Stress Detection and Audio-Visual Stimuli Classification From Electroencephalogram

Abstract

Electroencephalogram (EEG) is the graphical representation of Brain’s electrical activity. Mental stress can be detected in many ways and EEG is one of them. Regular mental stress gives rise to many mental disorders and it may cause various physiological and psychological diseases. As a result, early-stage detection of stress is very important. In this research, brain activity was recorded through EEG headset during inducing different levels of stress from audio-visual stimulus. Again, for better interaction between humans and machines, it is essential to analyze the power spectrum of the brain in response to different audio and visual stimulus. To better evaluate visual and auditory stress, an automated system is designed to differentiate among various audio and visual evoked potentials. This may further help for designing different assistive devices for the people having visual and hearing disability. In this paper, we proposed a framework to classify different levels of stress in response to audio and visual stimuli and also classified between these two stimuli by analyzing EEG signals. Raw EEG data was collected in lab environment and the necessary pre-processing steps were applied for denoising. By extracting robust features from the denoised audio and visual data, binary and multi-level stress were classified. A binary classification between audio and visual stimuli was also successfully done in this research. We achieved highest accuracy for binary stress classification 97.14% from visual stimuli, whereas we achieved 94.51% accuracy for auditory stimuli. Again, we achieved the accuracy for four level stress classification 89.59% for visual stimuli and 82.63% for audio stimuli.

Keywords

• Brain-computer interface (BCI)
• electroencephalogram (EEG)
• mental stress
• audio stimuli
• visual stimuli
• machine learning