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.
The paper evaluates deep learning models for electrocardiogram‑based emotion recognition, focusing on generalization across datasets rather than dataset‑specific performance. It introduces two open‑source tools—ARRC for standardized benchmarking and ARDT for inter‑dataset training—to merge three public AER datasets (CUADS, ASCERTAIN, DREAMER) into a more variable benchmark. Using these tools, the authors compare three prominent deep learning architectures and two CNN baselines with hyperparameter tuning and 10‑fold cross‑validation, revealing trade‑offs between accuracy and model complexity and providing a reproducible benchmark for future research.
By Timothy C Sweeney-Fanelli, Ajan Ahmed, Masudul Imtiaz
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
The paper investigates whether brief, minimally processed EEG segments can differentiate individuals labeled as extraverted or introverted by the Myers–Briggs Type Indicator. Using a subject‑independent nested 5‑fold cross‑validation, the authors compared EEGNet, EEGProgress, and a new compact convolutional network called SSCNet. SSCNet achieved the highest mean accuracy (0.6481), AUC (0.7063), and F1‑score (0.6276), exhibited the lowest variability across folds, and proved computationally efficient with the shortest training epoch time and highest throughput.
By Filip Postepski, Grzegorz M. Wojcik
arXiv:2512. 13998v3 Announce Type: replace-cross Abstract: Music Emotion Recognition (MER) is constrained by limited expert annotations and the need to establish robustness across heterogeneous corpora.
By Qilin Li, C. L. Philip Chen, Tong Zhang