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cs.HC2025
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…
cs.HC2023
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…
cs.HC2023
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…