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.

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Motor Imagery Multi-Tasks Classification for BCIs Using the NVIDIA Jetson TX2 Board and the EEGNet Network

Navigation of drones can be conceivably performed by operators by analyzing the brain signals of the person. EEG signal corresponding to the motor imaginations can be used for generation of control signals for drone. Different machine learning and deep learning approaches have been developed in the state of the art literature for the classification of motor imagery EEG signal. There is still a need for developing a suitable model that can classify the motor imagery signal fast and can generate a navigation command for drone in real-time. In this paper, we have reported the performance of convolutional stacked autoencoder and Convolutional Long short term memory models for classification of Motor imagery EEG signal. The developed models have been optimized using TensorRT that speeds up inference performance and the inference engine has been deployed on Jetson TX2 embedded platform. The performance of these models have been compared with different machine learning models.

Anamika

Papers

Classification of Motor Imagery EEG Signal for Navigation of Brain Controlled Drones