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Open AccessArticle

EEG-Based Emotion Classification for Alzheimer’s Disease Patients Using Conventional Machine Learning and Recurrent Neural Network Models

1
Department of Computer Engineering, Ajou University, Suwon 16499, Korea
2
Department of Digital Media, Ajou University, Suwon 16499, Korea
3
Department of Artificial Intelligence, Ajou University, Suwon 16499, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(24), 7212; https://doi.org/10.3390/s20247212
Received: 29 September 2020 / Revised: 8 December 2020 / Accepted: 12 December 2020 / Published: 16 December 2020
(This article belongs to the Special Issue Machine Learning for Sensing and Healthcare 2020–2021)
As the number of patients with Alzheimer’s disease (AD) increases, the effort needed to care for these patients increases as well. At the same time, advances in information and sensor technologies have reduced caring costs, providing a potential pathway for developing healthcare services for AD patients. For instance, if a virtual reality (VR) system can provide emotion-adaptive content, the time that AD patients spend interacting with VR content is expected to be extended, allowing caregivers to focus on other tasks. As the first step towards this goal, in this study, we develop a classification model that detects AD patients’ emotions (e.g., happy, peaceful, or bored). We first collected electroencephalography (EEG) data from 30 Korean female AD patients who watched emotion-evoking videos at a medical rehabilitation center. We applied conventional machine learning algorithms, such as a multilayer perceptron (MLP) and support vector machine, along with deep learning models of recurrent neural network (RNN) architectures. The best performance was obtained from MLP, which achieved an average accuracy of 70.97%; the RNN model’s accuracy reached only 48.18%. Our study results open a new stream of research in the field of EEG-based emotion detection for patients with neurological disorders. View Full-Text
Keywords: dementia; Alzheimer’s disease; EEG; sensor; machine learning; deep learning; emotion; classification dementia; Alzheimer’s disease; EEG; sensor; machine learning; deep learning; emotion; classification
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MDPI and ACS Style

Seo, J.; Laine, T.H.; Oh, G.; Sohn, K.-A. EEG-Based Emotion Classification for Alzheimer’s Disease Patients Using Conventional Machine Learning and Recurrent Neural Network Models. Sensors 2020, 20, 7212.

AMA Style

Seo J, Laine TH, Oh G, Sohn K-A. EEG-Based Emotion Classification for Alzheimer’s Disease Patients Using Conventional Machine Learning and Recurrent Neural Network Models. Sensors. 2020; 20(24):7212.

Chicago/Turabian Style

Seo, Jungryul; Laine, Teemu H.; Oh, Gyuhwan; Sohn, Kyung-Ah. 2020. "EEG-Based Emotion Classification for Alzheimer’s Disease Patients Using Conventional Machine Learning and Recurrent Neural Network Models" Sensors 20, no. 24: 7212.

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