Ketu Shwet, Mishra Pramod Kumar
Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, India.
Cogn Neurodyn. 2022 Feb;16(1):73-90. doi: 10.1007/s11571-021-09678-x. Epub 2021 Apr 17.
The electroencephalography (EEG) signal is an essential source of Brain-Computer Interface (BCI) technology implementation. The BCI is nothing but a non-muscle communication medium among the external devices and the brain. The basic concept of BCI is to enable the interaction among the neurological ill patients to others with the help of brain signals. EEG signal classification is an essential requirement for various applications such as motor imagery classification, drug effects diagnosis, emotion classification, seizure prediction/detection, eye state prediction/detection, and so on. Thus, there is a need for an efficient classification model that can deal with the EEG datasets more adequately with better classification accuracy, which will further help in developing the automatic solution for the medical domain. In this paper, we have introduced a hybrid classification model for eye state detection using electroencephalogram (EEG) signals. This hybrid classification model has been evaluated with the other traditional machine learning models, eight classification models (Prepossessed + Hypertuned) and six state-of-the-art methods to assess its appropriateness and correctness. This proposed classification model establishes a machine learning-based hybrid model for the classification of eye state using EEG signals with greater exactness. It is also capable of solving the issue of outlier detection and removal to address the class imbalance problem, which will offer the solution toward building the robotic or smart machine-based solution for social well-being.
脑电图（EEG）信号是脑机接口（BCI）技术实现的重要来源。BCI不过是外部设备与大脑之间的一种非肌肉通信媒介。BCI的基本概念是借助脑信号使神经疾病患者与他人进行互动。EEG信号分类是运动想象分类、药物效果诊断、情绪分类、癫痫预测/检测、眼睛状态预测/检测等各种应用的基本要求。因此，需要一种高效的分类模型，能够更充分地处理EEG数据集并具有更高的分类准确率，这将有助于进一步开发医疗领域的自动解决方案。在本文中，我们介绍了一种使用脑电图（EEG）信号进行眼睛状态检测的混合分类模型。该混合分类模型已与其他传统机器学习模型、八个分类模型（预处理+超参数调整）和六种先进方法进行了评估，以评估其适用性和正确性。所提出的分类模型建立了一种基于机器学习的混合模型，用于使用EEG信号更准确地对眼睛状态进行分类。它还能够解决异常值检测和去除问题，以解决类不平衡问题，这将为构建基于机器人或智能机器的社会福祉解决方案提供思路。
