Synchronization between overt speech envelope and EEG oscillations during imagined speech.

Neural oscillations synchronize with the periodicity of external stimuli such as the rhythm of the speech amplitude envelope. This synchronization induces a speech-specific, replicable neural phase pattern across trials and enables perceived speech to be classified. In this study, we hypothesized that neural oscillations during articulatory imagination of speech could also synchronize with the rhythm of speech imagery.

Electroencephalogram-Based Single-Trial Detection of Language Expectation Violations in Listening to Speech.

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.

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.

Quality prediction of synthesized speech based on tensor structured EEG signals.

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.

Removing noise from event-related potentials using a probabilistic generative model with grouped covariance matrices.

Analysis of electroencephalograms (EEG) usually suffers from a variety of noises. In this paper, we propose a new method for background noise removal from single-trial event-related potentials (ERPs) recorded with a multi-channel EEG. An observed signal is separated into multiple signals with a multi-channel Wiener filter, whose coefficients are estimated based on a probabilistic generative model in the time-frequency domain.

Automated social skills training with audiovisual information.

People with social communication difficulties tend to have superior skills using computers, and as a result computer-based social skills training systems are flourishing. Social skills training, performed by human trainers, is a well-established method to obtain appropriate skills in social interaction. Previous works have attempted to automate one or several parts of social skills training through human-computer interaction.

An evaluation of EEG ocular artifact removal with a multi-channel wiener filter based on probabilistic generative model.

Data contamination by ocular artifacts such as eye blinks and eye movements is a major barrier that must be overcome when attempting to analyze electroencephalogram (EEG) and event-related potential (ERP) data. To handle this problem, a number of artifact removal methods has been proposed. Specifically, we focus on a method using a multi-channel Wiener filters based on a probabilistic generative model.