ECA-FusionNet: a hybrid EEG-fNIRS signals network for MI classification.

Yuxin Qin, Baojiang Li, Wenlong Wang, Xingbin Shi, Cheng Peng, Xichao Wang, Haiyan Wang
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Abstract

Objective. Among all BCI paradigms, motion imagery (MI) has gained favor among researchers because it allows users to control external devices by imagining movements rather than actually performing actions. This property holds important promise for clinical applications, especially in areas such as stroke rehabilitation. Electroencephalogram (EEG) signals and functional near-infrared spectroscopy (fNIRS) signals are two of the more popular neuroimaging techniques for obtaining MI signals from the brain. However, the performance of MI-based unimodal classification methods is low due to the limitations of EEG or fNIRS.Approach. In this paper, we propose a new multimodal fusion classification method capable of combining the potential complementary advantages of EEG and fNIRS. First, we propose a feature extraction network capable of extracting spatio-temporal features from EEG-based and fNIRS-based MI signals. Then, we successively fused the EEG and fNIRS at the feature-level and the decision-level to improve the adaptability and robustness of the model.Main results. We validate the performance of ECA-FusionNet on a publicly available EEG-fNIRS dataset. The results show that ECA-FusionNet outperforms unimodal classification methods, as well as existing fusion classification methods, in terms of classification accuracy for MI.Significance. ECA-FusionNet may provide a useful reference for the field of multimodal fusion classification.

ECA-FusionNet:用于MI分类的混合EEG-fNIRS信号网络。
目的:在所有脑机接口范式中,运动想象(MI)因其允许用户通过想象运动而不是实际执行动作来控制外部设备而受到研究人员的青睐。这一特性对临床应用具有重要的前景,特别是在中风康复等领域。脑电图(EEG)信号和功能近红外光谱(fNIRS)信号是两种比较流行的神经成像技术,用于从大脑获得MI信号。然而,由于脑电或近红外光谱的限制,基于mi的单峰分类方法的性能较低。方法:本文提出了一种新的多模态融合分类方法,该方法能够将EEG和fNIRS的潜在互补优势结合起来。首先,我们提出了一个特征提取网络,能够从基于eeg和基于fnir的MI信号中提取时空特征。然后,我们分别在特征级和决策级对EEG和fNIRS进行融合,以提高模型的自适应性和鲁棒性。主要结果:我们在公开可用的EEG-fNIRS数据集上验证了ECA-FusionNet的性能。结果表明,在mi分类精度方面,ECA-FusionNet优于单模态分类方法以及现有的融合分类方法。意义:ECA-FusionNet可为多模态融合分类领域提供有益的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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