EEG-CDILNet: a lightweight and accurate CNN network using circular dilated convolution for motor imagery classification.

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-08-21 DOI:10.1088/1741-2552/acee1f
Tie Liang, Xionghui Yu, Xiaoguang Liu, Hongrui Wang, Xiuling Liu, Bin Dong
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Abstract

Objective.The combination of the motor imagery (MI) electroencephalography (EEG) signals and deep learning-based methods is an effective way to improve MI classification accuracy. However, deep learning-based methods often need too many trainable parameters. As a result, the trade-off between the network decoding performance and computational cost has always been an important challenge in the MI classification research.Approach.In the present study, we proposed a new end-to-end convolutional neural network (CNN) model called the EEG-circular dilated convolution (CDIL) network, which takes into account both the lightweight model and the classification accuracy. Specifically, the depth-separable convolution was used to reduce the number of network parameters and extract the temporal and spatial features from the EEG signals. CDIL was used to extract the time-varying deep features that were generated in the previous stage. Finally, we combined the features extracted from the two stages and used the global average pooling to further reduce the number of parameters, in order to achieve an accurate MI classification. The performance of the proposed model was verified using three publicly available datasets.Main results.The proposed model achieved an average classification accuracy of 79.63% and 94.53% for the BCIIV2a and HGD four-classification task, respectively, and 87.82% for the BCIIV2b two-classification task. In particular, by comparing the number of parameters, computation and classification accuracy with other lightweight models, it was confirmed that the proposed model achieved a better balance between the decoding performance and computational cost. Furthermore, the structural feasibility of the proposed model was confirmed by ablation experiments and feature visualization.Significance.The results indicated that the proposed CNN model presented high classification accuracy with less computing resources, and can be applied in the MI classification research.

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EEG-CDILNet:一个轻量级且精确的CNN网络,使用圆形扩张卷积进行运动图像分类。
目标。将运动图像(MI)脑电图信号与基于深度学习的方法相结合是提高MI分类准确率的有效途径。然而,基于深度学习的方法通常需要太多的可训练参数。因此,在网络解码性能和计算成本之间的权衡一直是MI分类研究中的一个重要挑战。在本研究中,我们提出了一种新的端到端卷积神经网络(CNN)模型,称为EEG-circular dilated convolution (CDIL)网络,它同时考虑了模型的轻量化和分类精度。具体来说,利用深度可分卷积减少网络参数的数量,提取脑电信号的时空特征。CDIL用于提取前一阶段生成的时变深度特征。最后,我们将两个阶段提取的特征结合起来,使用全局平均池化进一步减少参数数量,以实现准确的MI分类。使用三个公开可用的数据集验证了所提出模型的性能。主要的结果。该模型对BCIIV2a和HGD四分类任务的平均分类准确率分别为79.63%和94.53%,对BCIIV2b两分类任务的平均分类准确率为87.82%。特别是,通过与其他轻量级模型的参数数量、计算量和分类精度的比较,证实了该模型在解码性能和计算成本之间取得了更好的平衡。结果表明,本文提出的CNN模型具有较高的分类精度和较少的计算资源,可以应用于MI分类研究。
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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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