GR-307 EEG classification using Neural Network – An Application of Machine Learning in Classification of attention deficiency, to measure the effect of ChakraMarmaKosha Meditation-II

GR-307 EEG classification using Neural Network – An Application of Machine Learning in Classification of attention deficiency, to measure the effect of ChakraMarmaKosha Meditation-II

Stress reduces attention span and is a common problem that impacts students’ academic performance as well as their self-efficacy in handling challenging situations. Meditation techniques have been proven to help manage stress levels. In the previous research, the author used Heart Coherence as the metric to show the impact of ChakraMarmaKosha Meditation (CM), a meditation on human energy centers, on reducing the stress level. In this research we apply a new version of CM which is CM-II as a guided psychotherapy and cognitive therapy meditation, to analyze its impact on reducing attention deficiency among students. This study uses Electroencephalography (EEG) data as a metric to analyze electrical activities of the brain that contribute to attention deficiency. We use a neural network as a machine learning classifying algorithm to analyze the EEG data to measure the impact of CM-II on students’ attention deficiency.