Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification

arXiv:2402.09438v1 Announce Type: cross
Abstract: Motor imagery (MI) classification based on electroencephalogram (EEG) is a widely-used technique in non-invasive brain-computer interface (BCI) systems. Since EEG recordings suffer from heterogeneity across subjects and labeled data insufficiency, designing a classifier that performs the MI independently from the subject with limited labeled samples would be desirable. To overcome these limitations, we propose a novel subject-independent semi-supervised deep architecture (SSDA). The proposed SSDA consists of two parts: an unsupervised and a supervised element. The …

abstract architecture arxiv bci brain brain-computer interface classification classifier computer cs.lg data designing eeg eess.sp independent samples systems type