基于改进EEGNet的n-back任务脑电信号识别

EEG Signal Recognition for n-back Task Based on Improved EEGNet

在人机交互的过程中,脑力负荷过高是产生操作错误的重要因素,现阶段基于脑电信号具有时间分辨率高和便携性好的特点,常用于脑力负荷的评估.近几年来深度学习的快速发展也使得其广泛应用在脑电领域并取得了比传统的机器学习更加优异的效果, n-back任务可通过设定不同的n值来诱发不同程度的脑力负荷.由此设计了基于视觉和听觉的n-back的范式来避免维度单一,同时还提出一种新的卷积神经网络模型,使用64通道的eego脑电设备采集数据经eeglab预处理后用于该模型的训练.在测试集上与EEGNet, FBCNet, ShallowConNet的性能进行对比,其提出的新模型在分类准确率有较为明显的提升,使得该研究在脑力负荷的评估尤其在多维度n-back任务的分类上具有一定应用潜力.

During human-computer interaction,excessive mental workload is an important factor to produce operation errors.At present,EEG signals are often employed for the evaluation of mental workload based on their characteristics of high time resolution and good portability.In recent years,the rapid development of deep learning leads to its widespread application in brain electricity and better results are yielded than traditional machine learning.The n-back task can induce different degrees of psychological loads by setting different n values.In this study,the n-back paradigm based on vision and hearing is designed to avoid a single dimension.Additionally,a new convolutional neural network model is proposed.The data collected by 64-channel eego EEG equipment are preprocessed by eeglab for the training of the model.Compared with the performance of EEGNet,FBCNet,and ShallowConvNet in the test set,the classification accuracy of the proposed model is significantly improved,and thus this study has certain application potential in the evaluation of mental workload,especially in the classification of multi-dimensional n-back tasks.