An approach to estimate cognitive state with the impact of listening music on brain activity

This paper presents an approach to estimate the cognitive states depending on the variations of different effective frequency bands of EEG signal with listening music. In this work, EEG signals are collected for different types of songs to analyze the musical activity on human brain. Spatial and temporal analysis are applied to the brain signals to extract the salient features to estimate the human cognitive state. The classification accuracy of different frequency bands indicates the larger alpha or beta activity on human brain for the selected songs. The highest classification accuracy for soft song was found for θ-band which indicates the state of catnap. For medium and fast song the effectiveness of α band and β band are noteworthy, which indicate the state of relax and stress respectively. Therefore, our proposed approach is innovative and efficient to determine the human cognitive states.