Chronic pain detection from resting-state raw EEG signals using improved feature selection

• URL: http://arxiv.org/abs/2306.15194v1
• Date: Tue, 27 Jun 2023 04:28:52 GMT
• Title: Chronic pain detection from resting-state raw EEG signals using improved feature selection
• Authors: Jean Li, Dirk De Ridder, Divya Adhia, Matthew Hall, Jeremiah D. Deng
• Abstract summary: We present an automatic approach that works on resting-state raw EEG data for chronic pain detection. A new feature selection algorithm - modified Sequential Floating Forward Selection (mSFFS) - is proposed.
• Score: 1.5899411215927988
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present an automatic approach that works on resting-state raw EEG data for chronic pain detection. A new feature selection algorithm - modified Sequential Floating Forward Selection (mSFFS) - is proposed. The improved feature selection scheme is rather compact but displays better class separability as indicated by the Bhattacharyya distance measures and better visualization results. It also outperforms selections generated by other benchmark methods, boosting the test accuracy to 97.5% and yielding a test accuracy of 81.4% on an external dataset that contains different types of chronic pain

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