Authors:
Maryam Khoshkhooy Titkanlou
1
;
Ehsan Monjezi
2
and
Roman Mouček
1
Affiliations:
1
Department of Computer Science and Engineering, University of West Bohemia, 306 14 Plzen, Czech Republic
;
2
Department of Electrical Engineering, Shahid Chamran University, Golestan Blvd. Ahvaz, Iran
Keyword(s):
Electroencephalography, Brain-Computer Interface, ERD/ERS, Deep Neural Network, Motor Imagery, Inception Module.
Abstract:
The brain-computer interface (BCI) is an emerging technology that has the potential to revolutionize the world, with numerous applications ranging from healthcare to human augmentation. Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms used extensively in healthcare applications such as rehabilitation. Recently, neural networks, particularly deep architectures, have received substantial attention for analyzing EEG signals (BCI applications). EEG-ITNet is a classification algorithm proposed to improve the classification accuracy of motor imagery EEG signals in a noninvasive brain-computer interface. The resulting EEG-ITNet classification accuracy and precision were 75.45% and 76.43%, using a motor imagery dataset of 29 healthy subjects, including males aged 21-26 and females aged 18-23. Three different methods have also been implemented to augment this dataset.