Year: 2025
Type: conference-paper
Abstract: In this study preliminary results on the classification of EEG hyperscanning data acquired using MUSE-S consumer-level EEG headband are presented. Five pairs of subjects were involved and the recording protocol contained three two-person tetris game sessions alternating with relaxation periods. The data were segmented and ten spectral and cross-coherence features were calculated. The features were arranged into feature matrices and Convolutional Neural Network model was trained to discriminate between the relaxation and gaming. Two different feature sets - the full set and a set containing only inter-subject cross-coherence features were tested. The results indicate that using the full feature set, relaxation and gaming periods were perfectly discriminated. Using only inter-subject cross-coherence features 94 % and 79 % classification accuracy for training and testing data was obtained, respectively.
Authors: Tarmo Lipping, Ahmad Sharif, Matin Beiramvand, Jari Turunen
Institutions: Tampere University, University of Tampere
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4415399614
FWCI:
Cites:
Cited by:
Related to: