EEG Decoding

EEG: Electroencephalography

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21 papers in the last four weeks, up 200% on the four weeks before. 0.2% of all new papers.

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Making Brain-Computer Interfaces More Secure
May 22, 2026Md Fahimul Kabir Chowdhury, Gahangir HossainBrain-Computer Interfaces Adversarial Attacks
Department of Computer Science and Engineering, University of North Texas, USA · Department of Data Science, University of North Texas, USA
L-FAME: Longitudinal Focused Attention Meditation EEG Dataset and Benchmark
May 21, 2026Angqi Li, Ab Basit Rafi Syed, Hamzeh Alzweri +3Electroencephalography EEG Decoding
Department of CMSE Michigan State University East Lansing, MI 48824 · Department of CSE Michigan State University East Lansing, MI 48824 · Department of Psychology Michigan State University East Lansing, MI 48824 +2
Atoms of Thought: Universal EEG Representation Learning with Microstates
May 19, 2026Xinyang Tian, Ruitao Liu, Ziyi Ye +3Electroencephalography Representation Learning
Classification of IED-free EEG Responses for Assisted Epilepsy Diagnosis
May 19, 2026Giacomo Zanardini, Ryan Moesman, Paul van der Kleij +2Electroencephalography EEG Decoding
Signal Processing Systems, Delft University of Technology, Delft, The Netherlands · Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG
May 18, 2026Yang Shao, Peiliang Gong, Qun Dai +1Masked Autoencoders Self-Supervised Pre-Training
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics Nanjing, Jiangsu, China
Subject-Specific Analysis of Self-Initiated Attention Shifts from EEG with Controlled Internal and External Attention Conditions
May 18, 2026Yuwen Zeng, Dengzhe Hou, Zhang Zhang +4Electroencephalography EEG Decoding
Advanced Institute of Convergence Knowledge Informatics, Tohoku University, Japan · Graduate School of Information Sciences, Tohoku University, Japan · Center for Data-Driven Science and Artificial Intelligence, Tohoku University, Japan +1
Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs
May 18, 2026Jun-Yu Pan, Yansen Wang, Enze Zhang +3EEG Decoding EEG-Based Visual Decoding
Shanghai Jiao Tong University Shanghai, China · Microsoft Research Asia Shanghai, China
Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models
May 17, 2026Urban Širca, Maryam Alimardani, Stefanos Zafeiriou +1EEG Decoding Neural Network Interpretability
Vrije Universiteit Amsterdam · Imperial College London
RAG-based EEG-to-Text Translation Using Deep Learning and LLMs
May 17, 2026Enrico Collautti, Xiaopeng Mao, Luca Tonin +2Retrieval-Augmented Generation Brain-Computer Interfaces
IAS-LAB, Department of Information Engineering, University of Padova, 35122 Padova (PD), Italy · Department of Health Technology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark · Padova Neuroscience Center, 35131 Padova (PD), Italy.
Cross-Subject Intracranial EEG Reconstruction from Scalp Recordings Using Multi-Scale Cross-Attention Transformers
May 17, 2026Tien-Dat Pham, Xuan-The TranCross-Subject EEG Decoding Electroencephalography
HAI-Smartlink Research Lab, Anchi STE Company, Haiphong, Vietnam · School of Mechanical Engineering, Vietnam Maritime University, Haiphong, Vietnam
BCI-Based Assessment of Ocular Response Time Using Dynamic Time Warping Leveraging an RDWT-Driven Deep Neural Framework
May 14, 2026Shantanu Sarkar, Sai Shashank Gandavarapu, Jeff Feng +3EEG Decoding
Dept. of ECE, IUCRC BRAIN, Cullen College of Engineering University of Houston, Houston, USA · Dept. of Data Science, Cullen College of Engineering University of Houston, Houston, USA · Dept. of Industrial Design, IUCRC BRAIN, Gerald D.Hines College of Arch. & Design University of Houston, Houston, USA +1
NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces
May 14, 2026Konstantinos Kontras, Trui Osselaer, Stylianos G. Mouslech +12Electroencephalography Brain-Computer Interfaces
KU Leuven · MIT
KAST-BAR: Knowledge-Anchored Semantically-Dynamic Topology Brain Autoregressive Modeling for Universal Neural Interpretation
May 13, 2026Haoning Wang, Wenchao Yang, Shuai Shen +1EEG Decoding Multimodal Pretraining
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. · School of Biological Science and Medical Engineering, Beihang University, Beijing, China. · State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China. +1
From Clever Hans to Scientific Discovery: Interpreting EEG Foundational Transformers with LRP
May 12, 2026Justus Meyer zu Bexten, Nico Scherf, Bogdan Franczyk +1Transformer Interpretability EEG Decoding
Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Leipzig University · Neural Data Science and Statistical Computing, Max Planck Institute for Human Cognitive and Brain Sciences · Faculty of Economics, Leipzig University +1
DANCE: Detect and Classify Events in EEG
May 11, 2026Jarod Lévy, Hubert Banville, Jérémy Rapin +3Electroencephalography Brain-Computer Interfaces
1Meta AI · 2Inria, Université Paris-Saclay, Palaiseau, France
Explainability of Recurrent Neural Networks for Enhancing P300-based Brain-Computer Interfaces
May 11, 2026Christian Oliva, Vinicio Changoluisa, Francisco B Rodríguez +1Brain-Computer Interfaces EEG Decoding
Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, c/ Francisco Tomas y Valiente, 11, Madrid, 28049, Madrid, Spain · Grupo de Investigación en Electrónica y Telemática, Universidad Politécnica Salesiana, Campus el Girón, Ave 12 de Octubre 24-22, Quito, 170143, Quito, Ecuador
CFSPMNet: Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients
May 11, 2026Xiangkai Wang, Yun Zhao, Dongyi He +5Cross-Subject EEG Decoding Brain-Computer Interfaces
School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China; Chongqing Key Laboratory of Embodied Intelligence Perception and Autonomous Learning for Humanoid Robots; Key Laboratory of Advanced Equipment Intelligence of the Chongqing Education Commission, Chongqing 401135, China · School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing 401131, China · Department of Language Science and Technology, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China +1
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
May 8, 2026Hubert Banville, Stéphane d'Ascoli, Simon Dahan +12Benchmark Design EEG Decoding
Lucy · Brain & AI team, Meta FAIR · MIND, Inria +2
Same Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliability
May 8, 2026Dengzhe Hou, Zihao Wu, Lingyu Jiang +3Uncertainty Quantification EEG Decoding
Tohoku University · University of Georgia · Texas A&M University +1
Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets
May 5, 2026Laurits Dixen, Stefan Heinrich, Paolo BurelliEfficient Neural Network Inference MI Classification
IT University of Copenhagen Denmark
Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods
Apr 29, 2026Taida Li, Yujun Yan, Fei Dou +2Cross-Subject EEG Decoding EEG Decoding
Department of Computer Science, University of North Carolina at Charlotte, Charlotte, United States · Department of Computer Science, Dartmouth College, Hanover, United States · School of Computing, University of Georgia, Athens, United States +1
Task-guided Spatiotemporal Network with Diffusion Augmentation for EEG-based Dementia Diagnosis and MMSE Prediction
Apr 27, 2026Xiaoyu Zheng, Xu Tian, Bin Jiao +4Alzheimer's Disease Electroencephalography
Channel Adaptation for EEG Foundation Models: A Systematic Benchmark Across Architectures, Tasks, and Training Regimes
Apr 25, 2026Kuntal Kokate, Bruno Aristimunha, Dung Truong +1Fine-Tuning EEG Decoding
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA · Yneuro, Paris, France · Centre de Recherche Cerveau et Cognition, CNRS, Université Paul Sabatier, Toulouse, France
NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals
Apr 24, 2026Badri N. Patro, Vijay S. AgneeswaranElectroencephalography Neural Decoding
Microsoft
Leakage-Audited Benchmarking Reveals Limited Evidence for Cross-Subject Auditory-Evoked EEG Vowel Perception Decoding
Apr 22, 2026Xiaoyang Li, Zeyan TaoCross-Subject EEG Decoding Electroencephalography
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110016, China
Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving
Apr 21, 2026Ghadah Alosaimi, Hanadi Alhamdan, Wenke E +3Electroencephalography EEG Decoding
Department of Computer Science, Imam Mohammad Ibn Saud Islamic University, Saudi Arabia · Department of {2Computer Science, 3Engineering}, Durham University, UK · Department of Computer Science, Princess Nourah bint Abdulrahman University, Saudi Arabia
Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
Apr 21, 2026Peiliang Gong, Han Zhang, Zhen Jiang +5Cross-Subject EEG Decoding Source-Free Domain Adaptation
College of Computing and Data Science, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore · College of Artifical Intelligence and Automation, Hohai University, Changzhou 213022, China · College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China +2
One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment
Apr 21, 2026Stefanos Gkikas, Christian Arzate Cruz, Thomas Kassiotis +3Efficient Neural Network Inference Transformer
Honda Research Institute Japan · Wako City, Japan · Department of Electronic Engineering +6
EEG-Based Emergency Braking Intensity Prediction Using Blind Source Separation
Apr 20, 2026Zikun Zhou, Wenshuo Wang, Wenzhuo Liu +5Independent Component Analysis EEG Decoding
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China
DSAINet: An Efficient Dual-Scale Attentive Interaction Network for General EEG Decoding
Apr 20, 2026Zhiyuan Ma, Zeyuan Li, Zihao Qiu +6Cross-Subject EEG Decoding EEG Decoding
School of Biomedical Engineering, Tsinghua Laboratory of Brain and Intelligence, and IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China · School of Basic Medical Sciences and Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China · Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China +1

