The Electroencephalogram (EEG) Study for Estimating Endurance Sports Performance Base on Eigenvalues Extraction Method.

Zijian Zhou, Hongqi Xu, Yubing Sun, Guangda Liu

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Article in Brain sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

Upper Limb Fatigue Information Variation Analysis Based on Parallel Brain and Muscle Functional Networks.Annals of biomedical engineering · 2026
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4 authors.

Zijian ZhouResearch Field of Medical Instruments and Bioinformation Processing, College of Instrumentation and Electrical Engineering, Jilin University, No. 938 West Democracy Street, Changchun 130061, China.

Hongqi XuResearch Center of Exercise Capacity Assessment and Promotion, School of Sports Science and Physical Education, Northeast Normal University, Changchun 130024, China.

Yubing SunResearch Field of Medical Instruments and Bioinformation Processing, College of Instrumentation and Electrical Engineering, Jilin University, No. 938 West Democracy Street, Changchun 130061, China.

Guangda LiuResearch Field of Medical Instruments and Bioinformation Processing, College of Instrumentation and Electrical Engineering, Jilin University, No. 938 West Democracy Street, Changchun 130061, China.

Funding

Key Laboratory of Human Factors and Ergonomics for State Market Regulation 2023SYSKF02002National Key R&D Program of China 2023YFF0615902

6 · The paper itself

Abstract

objectivesBrain-behavior connections are a new means to evaluate sports performance. This electroencephalogram (EEG) study aims to estimate endurance exercise performance by investigating eigenvalue trends and comparing their sensitivity and linearity.

methodsTwenty-three cross-country skiers completed endurance cycling tasks. Twenty-four-channel full-brain EEG signals were recorded in the motor phase and recovery phase continuously. Eighteen EEG eigenvalues calculation methods were collected, commonly used in previous research. Time-frequency, band power, and nonlinear analyses were used to calculate the EEG eigenvalues. Their regression coefficients and correlation coefficients were calculated and compared, with the linear regression method.

resultsThe time-frequency eigenvalues shift slightly throughout the test. The power eigenvalues changed significantly before and after motor and recovery, but the linearity was not satisfactory. The sensitivity and linearity of the nonlinear eigenvalues were stronger than the other eigenvalues. Of all eigenvalues, Shannon entropy showed completely non-overlapping distribution intervals in the regression coefficients of the two phases, which were -0.1474 ± 0.0806 s

conclusionsThe Shannon entropy method provided more accurate estimations for endurance exercise performance compared to other eigenvalues.

Indexed as

cyclingelectroencephalographyelite athletesindividual alpha peak frequencyshannon entropy

Identifiers

PMID39595898

DOI10.3390/brainsci14111135

PMCPMC11591843

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