A new attention-based 3D densely connected cross-stage-partial network for motor imagery classification in BCI

作者：Yintang Wen, Wenjing He, Yuyan Zhang · 发表于：Journal of Neural Engineering · 年份：2022 · DOI：10.1088/1741-2552/ac93b4 · 被引用次数：10 · 研究领域：EEG and Brain-Computer Interfaces、Blind Source Separation Techniques、Advanced Memory and Neural Computing

Abstract Objective . The challenge for motor imagery (MI) in brain-computer interface (BCI) systems is finding a reliable classification model that has high classification accuracy and excellent robustness. Currently, one of the main problems leading to degraded classification performance is the inaccuracy caused by nonstationarities and low signal-to-noise ratio in electroencephalogram (EEG) signals. Approach . This study proposes a novel attention-based 3D densely connected cross-stage-partial network (DCSPNet) model to achieve efficient EEG-based MI classification. This is an end-to-end classification model framework based on the convolutional neural network (CNN) architecture. In this framework, to fully utilize the complementary features in each dimension, the optimal features are extracted adaptively from the EEG signals through the spatial-spectral-temporal (SST) attention mechanism. The 3D DCSPNet is introduced to reduce the gradient loss by segmenting the extracted feature maps to strengthen the network learning capability. Additionally, the design of the densely connected structure increases the robustness of the network. Main results . The performance of the proposed method was evaluated using the BCI competition IV 2a and the high gamma dataset, achieving an average accuracy of 84.45% and 97.88%, respectively. Our method outperformed most state-of-the-art classification algorithms, demonstrating its effectiveness and strong generalization ability. Significance. Th...