2026
← All research
Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs Compact Convolutional Transformer for Subject-Independent Motor Imagery EEG-Based BCIs A Jackknife-inspired deep learning approach to subject-independent classification of EEG Subject-Independent Classification of P300 Event-Related Potentials Using a Small Number of Training Subjects Subject-Independent Classification of Motor Imagery Tasks in EEG Using Multisubject Ensemble CNN.
Generalizable EEG Decoding
Developing models that transfer across people and reduce the need for individual calibration.
EEG varies between people and recording sessions. This line of work examines subject-independent decoding for motor imagery and event-related potentials, including approaches trained on limited numbers of participants.
Research questions & methods
- Multisubject ensembles and cross-subject classification
- Attention mechanisms and compact transformers
- Learning with small subject pools and zero-calibration ERP approaches
Selected publications
2024
2023
2022
2022