Prior-Guided Frequency-Calibrated Virtual EEG Channel Inference from Four Frontal Electrodes for Wearable EEG Augmentation
School of Biomedical Science and Engineering, South China University of Technology（生物医学工程学院，华南理工大学）；School of Automation Science and Engineering, South China University of Technology（自动化科学与工程学院，华南理工大学）
Low-channel wearable electroencephalography (EEG) is attractive for long-term monitoring, but four frontal electrodes provide only a sparse and spatially biased sampling of the scalp potential field. Virtual-channel methods should therefore be framed not as recovery of independent unmeasured brain activity, but as prior-guided conditional inference of posterior predictive scalp-potential representations at target electrode locations. We present FAVC-Net, a compact frequency-calibrated virtual-channel inference network that estimates 13 target channels from Fp1, Fp2, F7, and F8. The model combines shared multi-scale source encoding, source-state embeddings, target-conditioned signed source-block mixing, GATv2-based attention refinement, attention-consistent skip fusion, and weak Welch power spectral density calibration. The generator is trained as a task-agnostic reconstruction module, without class-label, classification, or CSP-like discriminative constraints, so that the virtual montage remains tied to conditional scalp-potential estimation rather than to a specific downstream decision. On the PRED+CT dataset, FAVC-Net achieved the best joint waveform-spectral operating point among neural and interpolation baselines. Its time-domain gains were modest, whereas log-spectral distance and PSD KL divergence were reduced by 30.50% and 38.94% relative to the strongest non-FAVC comparator. Under wearable-like source perturbations, the model preserved spectral fidelity and channel-frequency texture, with anti-collapse benefits most evident under EMG-like bursts and mixed stress. These results support virtual EEG channels as montage-compatible, frequency-calibrated posterior predictive representations derived from sparse frontal measurements, not as independent substitutes for physically recorded electrodes.
Multi-level Binarized LSTM in EEG Classification for Wearable Devices
School of Electrical and Computer Engineering, University of Tehran（德黑兰大学电气与计算机工程学院）；Division of Intelligent Future Technologies, Malardalen University（马拉达伦大学智能未来技术系）
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices
ETH Zürich（苏黎世联邦理工学院）；University of Bologna（博洛尼亚大学）；University of Modena and Reggio Emilia（摩德纳-雷焦艾米利亚大学）；Lausanne University Hospital (CHUV)（洛桑大学医院（CHUV））
Recording Brain Activity While Listening to Music Using Wearable EEG Devices Combined with Bidirectional Long Short-Term Memory Networks
School of Music, Jiangxi Normal University（江西师范大学音乐学院）；School of Electronic Information, HuZhou College（湖州学院电子信息学院）；San Francisco State University（旧金山州立大学）
Assessing Region-Level EEG Contributions to Cognitive Workload Prediction
School of InfoComm Technology, Ngee Ann Polytechnic（信息与通信技术学院，南洋理工学院新加坡分校）；Engineering Product Development Pillar, Singapore University of Technology and Design（工程产品开发支柱，新加坡科技设计大学）；NUS High School of Math and Science（国立大学科学高中）
Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection
Minnan Normal University（闽南师范大学）；Nanjing University of Chinese Medicine（南京中医药大学）；Nanyang Technological University（南洋理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Zhengzhou University of Light Industry（郑州轻工业大学）；Tsinghua University（清华大学）；Eastern Institute of Technology（宁波工程学院）；Ningbo Institute of Digital Twin（宁波数字孪生研究院）；Xiamen University（厦门大学）
EEG-BBNet: a Hybrid Framework for Brain Biometric using Graph Connectivity
Vidyasirimedhi Institute of Science & Technology（维迪亚西里梅迪科学技术学院）；King Mongkut’s Institute of Technology Ladkrabang（国王科技大学（拉卡邦））；Kamnoetvidya Science Academy School（卡姆努特维迪亚科学学院）；Tokyo Institute of Technology（东京工业大学）
Trial-Level Time-frequency EEG Desynchronization as a Neural Marker of Pain
Luxembourg Centre for Systems Biomedicine, University of Luxembourg（卢森堡系统生物医学研究中心，卢森堡大学）；Department of Behavioural and Cognitive Sciences, University of Luxembourg（行为与认知科学系，卢森堡大学）
Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis
Universiti Kebangsaan Malaysia（马来西亚国民大学）；Qatar University（卡塔尔大学）；Hamad General Hospital（哈马德综合医院）；Alkhor Hospital（阿尔霍尔医院）；BIRDEM General Hospital（BIRDEM综合医院）；North South University（南北大学）
Comparative Analysis of State-of-the-Art Foundation Models for Sleep Analysis Under Channel Reduction
University of Turku（图尔库大学）；Turku University Hospital（图尔库大学医院）；University College Cork（科克大学）；University at Albany, SUNY（纽约州立大学奥尔巴尼分校）；Fudan University（复旦大学）；University of Sydney（悉尼大学）
Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms
Institute of Biomedical Engineering, Ulm University（生物医学工程研究所，乌尔姆大学）；Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zurich（移动健康系统实验室，健康科学与技术系，苏黎世联邦理工学院）
Robust learning from corrupted EEG with dynamic spatial filtering
Université Paris-Saclay（巴黎萨克雷大学）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）；InteraXon Inc.（InteraXon公司）；Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）
PHINN-EEG: Topological Time-Series Analysis of Dream-State EEG -- Dynamic Betti Curves for Dream Content Classification and Topology-Conditioned Neural Signal Synthesis
Subject-Specific Analysis of Self-Initiated Attention Shifts from EEG with Controlled Internal and External Attention Conditions
Advanced Institute of Convergence Knowledge Informatics, Tohoku University, Japan（东京东京大学融合知识信息研究院）；Graduate School of Information Sciences, Tohoku University, Japan（东京东京大学信息科学研究生院）；Center for Data-Driven Science and Artificial Intelligence, Tohoku University, Japan（东京东京大学数据驱动科学与人工智能研究中心）；Research Institute of Electrical Communication, Tohoku University, Japan（东京东京大学电气通信研究所）
