Department of Computer Engineering, Ajou University, Suwon 16499, Korea.
Department of Digital Media, Ajou University, Suwon 16499, Korea.
Sensors (Basel). 2020 Dec 16;20(24):7212. doi: 10.3390/s20247212.
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.
随着阿尔茨海默病（AD）患者人数的增加，照顾这些患者所需的精力也在增加。与此同时，信息和传感器技术的进步降低了护理成本，为开发 AD 患者的医疗保健服务提供了潜在途径。例如，如果虚拟现实（VR）系统能够提供情感自适应内容，那么 AD 患者与 VR 内容交互的时间预计将延长，从而使护理人员能够专注于其他任务。作为实现这一目标的第一步，在本研究中，我们开发了一种分类模型，用于检测 AD 患者的情绪（例如，快乐、平静或无聊）。我们首先从一家医疗康复中心的 30 名韩国女性 AD 患者那里收集了脑电图（EEG）数据，这些患者观看了引发情绪的视频。我们应用了传统的机器学习算法，如多层感知机（MLP）和支持向量机，以及递归神经网络（RNN）架构的深度学习模型。MLP 取得了最佳性能，平均准确率为 70.97%；RNN 模型的准确率仅为 48.18%。我们的研究结果为基于脑电图的神经障碍患者情感检测领域开辟了新的研究途径。
