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- W2912507295 abstract "Classification of resting and cognitive states has its importance in brain neuroscience for understating the underlying behaviors of cognition. The human brain is considered as a complex system having different mental states such as resting, active or cognitive states. It is a well-understood fact that the brain activity increases with the increased demand of cognition. In this paper, the cognitive and resting state classification based on EEG-based feature extraction and connectivity approaches are described. EEG-based connectivity approaches are a good discriminator for different mental states. EEG data were collected from 34 human participants at resting and during a learning state. After preprocessing, EEG-based feature extraction method and connectivity approach were implemented, and their results were classified. Results showed that the connectivity approach gave 79.90% accuracy while the highest accuracy achieved by feature extraction approach was 78.50%. It is concluded that EEG-based connectivity approach discriminates the resting and cognitive states more efficiently." @default.
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- W2912507295 date "2018-12-01" @default.
- W2912507295 modified "2023-10-16" @default.
- W2912507295 title "Classification of Resting and Cognitive States using EEG-based Feature Extraction and Connectivity Approach" @default.
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- W2912507295 doi "https://doi.org/10.1109/iecbes.2018.8626658" @default.
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