Abstract
Generally, the more channels are used to acquire EEG signals, the better the performance of the brain–computer interface (BCI). However, from the user’s point of view, a BCI system comprising a large number of channels is not desirable because of the lower comfort and extended application time. Therefore, the current trend in BCI design is to use the smallest number of channels possible. The problem is, however, that usually when we decrease the number of channels, the interface accuracy also drops significantly. In the paper, we examined whether it is possible to maintain the high accuracy of a BCI based on steady-state visual evoked potentials (SSVEP-BCI) in a low-channel setup using a preprocessing procedure successfully used in a multichannel setting: independent component analysis (ICA). The influence of ICA on the BCI performance was measured in an off-line (24 subjects) mode and online (eight subjects) mode. In the off-line mode, we compared the number of correctly recognized different stimulation frequencies, and in the online mode, we compared the classification accuracy. In both experiments, we noted the predominance of signals that underwent ICA preprocessing. In the off-line mode, we detected 50% more stimulation frequencies after ICA preprocessing than before (in the case of four EEG channels), and in the online mode, we noted a classification accuracy increase of 8%. The most important results, however, were the results obtained for a very low luminance (350 lx), where we noted 71% gain in the off-line mode and 11% gain in the online mode.
Highlights
A visual evoked potential (VEP) is an electrical potential that can be derived from the scalp after a visual stimulus such as a flashing light
We analyzed the efficiency of stimulation frequency detection (ESFD) before and after independent component analysis (ICA) application for each 2 s of the recording (Fig. 4, Table 2)
The detection efficiency for one subject was calculated as the ratio between the number of detected stimulation frequencies and the total number of frequencies provided for the subject: ESFD = FNSSVEP ∗ 100, FN
Summary
A visual evoked potential (VEP) is an electrical potential that can be derived from the scalp after a visual stimulus such as a flashing light. Some authors report that the periodicity is observable when the stimulus frequency exceeds 3.5 Hz [1, 2] or 4 Hz [3, 4], while others state that SSVEP is a type of event-related. SSVEP is believed to have a stable amplitude (size) and phase (temporal shift) over time [9]. It is most prominent over occipital cortical areas [10] and presents strong immunity to physiological artifacts, such as eye and body movements [11,12,13,14,15]. Since SSVEP fundamental frequency is mainly the same as the frequency of the stimulus [4, 16], by providing stimuli of different frequencies, different SSVEPs can be evoked
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