A lightweight methodology for Motor Imagery EEG classification utilizing step scaled wavelet fractals and Bi-LSTM architecture

Brain-Computer Interface (BCI) represents a cutting-edge area of research that integrates neuroscience, engineering, and computer science to establish direct communication links between the human brain and external systems. BCI-based technologies hold significant promise, particularly in the development of prosthetic devices. However, the practical application of BCI in real-time scenarios faces several obstacles, including bulky models, noise interference, artifacts, and the complexity of motor imagery (MI) electroencephalogram (EEG) data, which exhibits both inter-subject and intra-subject variability. To address these challenges, the proposed algorithm utilizes loading and pre-processing of MI EEG, extracts their common spatial patterns, calculates the continuous wavelet transform (CWT) coefficients, computes their proposed step scaled mean wavelet fractals which exhibits robustness towards inherent noises and artifacts, calculates the cross-correlation matrix at different scale for all channels and observes the evolution in cross-correlation matrices with the help of customized Bi-long-short term memory (Bi-LSTM) neural network to classify MI EEG. The customized Bi-LSTM architecture had the size < 10MB showing the effectiveness of methodology for MI EEG classification utilizing embedded based devices. The best classification accuracies achieved with proposed step scaled mean wavelet fractals were 87.75% and 85.45% for intra-subject as well as 76.16% and 71.76% for inter-subject on BCI Competition IV 2b and 2a respectively; the comparative analysis with earlier state of the art methods showed an average improvement of 2.28% and 2.33% for intra-subject as well as 0.22% and 3.21% for inter-subject in accuracy.