Detecting neural changes during stress and fatigue effectively: a comparison of spectral analysis and sample entropy

Brain computer interface (BCI) technology as its name implies, relies upon decoding brain signals into operational commands. Aside from needing effective means of control, successful BCIs need to remain stable in varying physiological conditions. BCIs need to be developed with mechanisms to recognise and respond to physiological states (such as stress and fatigue) that can disrupt user capability. This paper compares a spectral analysis of EEG signals technique with a nonlinear method of sample entropy to detect changes in brain dynamics during moments of stress and fatigue. The results demonstrated few changes in the spectral frequency bands of the EEG during fatigue and stress conditions. However, when the EEG signals were analysed with the nonlinear technique of sample entropy the results indicated a reduction of complexity during moments of fatigue and stress and an increase in complexity during moments of engagement to the task.