Nonlinear analysis of multi-channel EEG and its application to mental workload detection / 生物医学工程学杂志

Mental workload research is important to people's health and work efficiency, Psychophysiological measures such as electroencephalography (EEG), ECG and respiration measures can be used to predict mental workload level. A Multi-channel phase-space reconstruction method is proposed in this paper which rearranges signal serials by the correlation coefficients and select time delay by signal determinism. The study of determinism and correlation dimension on simulative data exhibits a good performance. The result of EEG series shows a clearly consistency to workload level variety. The method is useful for multi-channel signals nonlinear analysis and mental workload detection.

Analyzing sleep EEG using correlation dimension and approximate entropy / 生物医学工程学杂志

Correct sleep scoring is the base of sleep studying; nonlinear features of EEG can represent different sleep stages. In this paper, correlation dimension (D2) and approximate entropy (ApEn) of sleep EEG have been calculated. The statistical results reveal that: D2 does not come to be saturated when the embedding dimension increases, but the relative value of D2 can effectively distinguish different sleep stages. ApEn has the advantage of calculating simply, steady result and representing preferably different sleep stages. ApEn and the relative value of D2 reveal, from different point of view, the same rule about EEG (brain) complexity changing, that is, both complexity and its fluctuation are maximal in the subject's awake hour, are decreasing with the deepening of sleep, but the complexity in REM is about the level between S1 and S2.