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
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:2608. 13285v1 Announce Type: new Abstract: 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.
By Athanasios Karagounis
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
NeuroStrata is a deep learning framework that analyzes mental stress by modeling the temporal evolution of frequency‑specific directed connectivity in EEG signals using Time‑Varying Partial Directed Coherence (TV‑PDC). It transforms TV‑PDC connectivity maps into deep embeddings with pretrained CNNs and Vision Transformers, then classifies them with lightweight machine learning models. Experiments on the SAM 40 dataset show that beta‑band connectivity yields the highest accuracy (97.3 %) with a ViT backbone, while alpha‑band connectivity remains consistently stable, and that stress‑related connectivity signatures consolidate in mid‑to‑late temporal windows.
By Sayantan Acharya, Hamzeh Asgharnezhad, Abbas Khosravi, Douglas Creighton, Roohallah Alizadehsani, U Rajendra Acharya
KnowEEG is a feature‑based EEG classification pipeline that extracts 783 per‑electrode statistical features and cross‑brain connectivity metrics, using a balanced sampling strategy to integrate high‑dimensional per‑electrode data with connectivity information. It achieves performance that matches or surpasses state‑of‑the‑art deep learning models on five diverse tasks—including emotion detection, mental workload, eyes open/closed, abnormal EEG, and event detection—without requiring GPU resources. The approach also offers inherent explainability via feature importance rankings, identifying occipital biomarkers in the eyes open/closed task that align with established neuroscience literature.
By Amarpal Sahota, Navid Mohammadi Foumani, Raul Santos-Rodriguez, Zahraa S. Abdallah
arXiv:2606. 10718v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a widely adopted technique for monitoring brain activity, offering valuable insights into neurological states due to its high temporal resolution and cost-effectiveness.
By Xinglong Cui, Dian Gu
arXiv:2608. 16541v1 Announce Type: cross Abstract: Objective assessment of learning remains a fundamental challenge in education.
By Isuru Nanayakkara, Thilina Halloluwa
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)
The paper presents a transformer-based framework for detecting schizophrenia from EEG signals. It converts preprocessed EEG recordings into spectrograms via Short-Time Fourier Transform and classifies them using both traditional machine learning algorithms and deep learning models, including CNN-Transformer hybrids. Subject-level data partitioning ensures reliable evaluation, yielding an AUC-ROC of 88.41% for the CNN-Transformer model and 92.88% for the CNN + Squeeze and Excitation + Transformer model on an independent test set.
By Abtin Shafiei, Mohsen Hooshmand, Majid Ramezani
arXiv:2507. 12645v1 Announce Type: cross Abstract: The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient assessment, especially in synchronous monitoring.
By Mohammed Guhdar, Ramadhan J. Mstafa, Abdulhakeem O. Mohammed
arXiv:2610.33832v1 Announce Type: cross
Abstract: Sustained attention is critical for academic performance, yet existing auditory support tools are static and not grounded in real-time neural evidenc...
By Isabella Guan