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11 September 2024 Multifeature attention detection algorithm based on power spectral density and differential entropy
Peng Yuan, Yan Bian, Yanli Liu, Shanshan Li, Chunxiao Han
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Proceedings Volume 13270, International Conference on Future of Medicine and Biological Information Engineering (MBIE 2024); 132700K (2024) https://doi.org/10.1117/12.3039982
Event: 2024 International Conference on Future of Medicine and Biological Information Engineering (MBIE 2024), 2024, Shenyang, China
Abstract
Attention is closely related to human life. To detect attention states quickly and accurately with fewer resources, this research proposes a method for attention state detection, it is based on differential entropy (DE) and power spectral density (PSD). Electroencephalogram (EEG) data is from 15 participants. It was processed using the Fast Fourier Transform (FFT) to extract DE and PSD features, which was normalized. These features were input into a Support Vector Machine (SVM). After optimizing the model parameters, it achieved a well-performing attention state detection model. The proposed method achieved a maximum classification accuracy of 85% and an average accuracy of 67%, The model described in the statement surpasses traditional SVM models that are trained solely on DE or PSD features, as well as single-channel or multi-channel SVM models. The new method can be used to learn additional features for attention verification and generalizes well for the task of developing a robust deep learning system.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Peng Yuan, Yan Bian, Yanli Liu, Shanshan Li, and Chunxiao Han "Multifeature attention detection algorithm based on power spectral density and differential entropy", Proc. SPIE 13270, International Conference on Future of Medicine and Biological Information Engineering (MBIE 2024), 132700K (11 September 2024); https://doi.org/10.1117/12.3039982
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KEYWORDS
Electroencephalography
Electrodes
Feature extraction
Spectral density
Detection and tracking algorithms
Deep learning
Support vector machines
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