MI Classification

MI: Motor Imagery

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Separating personal from population gains when calibrating EEG foundation models for new users
Sep 28, 2026Xilin Tao, Kani ChenCross-Subject EEG Decoding MI Classification
Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China · Department of Industrial Engineering and Decision Analytics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
ThinkNet: Compact Architecture Selection and Validation-Gated Ensembles for Subject-Independent MI-EEG Decoding
Sep 27, 2026Abdul Basit, Saim Rehman, Muhammad ShafiqueCross-Subject EEG Decoding MI Classification
eBRAIN Lab, Division of Engineering New York University (NYU) Abu Dhabi, Abu Dhabi, UAE
EEG-Fusion: Failure-Informed Source-Free Expert Routing for Robust Motor Imagery EEG Decoding
Sep 27, 2026Abdul Basit, Saim Rehman, Muhammad ShafiqueSource-Free Domain Adaptation MI Classification
eBRAIN Lab, Division of Engineering New York University (NYU) Abu Dhabi, Abu Dhabi, UAE
Matched-Input Estimates Differ in Sign Across Architectures: Auditing EEG Foundation Models on Motor Imagery
Sep 20, 2026Kevin Zhou, Sparsh RoyMI Classification EEG Decoding
Hopewell Valley Central High School
FRIST: FMRI Representation Informed Shared-space Training Improves EEG-only Individual-Finger BCI Decoding
Sep 14, 2026Jintao Zhang, Yidan Ding, Joshua Kosnoff +3MI Classification Cross-Modal Representation Learning
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA. · Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA. · Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Lightweight Adaptation of EEG Foundation Models for Stroke Motor Imagery Decoding: Domain Shift and Subject-Level Robustness
Aug 31, 2026Anh T. Nguyen, Zihua Sun, Michelle J. JohnsonCross-Subject EEG Decoding MI Classification
School of Engineering and Applied Science, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA · School of Engineering and Applied Science, Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA · Department of Physical Medicine and Rehabilitation, Department of Bioengineering, and Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA
EEG Decoding Using CNN and LSTM Network
Aug 13, 2026Athanasios KaragounisCross-Subject EEG Decoding MI Classification
School of Science, Department of Digital Industry Technologies GR34400, Psachna, Greece
ATCNet-CIAM for Multi-Session Motor Imagery EEG Signal Classification
Jul 26, 2026Le Huu Son Hai, Nguyen Chi Hai, Truong Viet Vu +3MI Classification Brain-Computer Interfaces
Faculty of Information Technology, Van Lang School of Technology, Van Lang University, Ho Chi Minh City 70000, Vietnam
Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram
Jul 25, 2026Ethan DavisCross-Subject EEG Decoding MI Classification
University of Washington, USA
Subject-Level Heterogeneity in EEG Motor Imagery Decoding: A Large-Scale Benchmark and Portfolio-Based Reduction of the Search Space
Jul 24, 2026Xavier Vasques, Paul Barbaste, Olivier OullierMI Classification Algorithm Selection
IBM Technology, Bois-Colombes, France · IBM France Lab, Orsay, France · Wavestone, Paris, France +4
Generative Augmentation for EEG Motor Imagery Classification: A Class-Conditional VAE with Cycle-Consistent Decoder Refinement
Jul 22, 2026Matei Moldoveanu, Alain Sirois, Claire Ben Ali +2Synthetic Data Augmentation MI Classification
Naqi Logix, Paris, France · Inria Center at Univ. Bordeaux / LaBRI, Talence, France · LITIS, INSA Rouen-Normandy, France
Average Rankings Mask Per-Subject Optimality: A Friedman-Nemenyi Benchmark of EEG Motor-Imagery BCI Decoders
Jun 23, 2026Xavier Vasques, Paul Barbaste, Olivier OullierMI Classification Brain-Computer Interfaces
IBM Technology, Bois-Colombes, France · IBM France Lab, Orsay, France · Inclusive Brains, Marseille, France +4
Learning aligned EEG representations with subject-specific encoders
Jun 15, 2026Bruna J. Lopes, Gabriel Schwartz, Sylvain Chevallier +2Cross-Subject EEG Decoding MI Classification
University of São Paulo, São Paulo, Brazil · Université Paris-Saclay, Inria TAU team, LISN-CNRS, France · Institut de neuromodulation, GHU Paris, psychiatrie et neurosciences, centre hospitalier Sainte-Anne, pôle hospitalo-universitaire 15, Université Paris Cité, Paris, France +3
A Domain-Informed Multi-Objective Framework for EEG Channel Selection in Motor Imagery BCIs
May 28, 2026Dekka Muni Kumar, Dhruba Jyoti Kalita, Yogesh Kumar MeenaMI Classification Brain-Computer Interfaces
are with Human-AI Interaction (HAIx) Lab, IIT Gandhinagar, India
Benchmarking Positional Encoding Strategies for Transformer-Based EEG Foundation Models
May 28, 2026Ayse Betul Yuce, Sebastian StoberMI Classification EEG Decoding
Department of Computer Science, Otto von Guericke University, Magdeburg, Germany
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
Variational Phasor Circuits for Phase-Native Brain-Computer Interface Classification
Mar 18, 2026Dibakar SigdelMI Classification Brain-Computer Interfaces
Mindverse Computing LLC, WA 98087
Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery
Nov 24, 2025Sanjeev Manivannan, Chandra Shekar LakshminarayanCross-Subject EEG Decoding MI Classification
Department of Biotechnology · Department of Data Science and Artificial Intelligence
Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals
Oct 17, 2025Shuntaro Suzuki, Shunya Nagashima, Komei SugiuraMI Classification Brain-Computer Interfaces
Keio University
EEG-based AI-BCI Wheelchair Advancement: Transformer-Based Learning with Motor Imagery for Brain Computer Interface
Sep 30, 2025Bipul Thapa, Biplov Paneru, Bishwash Paneru +1MI Classification Brain-Computer Interfaces
Department of Computer Science and Engineering, Kathmandu University, Kavre, Nepal · Department of Electronics and Communication Engineering, Nepal Engineering College, Pokhara University, Bhaktapur, Nepal · Department of Applied Sciences and Chemical Engineering, Institute of Engineering, Pulchowk Campus, Tribhuvan University, Lalitpur, Nepal

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Separating personal from population gains when calibrating EEG foundation models for new users

Xilin Tao, Kani Chen

Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China · Department of Industrial Engineering and Decision Analytics, The Hong Kong University of Science and Technology, Hong Kong SAR, China

Cross-Subject EEG Decoding MI Classification Brain-Computer Interfaces EEG Decoding EEG Foundation Models

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Foundation models are increasingly adapted to individual users, but an apparent personalization gain can simply reflect a stronger population model. This distinction matters for brain-computer interfaces, where every new user must be calibrated. We evaluated personal adaptation of three frozen EEG foundation models (CBraMod, REVE and LaBraM) in 235 held-out subjects from three motor-imagery datasets, comparing each subject's adapter with the population model and with adapters fitted to other subjects. Using all first-half session labels, personal adapters improved mean balanced accuracy over the population model by 1.5-5.4 percentage points and outperformed exchanged adapters by 2.3-7.3 points in all nine model-dataset combinations. The size of this benefit depended on population training: with four times the original budget, median gains remained positive (1.0-2.0 points) but were smaller for every model, and no population model reached a confirmed plateau. Acquiring the benefit cheaply was unreliable: few-label calibration was consistently non-negative on only one dataset, and in CBraMod neither unlabeled context nor meta-learned initialization outperformed matched controls. Personalization should therefore be evaluated against both a population reference and exchanged parameters, across population-training budgets.

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