Workload detection based on EEG device for teleoperation of a mobile robot

This paper proposes a brain signals based method to evaluate a human operator workload while teleoperating a mobile robot in presence of time delay. The procedure based on denoising and decomposition wavelet transform can be applied to raw EEG signals, analyzing the energy in the delta band to detect four states of cognitive workload. The index obtained is validated using the NASA-TLX question nary and quantitative metrics like time-to-complete the task and error of path tracking.