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The purpose of this study is to develop a new methodology for designingstimulus sequences for cVEP BCI based on experimental studies regarding thebehavior and the properties of the actual EEG responses of the visual system tocoded visual stimuli, such that training time is reduced and the possiblenumber of targets is increased. EEG from 8 occipital sites is recorded with2000 samples/sec per channel, in response to visual stimuli presented on acomputer monitor with 60Hz refresh rate. Onset and offset EEG responses to longvisual stimulus pulses are obtained through 160-trial signal averaging. Theseedge responses are used to predict the EEG responses to arbitrary stimulussequences using the superposition principle. A BCI speller which utilizes thetarget templates generated by this principle is also implemented and tested. Itis found that certain short stimulus patterns can be accurately predicted bythe superposition principle. BCI sequences that are constructed by combinationsof these optimal patterns yield higher accuracy (95.9%) and ITR (57.2 bpm)compared to when the superposition principle is applied to conventionalm-sequences and randomly generated sequences. Training time for the BCIapplication involves only the acquisition of the edge responses and is lessthan 4 minutes, and a huge number of sequences is possible. This is the firststudy in which cVEP BCI sequences are designed based on constraints obtained byobserving the actual brain responses to several stimulus patterns.

New Approach for Designing cVEP BCI Stimuli Based on Superposition of Edge Responses