High-Order Temporal Convolutional Network for Improving Classification Performance of SSVEP-EEG

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2024-03-09 DOI:10.1016/j.irbm.2024.100830
Jianli Yang, Songlei Zhao, Wei Zhang, Xiuling Liu
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引用次数: 0

Abstract

Background and objective

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) aim to detect target frequencies corresponding to specific commands in electroencephalographic (EEG) signals by classification algorithms to achieve the desired control. However, SSVEP signals suffer from low signal-to-noise ratio and large differences in brain activity. Moreover, the existing CNN models have small receptive fields, which make it difficult to receive large range of feature information and limit the effectiveness of classification algorithms.

Methods

To this end, we proposed a high-order temporal convolutional neural network (HOT-CNN) model for enhancing the performance of SSVEP target recognition. Specifically, the SSVEP-EEG signals was divided into equal-length time segments and a time-slice attention module was designed to capture the correlation between time slices. The module improves the local characterization of signals and reduces biological noise interference by automatically assigning high weights to locally relevant temporal sampling cues and lower weights to other temporal cues. Moreover, for global features, a temporal convolutional network module was designed to increases the receptive field of the network and to extract more comprehensive time domain features by using dilated causal convolution. Finally, the fusion and analysis of local and global features are achieved by designing a feature fusion and classification module to accomplish accurate classification of SSVEP signals.

Results

Our method was evaluated on large publicly available datasets containing 35 subjects and 40 categories. Experimental results indicated that HOT-CNN achieved encouraging performance compared with other advanced methods: the highest information transfer rate of 241.01bits/min was obtained using 0.5s stimuli, and the highest average accuracy of 96.39% was obtained using 1.0s stimuli.

Conclusions

The method effectively reinforced the global and local time-domain information and improved the classification performance of SSVEP, which has wide application prospects.

Abstract Image

用于提高 SSVEP-EEG 分类性能的高阶时空卷积网络
基于稳态视觉诱发电位(SSVEP)的脑机接口(BCI)旨在通过分类算法检测脑电信号中与特定指令相对应的目标频率,从而实现所需的控制。然而,SSVEP 信号存在信噪比低和大脑活动差异大的问题。此外,现有 CNN 模型的感受野较小,难以接收大范围的特征信息,限制了分类算法的有效性。为此,我们提出了一种高阶时空卷积神经网络(HOT-CNN)模型,以提高 SSVEP 目标识别的性能。具体来说,我们将 SSVEP-EEG 信号划分为等长的时间片段,并设计了一个时间片关注模块来捕捉时间片段之间的相关性。该模块通过自动为与局部相关的时间采样线索分配高权重,为其他时间线索分配低权重,从而改善信号的局部特征,减少生物噪声干扰。此外,针对全局特征,还设计了一个时间卷积网络模块,以增加网络的感受野,并通过使用扩张因果卷积提取更全面的时域特征。最后,通过设计一个特征融合和分类模块,实现了局部和全局特征的融合与分析,从而完成了对 SSVEP 信号的精确分类。我们的方法在包含 35 个受试者和 40 个类别的大型公开数据集上进行了评估。实验结果表明,与其他先进方法相比,HOT-CNN 取得了令人鼓舞的性能:使用 0.5 秒刺激时,信息传输率最高,达到 241.01bits/min;使用 1.0 秒刺激时,平均准确率最高,达到 96.39%。该方法有效地强化了全局和局部时域信息,提高了 SSVEP 的分类性能,具有广泛的应用前景。
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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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