Abstract
When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the viewer’s occipital pole exhibits so-called code-modulated visual evoked potentials (cVEPs), the phase lags of which can be tied to these targets. The cVEP paradigm has enjoyed interest in the brain-computer interfacing (BCI) community for the reported high information transfer rates (ITR, in bits/min). In this study, we introduce a novel decoding algorithm based on spatiotemporal beamforming, and show that this algorithm is able to accurately identify the gazed target. Especially for a small number of repetitions of the coding sequence, our beamforming approach significantly outperforms an optimised support vector machine (SVM)-based classifier, which is considered state-of-the-art in cVEP-based BCI. In addition to the traditional 60 Hz stimulus presentation rate for the coding sequence, we also explore the 120 Hz rate, and show that the latter enables faster communication, with a maximal median ITR of 172.87 bits/min. Finally, we also report on a transition effect in the EEG signal following the onset of the stimulus sequence, and recommend to exclude the first 150 ms of the trials from decoding when relying on a single presentation of the stimulus sequence.
Highlights
Among the gamut of brain-computer interfacing (BCI) paradigms[1], the code-modulated visual evoked potential has been reported to the yield one of the highest information transfer rates (ITRs)[2]
CVEP has achieved among the highest ITR, the paradigm is considerably less studied compared to other visual BCI paradigms such as the P300 event-related potential (ERP) and the steady-state visual evoked potential (SSVEP)
Using the individually optimised channel sets, the target identification accuracy for both the spatiotemporal beamformer- and the support vector machine (SVM)-based classifier are shown in Fig. 4c for S60 and Fig. 5c for S120, both with and without the exclusion of the initial 150 ms of the stimulation
Summary
Among the gamut of brain-computer interfacing (BCI) paradigms[1], the code-modulated visual evoked potential (cVEP) has been reported to the yield one of the highest information transfer rates (ITRs)[2]. The authors investigated the effect of stimulation colour[10,13], classifier kernels[11] and filter bands[12], but could not achieve a higher decoding performance for the faster stimulus rate. In one of their studies[11], they report on the decoding performance with an increasing number of m-sequence repetitions, but did not consider the implication on the performance in terms of ITR. The goal of this study is to assess the performance of the spatiotemporal beamforming algorithm for target identification when using cVEP-based encoding, and to compare the performance for both traditional (60 Hz) and high-speed (120 Hz) stimulus presentations
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