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Article

Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer’s Disease Subjects

1
Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania
2
Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Academic Editors: Piotr Wąż, Dorota Bielińska-Wąż and Katarzyna Zorena
Life 2022, 12(3), 374; https://doi.org/10.3390/life12030374
Received: 11 February 2022 / Revised: 28 February 2022 / Accepted: 2 March 2022 / Published: 4 March 2022
Visual perception is an important part of human life. In the context of facial recognition, it allows us to distinguish between emotions and important facial features that distinguish one person from another. However, subjects suffering from memory loss face significant facial processing problems. If the perception of facial features is affected by memory impairment, then it is possible to classify visual stimuli using brain activity data from the visual processing regions of the brain. This study differentiates the aspects of familiarity and emotion by the inversion effect of the face and uses convolutional neural network (CNN) models (EEGNet, EEGNet SSVEP (steady-state visual evoked potentials), and DeepConvNet) to learn discriminative features from raw electroencephalography (EEG) signals. Due to the limited number of available EEG data samples, Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) are introduced to generate synthetic EEG signals. The generated data are used to pretrain the models, and the learned weights are initialized to train them on the real EEG data. We investigate minor facial characteristics in brain signals and the ability of deep CNN models to learn them. The effect of face inversion was studied, and it was observed that the N170 component has a considerable and sustained delay. As a result, emotional and familiarity stimuli were divided into two categories based on the posture of the face. The categories of upright and inverted stimuli have the smallest incidences of confusion. The model’s ability to learn the face-inversion effect is demonstrated once more. View Full-Text
Keywords: Alzheimer’s disease; electroencephalogram; SSVEP; visual stimuli classification; face inversion; generative adversarial networks; data augmentation; deep learning Alzheimer’s disease; electroencephalogram; SSVEP; visual stimuli classification; face inversion; generative adversarial networks; data augmentation; deep learning
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MDPI and ACS Style

Komolovaitė, D.; Maskeliūnas, R.; Damaševičius, R. Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer’s Disease Subjects. Life 2022, 12, 374. https://doi.org/10.3390/life12030374

AMA Style

Komolovaitė D, Maskeliūnas R, Damaševičius R. Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer’s Disease Subjects. Life. 2022; 12(3):374. https://doi.org/10.3390/life12030374

Chicago/Turabian Style

Komolovaitė, Dovilė, Rytis Maskeliūnas, and Robertas Damaševičius. 2022. "Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer’s Disease Subjects" Life 12, no. 3: 374. https://doi.org/10.3390/life12030374

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