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Article

EEG and Deep Learning Based Brain Cognitive Function Classification

1
Southwestern Educational Society, Mayaguez, PR 00680, USA
2
Department of Electrical and Computer Engineering, University of Puerto Rico, Mayaguez, PR 00681-9000, USA
*
Author to whom correspondence should be addressed.
Received: 13 October 2020 / Revised: 1 December 2020 / Accepted: 14 December 2020 / Published: 21 December 2020
(This article belongs to the Special Issue Feature Paper in Computers)
Electroencephalogram signals are used to assess neurodegenerative diseases and develop sophisticated brain machine interfaces for rehabilitation and gaming. Most of the applications use only motor imagery or evoked potentials. Here, a deep learning network based on a sensory motor paradigm (auditory, olfactory, movement, and motor-imagery) that employs a subject-agnostic Bidirectional Long Short-Term Memory (BLSTM) Network is developed to assess cognitive functions and identify its relationship with brain signal features, which is hypothesized to consistently indicate cognitive decline. Testing occurred with healthy subjects of age 20–40, 40–60, and >60, and mildly cognitive impaired subjects. Auditory and olfactory stimuli were presented to the subjects and the subjects imagined and conducted movement of each arm during which Electroencephalogram (EEG)/Electromyogram (EMG) signals were recorded. A deep BLSTM Neural Network is trained with Principal Component features from evoked signals and assesses their corresponding pathways. Wavelet analysis is used to decompose evoked signals and calculate the band power of component frequency bands. This deep learning system performs better than conventional deep neural networks in detecting MCI. Most features studied peaked at the age range 40–60 and were lower for the MCI group than for any other group tested. Detection accuracy of left-hand motor imagery signals best indicated cognitive aging (p = 0.0012); here, the mean classification accuracy per age group declined from 91.93% to 81.64%, and is 69.53% for MCI subjects. Motor-imagery-evoked band power, particularly in gamma bands, best indicated (p = 0.007) cognitive aging. Although the classification accuracy of the potentials effectively distinguished cognitive aging from MCI (p < 0.05), followed by gamma-band power. View Full-Text
Keywords: deep learning; auditory and olfactory sensory functions; motor imagery and movement function; electroencephalogram; deep neural network; mild cognitive impairment deep learning; auditory and olfactory sensory functions; motor imagery and movement function; electroencephalogram; deep neural network; mild cognitive impairment
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MDPI and ACS Style

Sridhar, S.; Manian, V. EEG and Deep Learning Based Brain Cognitive Function Classification. Computers 2020, 9, 104. https://0-doi-org.brum.beds.ac.uk/10.3390/computers9040104

AMA Style

Sridhar S, Manian V. EEG and Deep Learning Based Brain Cognitive Function Classification. Computers. 2020; 9(4):104. https://0-doi-org.brum.beds.ac.uk/10.3390/computers9040104

Chicago/Turabian Style

Sridhar, Saraswati, and Vidya Manian. 2020. "EEG and Deep Learning Based Brain Cognitive Function Classification" Computers 9, no. 4: 104. https://0-doi-org.brum.beds.ac.uk/10.3390/computers9040104

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