Diversity in a signal-to-image transformation approach for EEG-based motor imagery task classification

Nowadays, motor imagery-based brain-computer interfaces (BCIs) have been developed rapidly. In these systems, electroencephalogram (EEG) signals are recorded when a subject is involved in the imagination of doing any motor imagery movement like the imagination of the right/left hands, etc. In this paper, we sought to validate and enhance our previously proposed angle-amplitude transformation (AAT) technique, which is a simple signal-to-image transformation approach for the classification of EEG and MEG signals. For this purpose, we diversified our previous method and proposed four new angle-amplitude graph (AAG) representation methods for AAT transformation. These modifications were made on some points such as using different left/right side changing points at a different distance. To confirm the validity of the proposed methods, we performed experiments on the BCI Competition III Dataset IIIa, which is a benchmark dataset widely used for EEG-based multi-class motor imagery tasks. The procedure of proposed methods can be summarized in a concise manner as follows: (i) convert EEG signals to AAG images by using the proposed AAT transformation approaches; (ii) extract image features by employing Scale Invariant Feature Transform (SIFT)-based Bag of Visual Word (BoW); and (iii) classify features with k-Nearest Neighbor (k NN) algorithm. Experimental results showed that the changes in the baseline AAT approaches enhanced the classification performance on Dataset IIIa with an accuracy of 96.50% for two-class problem (left/right hand movement imaginations) and 97.99% for four-class problem (left/right hand, foot and tongue movement imaginations). These achievements are mainly due to the help of effective enhancements on AAG image representations. Graphical The flow diagram of the proposed methodology.