基于多尺度特征提取与挤压激励模型的运动想象分类方法

Motor Imagery Classification Based on Multiscale Feature Extraction and Squeeze-Excitation Model

基于运动想象的脑机接口技术能够建立大脑与外界之间的联系,逐渐成为人机混合增强智能的重要应用,并广泛应用于医学康复治疗等领域.由于脑电信号具有非线性、非平稳和低信噪比等特点,使得准确的分类运动想象脑电信号具有很大挑战.为此,提出一种新颖的多尺度特征提取与挤压激励模型对运动想象脑电信号进行高精度分类.首先,基于多尺度卷积模块自动提取原始脑电信号的时域、频域和时频域特征;然后,使用残差模块和挤压激励模块分别进行特征的融合和选择;最后,利用全连接网络层进行运动想象脑电信号的分类.实验在2个公开的脑机接口竞赛数据集上进行分析,结果表明该模型与现有先进模型相比,有效地提升了运动想象脑电信号的识别效果,在2个数据集上分别取得了78.0%和82.5%的平均准确度,该模型可以在脑电通道较少的情况下有效地分类脑电信号且无需手动设计特征,具有较高的应用价值.

Brain-computer Interface(BCI)technology based on motor imagery(MI)can establish communication between the human brain and outside world.It has been widely used in medical rehabilitation and other fields.Owing to the characteristics of the motor imagery EEG signals,such as non-linear,non-stationary,and low signal-noise ratio,it is a huge challenge to classify motor imagery EEG signals accurately.Hence,we propose a novel multiscale feature extraction and squeeze-excitation model which is applied for the classification of motor imagery EEG signals.Firstly,the proposed deep learning module,which is based on multiscale structure,automatically extracts time domain features,frequency domain features and time-frequency domain features.Then,the residual module and squeeze-excitation module are applied for feature fusion and selection,respectively.Finally,fully connected network layers are used to classify motor imagery EEG signals.The proposed model is evaluated on two public BCI competition datasets.The results show that the proposed model can effectively improve the recognition performance of motor imagery EEG signals compared with the existing several state-of-the-art models.The average accuracy on the two datasets is 78.0%and 82.5%,respectively.Moreover,the proposed model has higher application value because it classifies motor imagery EEG signals efficiently without manual feature extraction when spatial information is insufficient.