Motor imagery (MI) brain--computer interfaces (BCIs) have emerged as a promising approach for establishing flexible communication pathways between the human brain and external devices , particularly for individuals affected by stroke or neurodegenerative disorders. Reliable decoding of motor-imagery electroencephalography (MI-EEG) remains challenging because EEG recordings contain substantial noise and exhibit complex, weakly informative relationships with the underlying brain activity.
arXiv:2510. 15371v2 Announce Type: replace-cross Abstract: Classification of electroencephalogram (EEG) signals obtained during motor imagery (MI) has substantial application potential, including communication assistance and rehabilitation support for patients with motor impairments.
By Shuntaro Suzuki, Shunya Nagashima, Komei Sugiura
The paper introduces a deep learning framework that uses a CNN‑GRU architecture to classify EEG recordings into resting or cognitive states. Time‑frequency analysis extracts salient signal features, which are then evaluated with both deep learning and traditional machine learning classifiers. The proposed method achieves accuracies of 83.177% for resting vs. mathematical tasks, 76.107% for resting vs. memory tasks, and 83.432% for resting vs. music tasks, outperforming comparative approaches.
By K. A. Januka S. Fernando, Harshit Srivastava
The paper introduces a deep learning framework that uses a CNN‑GRU architecture to classify EEG recordings into resting and various cognitive states. Time‑frequency analysis is applied to extract salient signal features, which are then evaluated with both deep learning and traditional machine learning classifiers, including a proposed 2D‑Net. The method achieves accuracies of 83.177% for resting vs. mathematical tasks, 76.107% for resting vs. memory tasks, and 83.432% for resting vs. music tasks, outperforming comparative approaches.
arXiv:2509. 17920v2 Announce Type: replace Abstract: Current deep learning models for electroencephalography (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability.
By Jamiyan Sukhbaatar, Satoshi Imamura, Ibuki Inoue, Shoya Murakami, Kazi Mahmudul Hassan, Seungwoo Han, Ingon Chanpornpakdi, Toshihisa Tanaka
arXiv:2606. 02166v1 Announce Type: new Abstract: Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life.
By Vigneshwar Hariharan (National University of Singapore), Chithra Reghuvaran (University College Dublin), Arlene John (University of Twente), Nhat Pham (Cardiff University), Omer Rana (Cardiff University), Deepu John (University College Dublin), Ganesh Neelakanta Iyer (National University of Singapore)