Stress detection for cognitive rehabilitation in COVID-19 scenario
Ahona Ghosh, Sima Das, Sriparna Saha
Abstract
Due to the current demand for emerging technologies like the Internet of Things integrated with machine learning in industry and academics, brain-computer interface tools like electroencephalogram in healthcare have drawn worldwide attention. As has been noticed that during recent times, mobile phone exposure to people increased in at least 2-fold way, so games have been used as stimuli for detecting how our brain becomes overburdened with increased exposure. After the data acquisition from 14 channels of an electroencephalogram, the activated regions were identified. Features were extracted from the most activated ten electrode channels using discrete wavelet transform. To reduce the dimensions of the feature space for enhancing the performance, principal component analysis was used. The mental state classification was performed using a support vector machine based on the detected stress. The proposed system has outperformed the existing ones for its effectiveness and efficiency in a broad application area of cognitive rehabilitation. Classification accuracy was obtained as 92.79% and different other metrics proved that the combination of channel selection, feature extraction, and classification methods in our proposed approach has outperformed the others. Privacy is maintained, and it is flexible to the user as per his/her convenient time.