A Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs.

ABSTRACT: We present a dynamic window-length classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that does not require the user to choose a feature extraction method or channel set. Instead, the classifier uses multiple feature extraction methods and channel selections to infer the SSVEP and relies on majority voting to pick the most likely target. The classifier extends the window length dynamically if no target obtains the majority of votes. Compared with existing solutions, our classifier: (i) does not assume that any single feature extraction method will consistently outperform the others; (ii) adapts the channel selection to individual users or tasks; (iii) uses dynamic window lengths; (iv) is unsupervised (i.e., does not need training). Collective

SUBMITTER: Habibzadeh H 

PROVIDER: S-EPMC8496754 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature