期刊
2023 IEEE 7TH PORTUGUESE MEETING ON BIOENGINEERING, ENBENG
卷 -, 期 -, 页码 104-107出版社
IEEE
DOI: 10.1109/ENBENG58165.2023.10175324
关键词
autism spectrum disorder; electroencephalography (EEG); 3D motion capture; machine learning
This paper presents a framework for collecting motion-related data through electroencephalography (EEG) recordings during walking and dancing imitation tasks. The results show that the modulation of mu power over the central EEG channels by action/perception cycles is discriminative of all motion-related tasks.
Action/perception cycles have been described to be impaired in autism spectrum disorder (ASD). This goes beyond typical motor coordination, including core symptoms of autism. However, the neural basis of action understanding and motor function impairment still remains poorly characterized. In this paper, we present a framework for motion-related data collection. Electroencephalography (EEG) is recorded during walking and dancing imitation tasks to allow motor function characterization. We also present the validation of the framework based on the analysis of mu frequency band activity on EEG signals from neurotypical individuals. mu power modulation over the central EEG channels by action/perception cycles showed to be discriminative of all motion-related tasks tested. Both time-frequency analysis and machine-learning approaches corroborate our results.
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