Event-related potentials (ERPs) of electroencephalogram (EEG) are often used as features for brain machine interfaces or for analysis of brain activities. However, as EEG signals easily suffer from various artifacts, ERPs are often collapsed and hard to observe. There are several attempts at using multi-channel EEG signals to enhance EEG signals of interest and make ERPs more clearly observed. For example, a previous work has proposed a blind EEG signal separation method using a multi-channel Wiener filter designed with a probabilistic generative model of observed EEG signals. This method copes with the under-determination of EEG signal separation by assuming sparseness of each EEG component in the time-frequency domain. Although this method blindly separates EEG signals into individual EEG components using time-varying scaled spatial correlation matrices, target EEG components, such as P300 of ERP, are often known in advance in some applications. In this paper, inspired by this previous work, we propose a probabilistic EEG signal enhancement method using a multi-channel Wiener filter, newly incorporating prior information of the spatial correlation matrices related to the target EEG component in the probabilistic generative model to improve performance of EEG signal enhancement. An experimental evaluation for P300 enhancement shows that the proposed method significantly reduces artifacts.

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 potential (ERP) components as N400 and the late positivity (P600) are evoked in the auditory where semantic and syntactic anomalies occur. We used 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 multilayer perceptron achieved 59.5% (semantic) and 57.7% (syntactic) accuracies.

This study investigates quality prediction methods for synthesized speech using EEG. Training a predictive model using EEG is challenging due to a small number of training trials, a low signal-to-noise ratio, and a high correlation among independent variables. When a predictive model is trained with a machine learning algorithm, the features extracted from multi-channel EEG signals are usually organized as a vector and their structures are ignored even though they are highly structured signals. This study predicts the subjective rating scores of synthesized speeches, including their overall impression, valence, and arousal, by creating tensor structured features instead of vectorized ones to exploit the structure of the features. We extracted various features to construct a tensor feature that maintained their structure. Vectorized and tensorial features were used to predict the rating scales, and the experimental result showed that prediction with tensorial features achieved the better predictive performance. Among the features, the alpha and beta bands are particularly more effective for predictions than other features, which agrees with previous neurophysiological studies.

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SUMMARYIn this paper a new method for noise removal from singletrial event-related potentials recorded with a multi-channel electroencephalogram is addressed. An observed signal is separated into multiple signals with a multi-channel Wiener filter whose coefficients are estimated based on parameter estimation of a probabilistic generative model that locally models the amplitude of each separated signal in the time-frequency domain. Effectiveness of using prior information about covariance matrices to estimate model parameters and frequency dependent covariance matrices were shown through an experiment with a simulated event-related potential data set.