Analysis of impact of Meditation on Cognitive Workload using EEG Signals

Abstract

Abstract

Mental activities can be indicated by the Cognitive workload which are useful in applications like Biomedical, Human Machine Interaction and Task analysis. The mental effort applied on the Working memory at a certain given time is commonly known as Cognitive load. The EEG Signals of Cognitive Workload can be studied and classified. The features such as Entropy, Energy, Power, etc. can be extracted from the EEG signals and processed using DWT and can be used to distinguish between load levels with high accuracy. Neurophysiology provides evidences that PFC (Pre-Frontal Cortex) is integral to the control of cognitive function. Studies have proved the attentional state, the control of eye, as well as a variety of high-level behavioral functions, such as working memory, response strategies, and rule learning have a correlation with the patterns of neuronal activity in sub- regions of Pre-Frontal Cortex. So, we can emphasize that the problem- solving activities, calculations are the major tasks of frontal lobe since it does have relation with the working memory and attention but due to several mental distractions and lack of attention, it is not possible to be able to perform with complete attention. The performance lack because of mental distractions and attention could be improved with meditation. The ability to solve the problems increases because of attention. So, we can compare the EEG signals before and after meditation while solving the problems and show that the cognitive workload gets reduced after meditation with the help of extracted features.

Keywords: cognitive workload, EEG signal, PFC, DWT, Entropy.

Cite this Article

Mohammadi B. Quazi, Ifrah Khanam, Anees F. Quazi. Analysis of impact of Meditation on Cognitive Workload using EEG Signals. Current Trends in Signal Processing. 2020; 10(1): 29–39p.