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Disabled patients using brain computer interface (BCI) applications have a more convenient life. The present study implements an electroencephalogram (EEG)-based signal processing algorithm for controlling a wireless mobile vehicle through imagination. The aim is to improve the filtered common spatial pattern (CSP) algorithm for BCI applications. The proposed method is a combination of the CSP projection with a Modified Secondary Projection of the filtered Common Spatial Pattern (MSPCSP). With this algorithm, distinctive differential features are obtained from the combination of the MSPCSP and CSP projection eigenvalues to identify four classes: moving-forward-for-pause, stop-for-pause, moving-forward-continuously, and stopped-continuously. The second contribution is the design of a task to produce clear imaginary movement patterns. The task is a combination of brain stimulation by viewing red and yellow sketches of the right hand that indicate opening the hand and making a fist. Eighteen subjects participated in the experiment for wireless control of a mobile vehicle in offline and real-time modes. The results were then evaluated through an accuracy and paired t-test statistical analysis for offline and real-time signal processing. The results based on the MSPCSP projection showed significant improvements in accuracy in comparison with the CSP projection: 82.16± 9.04% with <inline-formula> <tex-math notation="LaTeX">$p < 0.05$ </tex-math></inline-formula> and 70.83± 8.27% for offline and real-time processing, respectively. In addition, the MSPCSP projection attained higher accuracies of 14.72% and 13.33% for offline and real-time processing, respectively. It was concluded that the MSPCSP projection generates more discriminant differential features than the filtered CSP projection. Further, the MSPCSP projection with the thresholds extend the limitation of CSP-based methods from two- to four-class identification.

A Combined Projection for Remote Control of a Vehicle Based on Movement Imagination: A Single Trial Brain Computer Interface Study