Graph Regularized Tensor Factorization for Single-Trial EEG Analysis

Maki, Hayato; Tanaka, Hiroki; Sakti, Sakriani; Nakamura, Satoshi

doi:10.1109/icassp.2018.8461897

Cited by 4 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We will also improve our model using graph regularized tensor factorization (Maki et al, 2018) as well as non-negative matrix factorization, which we previously proposed. Automatic onset detection and the techniques of artificial shifted trials are also needed for completely automated anomaly detection (Kutas and Hillyard, 1980).…”

Section: Discussionmentioning

confidence: 99%

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

Tanaka

Watanabe

Maki

et al. 2019

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Tanaka

Watanabe

Maki

et al. 2019

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sensing technology now enables analyzing user's behaviors and physiological signals (heart-rate, EEG, etc). Various signal processing and machine learning methods can be used for such prediction tasks [11,12,14]. Beyond sensing, it is also important to analyze human behaviors, to model and to implement training methods (e.g.…”

Section: Workhop Goalmentioning

confidence: 99%

2nd Workshop on Social Affective Multimodal Interaction for Health (SAMIH)

Tanaka

Nakamura

Martin³

et al. 2021

Proceedings of the 2021 International Conference on Multimodal Interaction

Self Cite

View full text Add to dashboard Cite

This workshop discusses how interactive, multimodal technology such as virtual agents can be used in social skills training for measuring and training social-affective interactions. Sensing technology now enables analyzing user's behaviors and physiological signals.Various signal processing and machine learning methods can be used for such prediction tasks. Such social signal processing and tools can be applied to measure and reduce social stress in everyday situations, including public speaking at schools and workplaces. CCS CONCEPTS• Applied computing → Health care information systems.

show abstract