Inactive-state recognition from EEG signals and its application in cognitive load computation

Extraction of desirable information from electroencephalogram signals require same level of active involvement from the participants throughout the entire duration of the task. However, this is hard to attain due to environmental, personal and internal factors including thought processes. This poses a major challenge in realizing accurate evaluation of mental workload. This study is aimed at detection of the inactive mental states of the participant during an experimental task. Conventionally cognitive load is computed with respect to the baseline period. Here a novel approach is adopted based on the detection of most inactive mental state during the rest period. It is observed that alpha rhythms (8 - 12 Hz) are dominant than theta rhythms (4 - 7 Hz) during the rest state and this information is used in determining the most inactive mental states. Galvanic skin response (GSR) is also analyzed for the same purpose to validate the decoded mental state from the brain signals. Results indicate that the proposed approach of inactivity detection, improves the overall accuracy of detection of cognitive load by 15.57 %.