Overview of the EEG-Based Classification of Motor Imagery Activities Using Machine Learning Methods and Inference Acceleration with FPGA-Based Cards

• Published in: Electronics (Basel) Vol. 11; no. 15; p. 2293
• Main Authors: Majoros, Tamás, Oniga, Stefan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Acceleration; Accuracy; Algorithms; Artificial neural networks; Brain research; Classification; Decision trees; Deep learning; Electroencephalography; Field programmable gate arrays; Image classification; Image segmentation; Inference; Machine learning; Microprocessors; Neural networks; Neurons; Support vector machines
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11152293

In this article, we provide a brief overview of the EEG-based classification of motor imagery activities using machine learning methods. We examined the effect of data segmentation and different neural network structures. By applying proper window size and using a purely convolutional neural network, we achieved 97.7% recognition accuracy on data from twenty subjects in three classes. The proposed architecture outperforms several networks used in previous research and makes the motor imagery-based BCI more efficient in some applications. In addition, we examined the performance of the neural network on a FPGA-based card and compared it with the inference speed and accuracy provided by a general-purpose processor.