Background and objective

Recognition of motor intention based on electroencephalogram (EEG) signals has attracted considerable research interest in the field of pattern recognition due to its notable application of non-muscular communication and control for those with severe motor disabilities. In analysis of EEG data, achieving a higher classification performance is dependent on the appropriate representation of EEG features which is mostly characterized by one unique frequency before applying a learning model. Neglecting other frequencies of EEG signals could deteriorate the recognition performance of the model because each frequency has its unique advantages. Motivated by this idea, we propose to obtain distinguishable features with different frequencies by introducing an integrated deep learning model to accurately classify multiple classes of upper limb movement intentions.

Methods

The proposed model is a combination of long short-term memory (LSTM) and stacked autoencoder (SAE). To validate the method, four high-level amputees were recruited to perform five motor intention tasks. The acquired EEG signals were first preprocessed before exploring the consequence of input representation on the performance of LSTM-SAE by feeding four frequency bands related to the tasks into the model. The learning model was further improved by t-distributed stochastic neighbor embedding (t-SNE) to eliminate feature redundancy, and to enhance the motor intention recognition.

Results

The experimental results of the classification performance showed that the proposed model achieves an average performance of 99.01% for accuracy, 99.10% for precision, 99.09% for recall, 99.09% for f1_score, 99.77% for specificity, and 99.0% for Cohen's kappa, across multi-subject and multi-class scenarios. Further evaluation with 2-dimensional t-SNE revealed that the signal decomposition has a distinct multi-class separability in the feature space.

Conclusion

This study demonstrated the predominance of the proposed model in its ability to accurately classify upper limb movements from multiple classes of EEG signals, and its potential application in the development of a more intuitive and naturalistic prosthetic control.

中文翻译：

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用于上肢截肢者多类脑电信号运动意图识别的集成深度学习模型

背景和目标

基于脑电图（EEG）信号的运动意图识别在模式识别领域引起了相当大的研究兴趣，这是由于其在严重运动障碍者中非肌肉通信和控制的显着应用。在EEG数据分析中，实现更高的分类性能取决于EEG特征的适当表示，该特征在应用学习模型之前通常以一个唯一的频率为特征。忽略其他频率的EEG信号可能会降低模型的识别性能，因为每个频率都有其独特的优势。受这个想法的激励，

方法

所提出的模型是长短期存储器（LSTM）和堆叠式自动编码器（SAE）的组合。为了验证该方法，招募了四名高级截肢者来执行五项运动意图任务。首先对获取的EEG信号进行预处理，然后通过将与任务相关的四个频段输入模型，以探索输入表示对LSTM-SAE性能的影响。通过t分布随机邻居嵌入（t-SNE）进一步改进了学习模型，以消除特征冗余并增强运动意图识别。

结果

分类性能的实验结果表明，在整个模型中，所提模型的平均性能达到了99.01％，精度为99.10％，召回率为99.09％，f1_score为99.09％，特异性为99.77％和科恩kappa为99.0％多主题和多类方案。二维t-SNE的进一步评估表明，信号分解在特征空间中具有明显的多类可分离性。

结论

这项研究证明了该模型的优势在于能够根据多类EEG信号准确地对上肢运动进行分类，并且在开发更直观，自然主义的义肢控制中具有潜在的应用价值。