Single-Trial Detection of Semantic Anomalies From EEG During Listening to Spoken Sentences.

We propose a method for the automatic detection of mismatched feelings that occur in communication. As our first step, we examined the semantically anomalous feelings from EEGs when participants listened to spoken sentences. Previous studies have shown that the event-related potentials (ERP) of an electroencephalogram (EEG) are evoked in the auditory and visual modalities where a semantic anomaly occurs. We expand this knowledge and detect it from a single-trial ERP using machine learning techniques. We recorded the brain activity of eight participants as they listened to sentences that contained semantic anomalies and found that a combination of feature selection using linear discriminant analysis and linear kernel support vector machines achieved the highest accuracy that exceeded 60%. By applying this technique, we plan to detect other types of anomalies in practical situations.