Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interface

arXiv:2404.15350v1 Announce Type: cross
Abstract: Electroencephalography (EEG) classification is a versatile and portable technique for building non-invasive Brain-computer Interfaces (BCI). However, the classifiers that decode cognitive states from EEG brain data perform poorly when tested on newer domains, such as tasks or individuals absent during model training. Researchers have recently used complex strategies like Model-agnostic meta-learning (MAML) for domain adaptation. Nevertheless, there is a need for an evaluation strategy to evaluate the fast adaptability of the models, as this characteristic …

abstract adaptability arxiv bci brain brain-computer interface building classification classifiers cognitive computer cs.hc cs.lg data decode domains eeg eess.sp however interfaces networks neural networks researchers tasks training type