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EMG and EEG Pattern Analysis for Monitoring Human Cognitive Activity during Emotional Stimulation

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Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1427))

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Abstract

The paper describes the experiments on monitoring human cognitive activity with additional emotional stimulation and their results. The purpose of the research is to determine the characteristics of EMG and EEG signals that reflect an emotional state and cognitive activity dynamics. The experiments involved using a multi-channel bioengineering system. The channels for recording EEG signals (19 leads according to the 10–20 system), EMG signals (by the “corrugator supercilia” and “zygomaticus major” channels according to the Fridlund and Cacioppo methodology) and the protocol information channel were engaged. There is a description of an experimental scenario, which assumed that testees performed homogeneous calculating tasks. According to the experimental results, there were formed 1344 artifact-free EEG and EMG patterns with a duration of 4 s. During emotiogenic stimulation, an EMG signal by the corresponding channel intensifies and a power spectrum shifts to the low-frequency region.An emotional state interpreter based on a neural-like hierarchical structure was used to classify EMG patterns. The classification success was 93%. The authors have determined spectral characteristics and attractors of EEG patterns. The highlighted attractor features were: the averaged vector length for the i-th two-dimensional attractor projection; density of trajectories near its center. The most informative frequency range (theta rhythm) and leads (P3-A1, C3-A1, P4-A2, C4-A2) were selected. These features have revealed a decrease in testees’ cognitive activity after 30–40 min of work. After negative emotional stimulation, there was an increase in absolute power in the theta rhythm, an increase in the average vector length for the i-th two-dimensional attractor projection, and a decrease in the trajectory density in four central cells. Tasks success indicators were improving. The revealed EEG signal features allow assessing the current cognitive activity of a person taking into account the influence of emotional stimulation.

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Acknowledgements

The work has been done within the framework of the grant of the President of the Russian Federation for state support of young Russian PhD scientists (MК-1398.2020.9).

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Authors and Affiliations

  1. Tver State Technical University, Lenina Ave. 25, Tver, Russia

    Konstantin Sidorov, Natalya Bodrina & Natalya Filatova

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  1. Konstantin Sidorov
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  2. Natalya Bodrina
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Editors and Affiliations

  1. Voronezh State University, Voronezh, Russia

    Alexander Sychev

  2. Voronezh State University, Voronezh, Russia

    Sergey Makhortov

  3. Christian-Albrecht University of Kiel, Kiel, Schleswig-Holstein, Germany

    Bernhard Thalheim

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Sidorov, K., Bodrina, N., Filatova, N. (2021). EMG and EEG Pattern Analysis for Monitoring Human Cognitive Activity during Emotional Stimulation. In: Sychev, A., Makhortov, S., Thalheim, B. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2020. Communications in Computer and Information Science, vol 1427. Springer, Cham. https://doi.org/10.1007/978-3-030-81200-3_7

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  • DOI: https://doi.org/10.1007/978-3-030-81200-3_7

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