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). Collectively, these characteristics make the classifier easy-to-use, especially for caregivers and others with limited technical expertise. We evaluated the performance of our classifier on a publicly available benchmark dataset from 35 healthy participants. We compared the information transfer rate (ITR) of this new classifier to those of the minimum energy combination (MEC), maximum synchronization index (MSI), and filter bank canonical correlation analysis (FBCCA). The new classifier increases average ITR to 123.5 bits-per-minute (bpm), 47.5, 51.2, and 19.5 bpm greater than the MEC, MSI, and FBCCA classifiers, respectively.

Front-End Replication Dynamic Window (FRDW) for Online Motor Imagery Classification

“…Chen et al [37] proposed a filter bank canonical correlation analysis based training-free DW recognition approach for SSVEP. Hadi et al [38] proposed a novel DW classifier, using ensembling learning for SSVEP recognition. [39], [40] enhanced DW threshold selection to further improve the ITR for SSVEP classification.…”

Adaptive Window Method Based on FBCCA for Optimal SSVEP Recognition

“…This method significantly outperformed STE-DW in online experiments [14]. In a recent study [15], a new dynamic window method was proposed based on the majority voting system. The window length was dynamically extended until either the number of votes for one target exceeded a pre-determined threshold or reached a preset maximum value.…”