PDF(Pubmed)
Abstract：
UNASSIGNED: During electroencephalography (EEG)-based motor imagery-brain-computer interfaces (MI-BCIs) task, a large number of electrodes are commonly used, and consume much computational resources. Therefore, channel selection is crucial while ensuring classification accuracy.
UNASSIGNED: This paper proposes a channel selection method by integrating the efficient channel attention (ECA) module with a convolutional neural network (CNN). During model training process, the ECA module automatically assigns the channel weights by evaluating the relative importance for BCI classification accuracy of every channel. Then a ranking of EEG channel importance can be established so as to select an appropriate number of channels to form a channel subset from the ranking. In this paper, the ECA module is embedded into a commonly used network for MI, and comparative experiments are conducted on the BCI Competition IV dataset 2a.
UNASSIGNED: The proposed method achieved an average accuracy of 75.76% with all 22 channels and 69.52% with eight channels in a four-class classification task, outperforming other state-of-the-art EEG channel selection methods. The result demonstrates that the proposed method provides an effective channel selection approach for EEG-based MI-BCI.
UNASSIGNED: This paper proposes a channel selection method by integrating the efficient channel attention (ECA) module with a convolutional neural network (CNN). During model training process, the ECA module automatically assigns the channel weights by evaluating the relative importance for BCI classification accuracy of every channel. Then a ranking of EEG channel importance can be established so as to select an appropriate number of channels to form a channel subset from the ranking. In this paper, the ECA module is embedded into a commonly used network for MI, and comparative experiments are conducted on the BCI Competition IV dataset 2a.
UNASSIGNED: The proposed method achieved an average accuracy of 75.76% with all 22 channels and 69.52% with eight channels in a four-class classification task, outperforming other state-of-the-art EEG channel selection methods. The result demonstrates that the proposed method provides an effective channel selection approach for EEG-based MI-BCI.
摘要：
■在基于脑电图（EEG）的运动图像-脑机接口（MI-BCI）任务期间，通常使用大量的电极，并消耗大量的计算资源。因此,通道选择在确保分类准确性的同时至关重要。
■本文通过将有效的信道注意（ECA）模块与卷积神经网络（CNN）集成，提出了一种信道选择方法。在模型训练过程中，ECA模块通过评估每个通道的BCI分类精度的相对重要性来自动分配通道权重。然后,可以建立EEG通道重要性的排序,以便从排序中选择适当数量的通道以形成通道子集。在本文中,ECA模块嵌入到常用的MI网络中，在BCI竞争IV数据集2a上进行比较实验。
■所提出的方法在四类分类任务中，所有22个通道的平均精度为75.76％，在八个通道的平均精度为69.52％，优于其他最先进的EEG通道选择方法。结果表明，该方法为基于脑电的MI-BCI提供了一种有效的通道选择方法。
■本文通过将有效的信道注意（ECA）模块与卷积神经网络（CNN）集成，提出了一种信道选择方法。在模型训练过程中，ECA模块通过评估每个通道的BCI分类精度的相对重要性来自动分配通道权重。然后,可以建立EEG通道重要性的排序,以便从排序中选择适当数量的通道以形成通道子集。在本文中,ECA模块嵌入到常用的MI网络中，在BCI竞争IV数据集2a上进行比较实验。
■所提出的方法在四类分类任务中，所有22个通道的平均精度为75.76％，在八个通道的平均精度为69.52％，优于其他最先进的EEG通道选择方法。结果表明，该方法为基于脑电的MI-BCI提供了一种有效的通道选择方法。
