Kaur Rachneet, Korolkov Maxim, Hernandez Manuel E, Sowers Richard
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:95-98. doi: 10.1109/EMBC44109.2020.9175741.
Electroencephalography (EEG) is a commonly used method for monitoring brain activity. Automating an EEG signal processing pipeline is imperative to the exploration of real-time brain computer interface (BCI) applications. EEG analysis demands substantial training and time for removal of distinct unwanted independent components (ICs), generated via independent component analysis, corresponding to artifacts. The considerable subject-wise variations across these components motivates defining a procedural way to identify and eliminate these artifacts. We propose DeepIC-virtual, a convolutional neural network (CNN) deep learning classifier to automatically identify brain components in the ICs extracted from the subject's EEG data gathered while they are being immersed in a virtual reality (VR) environment. This work examined the feasibility of DL techniques to provide automated ICs classification on noisy and visually engaging upright stance EEG data. We collected the EEG data for six subjects while they were standing upright in a VR testing setup simulating pseudo-randomized variations in height and depth conditions and induced perturbations. An extensive 1432 IC representation images data set was generated and manually labelled via an expert as brain components or one of the six distinct removable artifacts. The supervised CNN architecture was utilized to categorize good brain ICs and bad artifactual ICs via generated images of topographical maps. Our model categorizing good versus bad IC topographical maps resulted in a binary classification accuracy and area under curve of 89.20% and 0.93 respectively. Despite significant imbalance, only 1 out of the 57 present brain ICs in the withheld testing set was miss-classified as an artifact. These results will hopefully encourage clinicians to integrate BCI methods and neurofeedback to control anxiety and provide a treatment of acrophobia, given the viability of automatic classification of artifactual ICs.
脑电图（EEG）是一种常用的监测大脑活动的方法。实现脑电图信号处理流程的自动化对于探索实时脑机接口（BCI）应用至关重要。脑电图分析需要大量的训练和时间来去除通过独立成分分析生成的、与伪迹相对应的不同的不需要的独立成分（IC）。这些成分在不同受试者之间存在相当大的差异，这促使我们定义一种程序方法来识别和消除这些伪迹。我们提出了DeepIC-virtual，一种卷积神经网络（CNN）深度学习分类器，用于自动识别从受试者在虚拟现实（VR）环境中沉浸时收集的脑电图数据中提取的IC中的大脑成分。这项工作研究了深度学习技术在对嘈杂且视觉上引人入胜的直立姿势脑电图数据进行自动IC分类方面的可行性。我们收集了六名受试者在VR测试装置中直立站立时的脑电图数据，该装置模拟了高度和深度条件的伪随机变化并引入了干扰。生成了一个包含1432个IC表示图像的广泛数据集，并由专家手动标记为大脑成分或六种不同的可去除伪迹之一。利用监督式CNN架构通过生成的地形图图像对良好的大脑IC和不良的伪迹IC进行分类。我们对良好与不良IC地形图进行分类的模型的二元分类准确率和曲线下面积分别为89.20%和0.93。尽管存在显著的不平衡，但在保留的测试集中，57个现有的大脑IC中只有1个被误分类为伪迹。鉴于伪迹IC自动分类的可行性，这些结果有望鼓励临床医生整合BCI方法和神经反馈来控制焦虑并治疗恐高症。
