Objective. Most current methods of classifying different patterns for motor imagery EEG signals require complex pre-processing and feature extraction steps, which consume time and lack adaptability, ignoring individual differences in EEG signals. It is essential to improve algorithm performance with the increased classes and diversity of subjects. Approach. This study introduces deep learning method for end-to-end learning to complete the classification of four-class MI tasks, aiming to improve the recognition rate and balance the classification accuracy among different subjects. A new one-dimensional input data representation method is proposed. This representation method can increase the number of samples and ignore the influence of channel correlation. In addition, a cascade network of convolutional neural network and gated recurrent unit is designed to learn time-frequency information from EEG data without extracting features manually, this model can capture the hidden representations related to different MI mode of each people. Main results. Experiments on BCI Competition 2a dataset and actual collected dataset achieve high accuracy near 99.40% and 92.56%, and the standard deviation is 0.34 and 1.35 respectively. Results demonstrate that the proposed method outperforms the advanced methods and baseline models. Significance. Experimental results show that the proposed method improves the accuracy of multi-classification and overcomes the impact of individual differences on classification by training neural network subject-dependent, which promotes the development of actual brain-computer interface systems.

客观的。目前大多数对运动想象脑电信号进行不同模式分类的方法都需要复杂的预处理和特征提取步骤，这些步骤耗时且缺乏适应性，忽略了脑电信号的个体差异。随着科目类别和多样性的增加，提高算法性能至关重要。方法。本研究引入深度学习方法进行端到端学习，完成四类MI任务的分类，旨在提高识别率，平衡不同学科间的分类准确率。提出了一种新的一维输入数据表示方法。这种表示方法可以增加样本数，忽略信道相关性的影响。此外，卷积神经网络和门循环单元的级联网络被设计用于从脑电数据中学习时频信息，而无需手动提取特征，该模型可以捕获与每个人的不同 MI 模式相关的隐藏表示。主要结果. 在 BCI Competition 2a 数据集和实际采集的数据集上的实验达到了接近 99.40% 和 92.56% 的高精度，标准差分别为 0.34 和 1.35。结果表明，所提出的方法优于先进的方法和基线模型。意义。实验结果表明，该方法通过训练神经网络学科依赖，提高了多分类的准确率，克服了个体差异对分类的影响，促进了实际脑机接口系统的发展。