BCI-Based Motor Imagery EEG Signal Classification Using a Novel Method (EEG-ITT) in Upper-Limb Exoskeleton

Brain-computer interface (BCI) is an emerging technology that receives, processes, and converts brain signals into commands sent to output devices to perform desired tasks. Motor imagery (MI) based on electroencephalograms (EEGs) is one of the most widely used BCI paradigms, and it has demonstrated potential as an effective tool for neurorehabilitation. Recently, neural networks-in particular, deep architectures-have received substantial attention for the analysis of EEG signals (BCI applications). This paper proposes a new classification algorithm called EEG-ITT to increase the accuracy of classification motor imagery EEG signals using a non-invasive brain-computer interface. Utilizing a motor imagery dataset of 29 healthy subjects, including males aged 2126 and females aged 18-23, the proposed model demonstrated the highest accuracy, at 79.53 %. The noise injection method has also been implemented for data augmentation.