摘要
The objective was to study changes in EEG time-domain Kolmogorov complexity under different mental fatigue state and to evaluate mental fatigue using Lempel-Ziv complexity analysis of spontaneous EEG in healthy human subjects. EEG data for healthy subjects were acquired using a net of 2 electrodes (Fp1 and Fp2) at PM 4:00, AM 12:00 and AM 3:00 in the 24 hours sleep-deprived mental fatigue experiments. It was presented that initial results for eight subjects examined in three different mental fa-tigue state with 2-channel EEG time-domain Lempel-Ziv complexity computations. It was found that the value of mean Lempel-Ziv com-plexity corresponding to a special mental state fluctuates within the special range and the value of C(n) increases with mental fatigue increasing for the total frequency spectrum. The result in-dicates that the value of C(n) is strongly cor-relative with the mental fatigue state. These re-sults suggest that it may be possible to nonin-vasively differentiate different mental fatigue level according to the value of C(n) for particular mental state from scalp spontaneous EEG data. This method may be useful in further research and efforts to evaluate mental fatigue level ob-jectively. It may also provide a basis for the study of effects of mental fatigue on central neural system.
The objective was to study changes in EEG time-domain Kolmogorov complexity under different mental fatigue state and to evaluate mental fatigue using Lempel-Ziv complexity analysis of spontaneous EEG in healthy human subjects. EEG data for healthy subjects were acquired using a net of 2 electrodes (Fp1 and Fp2) at PM 4:00, AM 12:00 and AM 3:00 in the 24 hours sleep-deprived mental fatigue experiments. It was presented that initial results for eight subjects examined in three different mental fa-tigue state with 2-channel EEG time-domain Lempel-Ziv complexity computations. It was found that the value of mean Lempel-Ziv com-plexity corresponding to a special mental state fluctuates within the special range and the value of C(n) increases with mental fatigue increasing for the total frequency spectrum. The result in-dicates that the value of C(n) is strongly cor-relative with the mental fatigue state. These re-sults suggest that it may be possible to nonin-vasively differentiate different mental fatigue level according to the value of C(n) for particular mental state from scalp spontaneous EEG data. This method may be useful in further research and efforts to evaluate mental fatigue level ob-jectively. It may also provide a basis for the study of effects of mental fatigue on central neural system.
