Electroencephalogram-Based Single-Trial Detection of Language Expectation Violations in Listening to Speech - Tanaka, Hiroki; Watanabe, Hiroki; Maki, Hayato; Sakriani, Sakti; Nakamura, Satoshi (Frontiers in Computational Neuroscience, 2019)
We propose an approach for the detection of language expectation violations that occur in communication. We examined semantic and syntactic violations from electroencephalogram (EEG) when participants listened to spoken sentences. Previous studies have shown that such event-related potentials (ERPs) components as N400 and the late positivity (P600) are evoked in the auditory where semantic and syntactic anomalies occur. We use this knowledge to detect language expectation violation from single-trial EEGs by machine learning techniques. We recorded the brain activity of 18 participants while they listened to sentences that contained semantic and syntactic anomalies and identified the significant main effects of these anomalies in the ERP components. We also found that a multi-layer perceptron achieved 59.5% (semantic) and 57.7% (syntactic) accuracies.