A custom-designed mental task-based brain-computer interface

At present, brain-computer interfaces cannot be used in real-life applications mainly because of their high false activation rates. To achieve a zero false positive rate, a mental task-based brain-computer interface custom designed for each subject and each task is proposed. The most discriminatory mental task is determined for each subject. We used the EEG signals of four subjects recorded while they were performing five different mental tasks. Autoregressive modeling and stationary wavelet transform are used in the process of feature extraction. Classification is based on quadratic discriminant analysis. For the most discriminatory mental task of each subject, we achieved a false positive rate of zero value while the true positive rate obtained was above 60%.