Centro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN), Avenida Juan de Dios Bátiz Esquina Miguel Othón de Mendizábal Colonia Nueva Industrial Vallejo, Alcaldía Gustavo A. Madero, Ciudad de Mexico C.P. 07738, Mexico.
Institut Supérieur Pédagogique Technique de Kinshasa (I.S.P.T.-KIN), Av. de la Science 5, Gombe, Kinshasa 3287, Democratic Republic of the Congo.
Sensors (Basel). 2023 Apr 21;23(8):4164. doi: 10.3390/s23084164.
Nowadays, Brain-Computer Interfaces (BCIs) still captivate large interest because of multiple advantages offered in numerous domains, explicitly assisting people with motor disabilities in communicating with the surrounding environment. However, challenges of portability, instantaneous processing time, and accurate data processing remain for numerous BCI system setups. This work implements an embedded multi-tasks classifier based on motor imagery using the EEGNet network integrated into the NVIDIA Jetson TX2 card. Therefore, two strategies are developed to select the most discriminant channels. The former uses the accuracy based-classifier criterion, while the latter evaluates electrode mutual information to form discriminant channel subsets. Next, the EEGNet network is implemented to classify discriminant channel signals. Additionally, a cyclic learning algorithm is implemented at the software level to accelerate the model learning convergence and fully profit from the NJT2 hardware resources. Finally, motor imagery Electroencephalogram (EEG) signals provided by HaLT's public benchmark were used, in addition to the k-fold cross-validation method. Average accuracies of 83.7% and 81.3% were achieved by classifying EEG signals per subject and motor imagery task, respectively. Each task was processed with an average latency of 48.7 ms. This framework offers an alternative for online EEG-BCI systems' requirements, dealing with short processing times and reliable classification accuracy.
如今，脑机接口 (BCI) 由于在众多领域提供的多种优势，仍然引起了广泛的兴趣，特别是有助于运动障碍者与周围环境进行交流。然而，对于许多 BCI 系统设置来说，便携性、即时处理时间和准确的数据处理仍然存在挑战。
这项工作基于运动想象，使用集成到 NVIDIA Jetson TX2 卡中的 EEGNet 网络，实现了基于嵌入式的多任务分类器。因此，开发了两种策略来选择最具判别力的通道。前者使用基于准确性的分类器准则，而后者则评估电极互信息以形成判别通道子集。接下来，实施 EEGNet 网络对判别通道信号进行分类。此外，在软件级别实现了循环学习算法，以加速模型学习收敛并充分利用 NJT2 硬件资源。
最后，使用 HaLT 的公共基准提供的运动想象脑电图 (EEG) 信号，并采用 k 折交叉验证方法。对每个受试者和运动想象任务的 EEG 信号进行分类，分别达到了 83.7%和 81.3%的平均准确率。每个任务的平均处理延迟为 48.7ms。该框架为在线 EEG-BCI 系统的要求提供了一种替代方案，满足了短处理时间和可靠分类准确性的要求。
