Calibration-less detection of steady-state visual evoked potentials-comparisons and combinations of methods

Brain-Computer Interfaces (BCIs) represent a great challenge in signal processing and machine learning, because it is difficult to extract discriminant features corresponding to particular brain responses due to the low signal-to-noise ratio of the EEG signal. Steady-state visual evoked potentials (SSVEPs) are one of the most reliable brain responses to detect in the EEG signal. Although advanced supervised machine learning techniques can improve the classification performance of SSVEP responses, obtaining robust techniques that do not rely on training a classifier is also important. We propose to analyze, compare, and combine the performance of three state-of-the-art techniques for the detection of SSVEP responses across 10 subjects and different time segments to determine if robust classification can be obtained without subject-specific rigorous analysis using a combination of one or more techniques. The methods include two approaches based on spatial filtering, and canonical correlation analysis. The results support the conclusion that the choice of the method does not depend on the time segment, and the current techniques provide equivalent performance.