4 papers
Tailoring deep learning for real-time brain-computer interfaces: From offline models to calibration-free online decoding
Martin Wimpff, Jan Zerfowski, Bin Yang
Despite the growing success of deep learning (DL) in offline brain-computer interfaces (BCIs), its adoption in real-time applications remains limited due to three primary challenge…
Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study
Martin Wimpff, Bruno Aristimunha, Sylvain Chevallier +1
This study investigates continual fine-tuning strategies for deep learning in online longitudinal electroencephalography (EEG) motor imagery (MI) decoding within a causal setting i…
Calibration-free online test-time adaptation for electroencephalography motor imagery decoding
Martin Wimpff, Mario Döbler, Bin Yang
Providing a promising pathway to link the human brain with external devices, Brain-Computer Interfaces (BCIs) have seen notable advancements in decoding capabilities, primarily dri…
EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms
Martin Wimpff, Leonardo Gizzi, Jan Zerfowski +1
The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding…