Electroencephalography based detection of cognitive state during learning tasks: An extensive approach

Written by Theparambil Asharaf Suhail, Kottanayil Pally Indiradevi, Ekkarakkudy Makkar Suhara, Poovathinal Azhakan Suresh, Ayyappan Anitha on . Posted in Volume XXV, Nr 2


Theparambil Asharaf Suhail1*, Kottanayil Pally Indiradevi2, Ekkarakkudy Makkar Suhara3, Poovathinal Azhakan Suresh4, Ayyappan Anitha5

1Department of ECE, Government Engineering College/ APJ Abdul Kalam Technological University, Kerala, India
2Principal MGM College of Engineering and Pharmaceutical Sciences Malappuram, Kerala, India
3Department of EEE, Government Engineering College, Thrissur, Kerala, India
4Department of Neurology, Valiyath Institute of Medical Sciences, Kollam, Kerala, India
5Department of Neurogenetics, Institute for Communicative and Cognitive Neurosciences, Shoranur, Kerala, India


Detecting cognitive states during learning tasks is an essential component in neurocognitive experiments for assessing and enhancing the cognitive performance of individuals. Studies have demonstrated that mental state recognition systems utilizing brain signals are proficient in the automated monitoring of learners’ cognitive states. The current study focuses on developing an efficient individualized and cross-subject cognitive state assessment model based on Electroencephalography (EEG) patterns during learning tasks. For this study, EEGs of 20 healthy subjects were recorded during a resting state followed by a learning task and examined EEG activations patterns in a wide perspective of feature types and rhythms. The extracted features included time-domain features such as Hjorth parameters, Wavelet-based features, and Spectral entropy. Three classifiers, Support Vector Machine, k-Nearest Neighbor, and Linear Discriminant Analysis were employed to recognize the mental state. A new EEG-based attention index using band ratios is proposed and is demonstrated as an effective predictor for recognizing attentive reading. The proposed model can yield recognition performance with an accuracy of 92.9% in the subject-dependent approach and 77.2% in the subject-independent approach with the Support Vector Machine Classifier. The findings are useful for the design and development of neurofeedback systems that monitor and enhance the cognitive performance in healthy individuals, as well as in individuals with cognitive deficits.

Keywords: Wavelet Transform, EEG Band ratio, Attention Index, Neurofeedback Training, Spectral Entropy, Support Vector Machine



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