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

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.

Data Collection Framework for Context-Aware Virtual Reality Application Development in Unity: Case of Avatar Embodiment

“…To the best of our knowledge, this study marks the first occasion of proposing a reusable context data collection framework for the purpose of context-aware VR application development. The previous studies on context-aware VR applications that we reviewed [2,5,7,8,16,17] utilized systems that were built for a purpose specific to the research objective. Additionally, these studies did not report any reusable and extensible method or technique for acquiring rich context data that could be then used for context-aware application development.…”

“…As another example, collected context data could be used to develop a measurement of the user's empathy expression level by analyzing cues such as eye contact, leaning forward, body orientation, and distance [32]. Similarly, by detecting emotions from context data, we can develop VR systems that respond to the user's emotional state [5]. Moreover, centralized collection and storage of context data is also useful for researchers who wish to understand the user experience or usability of a VR experience [33].…”

“…A common approach is classifying the user's context using sensor data captured from the user. For instance, Seo et al developed emotion classification models using the user's electroencephalogram (EEG) data and machine learning classifiers to be applied to VR contents for Alzheimer's disease patients [5]. Seo et al's study used only one sensor device; however, a key aspect of context-aware VR systems is to support data collection from diverse sensor sources because this way the system can be more aware of the user's context [6].…”

An EEG-based systematic explainable detection framework for probing and localizing abnormal patterns in Alzheimer’s disease

“…We reproduced the state-of-the-art algorithms to automatically detect AD patterns proposed in recent articles [81][82][83][84]. For those methods, the configurations clearly stated in the original content were set according to their papers, the flexible parameters were investigated to obtain the best performance on our data.…”

Deep learning-based EEG emotion recognition: Current trends and future perspectives

“…From the perspective of application prospects, EEG-based emotion recognition technology has penetrated into various fields, including medical, education, entertainment, shopping, military, social, and safe driving (Suhaimi et al, 2020). In the medical field, timely acquisition of patients' EEG signals and rapid analysis of their emotional state can help doctors and nurses to accurately understand the patients' psychological state and then make reasonable medical decisions, which has an important effect on the rehabilitation of some people with mental disorders, such as autism (Mehdizadehfar et al, 2020;Mayor-Torres et al, 2021;Ji et al, 2022), depression (Cai et al, 2020;Chen X. et al, 2021), Alzheimer's disease (Güntekin et al, 2019;Seo et al, 2020), and physical disabilities (Chakladar and Chakraborty, 2018). In terms of education, the emotion recognition technology based on EEG signals can enable teaching staff to adjust teaching methods and teaching attitudes in a timely manner in accordance with the emotional performance of different trainees in class, such as increasing or reducing the workload (Menezes et al, 2017).…”