# Effect of Subject-Specific Region of Interest on Motor Imagery Brain-Computer Interface
###### Abstract  
A brain-computer interface (BCI), as a solution to disabled people's concerns, has drawn attention in biomedical engineering over the last decade. However, the most existing brain-computer interface systems are based on the time or frequency domain of feature extraction, and it is associated with inaccurate detection of event-related desynchronization (ERD). In this study, a new algorithm relating to subject-specific regions of interest (ROIs) with intrinsic time-scale decomposition (ITD) was investigated to achieve satisfactory classification accuracy. ROI-based discrete wavelet transform (DWT) combined with an artificial neural network was used to validate the ROI-based ITD method. Experimentally recorded data of motor imagery movement tasks (right hand, left hand, both hands and both feet) were collected from 15 subjects. The parameters of the subject-specific regions of interest were investigated and optimized. An optimal condition was observed at a specific region of interest and the accuracy increased by 12.76 to 15.17% compared to that without ROI estimation. ITD showed higher classification accuracy, sensitivity, specificity and Kappa coefficient of 9.47%, 8.99%, 9.79% and 12.09%, respectively, for the four classes of motor imagery movements compared to DWT. The developed ITD model was validated using the dataset from BCI Competition IV. On average, ITD with ROIs showed 8.56% and 7.32% higher classification accuracy compared to common spatial patents (CSP) and DWT with ROIs. intrinsic time-scale decomposition; discreet wavelet transform; artificial neural network; electroencephalography; brain-computer interface; event-related desynchronization (ERD) 202313634 10.3390/app13116364 10.  
known as sensorimotor rhythms (SMR) [8,9]. Although ERD and ERS have been investigated with regard to their modulation effects on SMR [10, 11, 12], the dynamics of ERD/ERS phenomena during motor tasks have not been studied. The ERD can be readily detected in EEG signals within the band of alpha (8 Hz to 12 Hz) and beta rhythms at frequencies of 8 Hz to 12 Hz and 14 Hz to 28 Hz, respectively [13]. The data behavior at specific frequency bands was computed using the coefficient of determination (r2) as a function of electrode location [14]. The Rolandic beta and mu rhythms in the human brain were observed in sensorimotor areas with frequency bands between 10 Hz and 20 Hz [15]. These frequency bands show typical responses associated with the subject's movement and motor imagery [16, 17, 18]. However, ERD/ERS values have been found to vary between individuals because of the differences in brain states, structures and physiology. As a result, different frequency ranges, time periods, and brain regions are activated. Several methods have been proposed to detect and classify ERD/ERS in BCI. [PERSON] et al. [8,19] combined band power and learning vector quantization (LVQ) for classification. However, inferior results were reported because the band power requires prior knowledge of frequency bands and reactive bands. Distinction-sensitive learning vector quantization was used as an automated approach to select relevant bands in fixed time segments [20]. [PERSON] and [PERSON] [21] maximized the scatters between two classes of EEGs using a common special pattern (CSP) for a motor imagery task. The diagonals of two covariance matrices were used to compute the spatial filters. However, CSP-based approaches were found to have some drawbacks. It depends on subject-specific segments and frequency bands, and the estimations of covariance matrices are affected by noises and outliers [21]. The spatial non-stationary characters of ERS/ERD and the temporal frequencies result in high inter-subject and intra-subject variability in motor imagery task-based BCIs. However, the occurrences of ERD and ERS are found to be independent of a time interval; hence, selecting which segment is active could be a problem.