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Fast Recognition of Anticipation-Related Potentials

Anticipation increases the efficiency of daily tasks by partial advance activation of neural substrates involved in it. Here, we develop a method for the recognition of EEG correlates of this activation as early as possible on single trials, which is essential for brain-computer interaction. We explore various features from the EEG recorded in a contingent negative variation (CNV) paradigm. We also develop a novel technique called time aggregation of classification (TAC) for fast and reliable decisions that combines the posterior probabilities of several classifiers trained with features computed from temporal blocks of EEG until a certainty threshold is reached. Experiments with nine naive subjects performing the CNV experiment with GO (anticipation) and NOGO (control) conditions with an interstimulus interval of 4 s show that the performance of the TAC method is above 70% for four subjects, around 60% for two other subjects, and random for the remaining subjects. On average over all subjects, more than 50% of the correct decisions are made at 2 s, without needing to wait until 4 s.