论文标题
基于单渠道脑电图
Light-Weight 1-D Convolutional Neural Network Architecture for Mental Task Identification and Classification Based on Single-Channel EEG
论文作者
论文摘要
实时使用单/有限通道（S）脑电图（EEG）信号的精神任务识别和分类在便携式脑部计算机界面（BCI）和Neurofeastback（NFB）系统的设计中起重要作用。但是，经过实时记录的EEG信号通常被诸如眼部伪像（OAS）和肌肉伪像（MAS）之类的噪音污染，这些噪声降低了从EEG信号中提取的手工制作的特征，从而导致标识不足和精神任务的分类。因此，我们研究了最近的深度学习技术的使用，这些技术不需要任何手动特征提取或伪影抑制步骤。在本文中，我们提出了一个轻巧的一维卷积神经网络（1D-CNN）架构，以进行心理任务识别和分类。使用无伪影和伪影污染的EEG信号评估所提出的体系结构的鲁棒性，从两个公开可用数据库（即Keirn和Aunon（$ k $）数据库）和EEGMAT（$ e $）数据库（$ e $）数据库（$ e $）数据库（$ e $）数据库（$ r $）录制的数据库（$ r $）仅使用单次录制的单台上的数据库（$ r $），精神/非精神二进制任务分类，但也不同的心理/心理多任务分类。评估结果表明，所提出的体系结构可用于$ 99.7 \％$和$ 100 \％$的最高主题分类精度，分别用于多级分类和数据库$ k $中的成对精神任务分类。此外，所提出的体系结构在数据库$ e $和录制的数据库$ r $中分别实现了$ 99 \％$和$ 98 \％$ $ 98 \％$的分类精度。比较性能分析表明，所提出的体系结构不仅在分类准确性方面，而且在针对工件的鲁棒性方面都优于现有方法。
Mental task identification and classification using single/limited channel(s) electroencephalogram (EEG) signals in real-time play an important role in the design of portable brain-computer interface (BCI) and neurofeedback (NFB) systems. However, the real-time recorded EEG signals are often contaminated with noises such as ocular artifacts (OAs) and muscle artifacts (MAs), which deteriorate the hand-crafted features extracted from EEG signal, resulting inadequate identification and classification of mental tasks. Therefore, we investigate the use of recent deep learning techniques which do not require any manual feature extraction or artifact suppression step. In this paper, we propose a light-weight one-dimensional convolutional neural network (1D-CNN) architecture for mental task identification and classification. The robustness of the proposed architecture is evaluated using artifact-free and artifact-contaminated EEG signals taken from two publicly available databases (i.e, Keirn and Aunon ($K$) database and EEGMAT ($E$) database) and in-house ($R$) database recorded using single-channel neurosky mindwave mobile 2 (MWM2) EEG headset in performing not only mental/non-mental binary task classification but also different mental/mental multi-tasks classification. Evaluation results demonstrate that the proposed architecture achieves the highest subject-independent classification accuracy of $99.7\%$ and $100\%$ for multi-class classification and pair-wise mental tasks classification respectively in database $K$. Further, the proposed architecture achieves subject-independent classification accuracy of $99\%$ and $98\%$ in database $E$ and the recorded database $R$ respectively. Comparative performance analysis demonstrates that the proposed architecture outperforms existing approaches not only in terms of classification accuracy but also in robustness against artifacts.
