A novel deep learning approach for classification of EEG motor imagery signals.

Journal: Journal of neural engineering

Published Date: Feb 1, 2017

Abstract

OBJECTIVE: Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals.

Authors

Yousef Rezaei Tabar

Biomedical Engineering Department, Middle East Technical University, Ankara, Turkey.
Ugur Halici

Keywords

Algorithms Brain-Computer Interfaces Electroencephalography Evoked Potentials, Motor Humans Imagination Machine Learning Movement Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Sensorimotor Cortex

External Resources

View on PubMed Access via DOI PubMed (27900952)

A novel deep learning approach for classification of EEG motor imagery signals.

Abstract

Authors

Keywords

External Resources

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