A Novel Experimental Study to Enhance the Attentional State using EEG Signals

Bandaru, Jagadish and Rajalakshmi, P (2020) A Novel Experimental Study to Enhance the Attentional State using EEG Signals. In: 15th IEEE Sensors Applications Symposium, SAS 2020, 9 March 2020through 11 March 2020, Kuala Lumpur.

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In this paper, we propose a simple low-complex classification framework for the cognitive enhancement with the sustained attention stimuli using Electroencephalography (EEG) signals. The visual stimuli comprise of four face images: two happy (one male and one female) and two unhappy (one male and one female). The neuronal response is decoded using a combination of discrete wavelet transform (DWT) and ensemble classifier. The features are extracted by decomposition of recorded EEG signals using Daubechies wavelet filter (db4) and used the statistical methods such as the absolute mean value, power, and standard deviation for classification. The proposed methodology is validated on in-house recorded visual attention EEG (VA-EEG) dataset using six subjects (three males, three females) and evaluated the performance on six binary combinations of facial stimuli. The performance results show that the binary combination of male happy (MH) and female happy (FH) facial stimuli aids in cognitive enhancement for the people suffering from cognitive symptoms. The proposed low-complex feature extraction classification framework obtained a mean classification accuracy (CA) and a mean kappa value of 86.58% and 0.72, respectively. © 2020 IEEE.

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Item Type: Conference or Workshop Item (Paper)
Additional Information: ACKNOWLEDGMENT This work is partly supported by Visvesvaraya PhD Scheme, Media Lab Asia, MeitY, Govt. of India and partly funded by Indian Institute of Technology Hyderabad.
Uncontrolled Keywords: classification accuracy; discrete wavelet transform (DWT); Electroencephalography (EEG); ensemble classifier; kappa coefficient; visual attention
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 22 Oct 2022 09:25
Last Modified: 22 Oct 2022 09:25
URI: http://raiith.iith.ac.in/id/eprint/11029
Publisher URL: http://doi.org/10.1109/SAS48726.2020.9220056
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