We researched on Novel Muscle fatigue detection and estimation criterions in EEG domain and verified results by correlating with already discovered features such as PSD and Median Frequency in sEMG domain. 14 healthy subjects, 10 male and 4 female participated for the Data collection. The experimental procedure involved lifting of 3 kg weight for both male and female subjects. The end time signified the moment when a visible fatigue by the vibration of the hands were shown by the subjects. Three such trials were taken with a 10 minute rest time in between successive trials. The analysis yeilded parameter for classifying stages of fatigue development before a break point. The parameter that can estimate the fatigue levels and provide a break point for maximum fatigue is useful for physiology. The Brain Computer Interface aids in increasing efficacy in arduous tasks. Examples can be of athletes in training or people working continuously under mines where a monitor for the fatigue break point can help increase the overall productivity, preventing exaggeration and exhaustion.
“Fatigue Detection and Estimation using Auto-Regression analysis in EEG”
Abhinandan Jain, Baqar Abbas, Omar Farooq, Shashank K. Garg
5th International Conference on Advances in Computing, Communications and Informatics (ICACCI-2016), Jaipur.