学术文献库

The purpose of this study is to develop a new methodology for designing stimulus sequences for cVEP BCI based on experimental studies regarding the behavior and the properties of the actual EEG responses of the visual system to coded visual stimuli, such that training time is reduced and the possible number of targets is increased. EEG from 8 occipital sites is recorded with 2000 samples/sec per channel, in response to visual stimuli presented on a computer monitor with 60Hz refresh rate. Onset and offset EEG responses to long visual stimulus pulses are obtained through 160-trial signal averaging. These edge responses are used to predict the EEG responses to arbitrary stimulus sequences using the superposition principle. A BCI speller which utilizes the target templates generated by this principle is also implemented and tested. It is found that certain short stimulus patterns can be accurately predicted by the superposition principle. BCI sequences that are constructed by combinations of these optimal patterns yield higher accuracy (95.9%) and ITR (57.2 bpm) compared to when the superposition principle is applied to conventional m-sequences and randomly generated sequences. Training time for the BCI application involves only the acquisition of the edge responses and is less than 4 minutes, and a huge number of sequences is possible. This is the first study in which cVEP BCI sequences are designed based on constraints obtained by observing the actual brain responses to several stimulus patterns.