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Auditory brain-computer interfaces (BCI) provide a method of non-muscular commu-nication and control for late-stage amyotrophic lateral sclerosis (ALS) patients, who have impaired eye movements or compromised vision. In this study, random sequences of spoken digits were presented as auditory stimulation. According the protocol, the subject should pay attention to target digits and ignore non-target digits. EEG data were recorded and the components of P300 and N200 were extracted as features for pattern recognition. Fisher classifier was designed and provided likelihood estimates for the Dynamic Stopping Criterion (DSC). Dynamic data collection was controlled by a threshold of the posterior probabilities which were continually updated with each additional measurement. In addition, the experiment would be stopped and the decision was made once the probabilities were above the threshold. The results showed that this paradigm could effectively evoke the characteristic EEG, and the DSC algorithm could improve the accuracy and communication rate.

Auditory BCI Research Using Spoken Digits Stimulation and Dynamic Stopping Criterion