May 22, 2026, cs.LGJ/K move · Enter open · S save

Making Brain-Computer Interfaces More Secure

Md Fahimul Kabir Chowdhury, Gahangir Hossain

Department of Computer Science and Engineering, University of North Texas, USA · Department of Data Science, University of North Texas, USA

Brain-Computer Interfaces Adversarial Attacks EEG Decoding Adversarial Robustness Convolutional Neural Networks

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The development of brain-computer interfaces (BCIs) based on electroencephalograms (EEGs) has advanced significantly mainly to machine learning. Although the majority of earlier research has been on increasing classification accuracy, relatively little focus has been placed on security and robustness. According to recent research, EEG-based BCIs are susceptible to adversarial attacks, which can cause misdiagnosis due to minute, well-crafted disturbances. Evaluating model robustness against such perturbations is therefore critical for ensuring reliable deployment. In this study, we propose a lightweight custom Convolutional Neural Network (CNN) architecture to investigate adversarial robustness in EEG-based BCIs. The suggested method is assessed using two EEG datasets and contrasted with three novel CNN models tailored to EEG, namely EEGNet, DeepConvNet, and SleepEEGNet, under gradient-based adversarial attack scenarios. According to experimental findings, the suggested model continuously performs better in classification under adversarial perturbations compared to baseline models, indicating improved robustness. These findings highlight the potential of lightweight architectures for enhancing the reliability of EEG-based BCI systems under adversarial conditions.

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