An Algorithm to Measure the Stress Level from EEG, EMG and HRV Signals

This work proposes the analysis in time and frequency of EEG and EMG waves with the purpose of obtaining stress states in 5 levels. Due to the advance and evolution of technology, it is possible to obtain low-cost brain-computer interfaces with greater ease in neurofeedback sessions, which, in turn, helps to reduce stress levels in individuals and this requires a deeper analysis due to a scarce investigation. Different studies are limited to only obtaining binary results, that is to say, if an individual is in a state of stress or not, but they do not present results in a scale of levels. We analyzed 6 EEG channels with a sampling frequency of 250 Hz following the 10-20 standard and 1 EMG channel decomposed in the time and frequency domain obtaining parameters with the discrete wavelet transform and energy per band. The parameters obtained from each signal were entered into a k-NN classifier. In the same way, for the validation, the stress level was established by the graphic analysis of the heart rate variability following Baevsky's method, following with the measurement of the relaxation and stress of differents students while subjecting them to psychotechnical tests. The proposed algorithm was able to differentiate in most cases and quantify the states, reaching an accuracy level of 92%.