EEG-Based Classification of Implicit Intention During Self-Relevant Sentence Reading

Abstract

From electroencephalography (EEG) data during self-relevant sentence reading, we were able to discriminate two implicit intentions: 1) "agreement" and 2) "disagreement" to the read sentence. To improve the classification accuracy, discriminant features were selected based on Fisher score among EEG frequency bands and electrodes. Especially, the time-frequency representation with Morlet wavelet transforms showed clear differences in gamma, beta, and alpha band powers at frontocentral area, and theta band power at centroparietal area. The best classification accuracy of 75.5% was obtained by a support vector machine classifier with the gamma band features at frontocentral area. This result may enable a new intelligent user-interface which understands users' implicit intention, i.e., unexpressed or hidden intention.