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In this paper, the effect of meditation on the seven load arithmetic tasks using Electroencephalogram (EEG) is investigated. The focused attention on breathing is taught to 11 healthy subjects as a simple meditation technique. EEG signals have been recorded at the beginning of meditation and after four weeks of regular meditation (daily 20 minutes) using EMOTIV device. The higher order crossings (HOC) features are derived from EEG signals for analysis of seven cognitive loads. The pre and post-experiment results are compared based on reaction time and effect on brain lobe. The K-Nearest Neighbors (K-NN) classifier is used for classification of seven loads and achieved 83.57% accuracy in the post and 89.61 % accuracy in pre-experiment. The EEG-based cognitive load analysis is an important tool for continuous memory load measurement application.

Analysis of effect of Meditation on Cognitive Load using Higher Order Crossing Features