An Analytical framework for Classification of ElectroEncephaloGram (EEG) Motor Imagery tasks using Deep Learning Techniques

Motor Imagery (MI) on Electroencephalogram (EEG) signals is a common approach within Brain-Machine Interface (BMI) models, extensively applied in medical rehabilitation and artificial machinery. BMI serves as a direct link between the brain's electrical activity and external devices, such as computers or artificial limbs. BMIs are regularly expressed at researching, directing, helping, extending, augmenting, and correcting the people cognitive or motor sensory devices. The accomplishment of BMI’s ranges from non-invasive to invasive device based on the tight electrodes gets into brain tissue. Now a days, Deep Learning (DL) is playing a crucial role in MI EEG based BMI. The framework focuses on how Deep Learning (DL) is enhancing MI-based BMIs by improving signal processing and classification. Key aspects covered include preprocessing, feature extraction, classification techniques, and performance evaluation. The analysis highlights the strengths and challenges of various DL approaches, providing insights to advance the development of reliable and efficient BMI systems. The models were analyzed with respect to the domains of undirected and discriminated analysis using belief networks.