Continuous EEG Classification for a Self-paced BCI

Transferring electroencephalogram (EEG)-based brain-computer interface (BCI) systems from synchronous laboratory conditions to real-world applications and situations demands the continuous detection of brain patterns in which the user is in control of the timing and pace of the BCI instead of the computer. A self-paced BCI requires continuous analysis of the continuing brain activity, however, not only the intentional-control (IC) states have to be detected (e.g., motor imagery and imagination) but also the inactive periods, where the user is in a non-control state (NC). The nonstationary nature of the brain signals provides a rather unstable input resulting in uncertainty and complexity in the control. Intelligent processing algorithms adapted to the task at hand are a prerequisite for reliable self-paced BCI applications. This work presents a novel intelligent processing strategy for the realization of an effective self-paced BCI which has the capability to reduce noise as well as adaptation to continuous online biasing. A Savitzki-Golay filter has been applied to remove spikes/outliers while preserving the feature set structure. An anti-bias system is introduced which readjusts the classification output based on the brain's current and previous states. Furthermore, a multiple threshold algorithm is applied on the resultant unbiased classifier output for improved accuracy. These algorithms are tested on 4 real and 3 artificial datasets and results shown are considerably promising and demonstrate the significance of the proposed intelligent and adaptive algorithms.