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

Autor:	Tamás Majoros, Stefan Oniga
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Computer Networks and Communications Hardware and Architecture Control and Systems Engineering Signal Processing Electrical and Electronic Engineering EEG neural network classification data segmentation activity recognition Alveo
Zdroj:	Electronics; Volume 11; Issue 15; Pages: 2293
ISSN:	2079-9292
DOI:	10.3390/electronics11152293
Popis:	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.
Databáze:	OpenAIRE
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