Imagery-based brain-computer interfaces (BCIs) aim to decode different neural activities into control signals by identifying and classifying various natural commands from electroencephalogram (EEG) patterns and then control corresponding equipment. However, several traditional BCI recognition algorithms have the “one person, one model” issue, where the convergence of the recognition model’s training process is complicated. In this study, a new BCI model with a Dense long short-term memory (Dense-LSTM) algorithm is proposed, which combines the event-related desynchronization (ERD) and the event-related synchronization (ERS) of the imagery-based BCI; model training and testing were conducted with its own data set. Furthermore, a new experimental platform was built to decode the neural activity of different subjects in a static state. Experimental evaluation of the proposed recognition algorithm presents an accuracy of 91.56%, which resolves the “one person one model” issue along with the difficulty of convergence in the training process.

中文翻译：

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增强基于图像的脑机接口的密集短期记忆模型

基于图像的脑计算机接口（BCI）旨在通过从脑电图（EEG）模式中识别和分类各种自然命令，然后将不同的神经活动解码为控制信号，然后控制相应的设备。然而，几种传统的BCI识别算法都存在“一个人，一个模型”的问题，识别模型的训练过程的收敛很复杂。在这项研究中，提出了一种新的BCI模型，该模型具有密集长短期记忆（Dense-LSTM）算法，该模型结合了基于图像的BCI的事件相关去同步（ERD）和事件相关同步（ERS） ; 使用自己的数据集进行模型训练和测试。此外，建立了一个新的实验平台来解码静态下不同对象的神经活动。