EEG-Based Driving Fatigue Alleviation Using Multi-Acupoint Electrical Stimulation Combine Music Conditioning Method

Wang, Fuwang; Kang, Xiaogang; Lu, Bin; Wang, Hao; Fu, Rongrong · 2021 · Crossref

DOI: 10.21203/rs.3.rs-827749/v1

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Summary

This study addresses the critical safety issue of driving fatigue, which contributes significantly to traffic accidents. While Electroencephalogram (EEG) signals are considered the "gold standard" for objectively detecting fatigue, mere detection is insufficient; effective alleviation methods are required. Existing relief strategies, such as breaks or caffeine, may disrupt work or have side effects. The authors propose a novel intervention: Multi-Acupoint Electrical Stimulation Combined with Music Conditioning (MESCMC). This method aims to alleviate mental fatigue by simultaneously applying electrical stimulation to multiple acupoints (including the Láogóng point and points on the waist, shoulders, and buttocks) and playing mid-rhythm music (70–100 BPM). The study evaluates the efficacy of MESCMC compared to Single-Acupoint Electrical Stimulation (SES) and normal driving conditions. The experiment involved 10 healthy subjects (8 males, 2 females, aged 24 ± 1.6 years) performing a monotonous driving simulation for three hours. EEG signals were recorded using a portable Emotiv device with 14 channels. The protocol consisted of seven 30-minute stages. Subjects underwent three conditions: normal driving, driving with SES (activated from stage 4), and driving with MESCMC (activated from stage 4). EEG signals were preprocessed using four-layer wavelet packet decomposition to extract δ, θ, α, and β rhythms. Fatigue was quantified using two primary metrics: Lempel-Ziv complexity (C0) of α and β rhythms, and relative power spectrum ratios (θ/β and (θ+α)/(α+β)). Lower complexity and higher slow-wave ratios indicate increased fatigue. The results demonstrated that in the normal driving condition, EEG complexity decreased and fatigue indicators worsened progressively from stage 1 to 7. In contrast, both intervention groups showed an upward trend in α and β complexity from stage 4 onwards, indicating fatigue alleviation. Crucially, the MESCMC group exhibited significantly higher complexity values and better fatigue mitigation than the SES group. The combined method proved more effective than single-point stimulation alone. The study concludes that MESCMC is a practical, low-cost, and individualized solution for relieving driving fatigue. By integrating non-invasive electrical stimulation with music, the method offers a superior alternative to traditional single-point stimulation, showing promise for real-world application in enhancing driver safety during long-distance or monotonous driving tasks.

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• drowsiness detection algorithms

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• Empirical Findings: physiological data