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

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ID: 274308
2020
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Abstract
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.
Reference Key
seo2020sensorseeg-based Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jungryul Seo;Teemu H. Laine;Gyuhwan Oh;Kyung-Ah Sohn;Seo, Jungryul;Laine, Teemu H.;Oh, Gyuhwan;Sohn, Kyung-Ah;
Journal sensors
Year 2020
DOI
10.3390/s20247212
URL
https://doi.org/10.3390/s20247212
Keywords
Deep learning Machine learning dementia EEG classification emotion alzheimer’s disease sensor

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