decoding Motor Behavior Using Deep Learning and Reservoir Computing","abstract":"We present a novel approach to EEG decoding for non-invasive brain machine interfaces (BMIs), with a focus on motor-behavior classification. While conventional convolutional architectures such as EEGNet and DeepConvNet are effective in capturing local spatial patterns, they are markedly less suited for modeling long-range temporal dependencies and nonlinear dynamics. To address this limitation, we integrate an Echo State Network (ESN), a prominent paradigm in reservoir computing into the decoding pipeline. ESNs construct a high-dimensional, sparsely connected recurrent reservoir that excels at tracking temporal dynamics, thereby complementing the spatial representational power of CNNs. Evaluated on a skateboard-trick EEG dataset preprocessed via the PREP pipeline and implemented in MNE-Python, our ESNNet achieves 83.2% within-subject and 51.3% LOSO accuracies, surpassing widely used CNN-based baselines. Code is available at https://github.com/Yutiankunkun/Motion-Decoding-Using-Biosignals","short_abstract":"We present a novel approach to EEG decoding for non-invasive brain machine interfaces (BMIs), with a focus on motor-behavior classification. While conventional convolutional architectures such as EEGNet and DeepConvNet are effective in capturing local spatial patterns, they are markedly less suited for modeling long-ra...","url_abs":"https://arxiv.org/abs/2512.06725","url_pdf":"https://arxiv.org/pdf/2512.06725v1","authors":"[\"Tian Lan\"]","published":"2025-12-07T08:29:43Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"eess.SP\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false,"code_links":[{"ID":606182,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2831966,"paper_url":"https://arxiv.org/abs/2512.06725","paper_title":"Decoding Motor Behavior Using Deep Learning and Reservoir Computing","repo_url":"https://github.com/Yutiankunkun/Motion-Decoding-Using-Biosignals","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}