Abstract
OBJECTIVES: Brain-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. METHODS: Twenty-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. RESULTS: The 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(-1) in the motor phase and 0.2560 ± 0.1365 s(-1) in the recovery phase. Shannon entropy amplitude decreased more in the F region of the brain than in the other regions. Additionally, the higher the level of sport, the slower the decline in Shannon entropy of the athlete. CONCLUSIONS: The Shannon entropy method provided more accurate estimations for endurance exercise performance compared to other eigenvalues.