Classification of EEG Signals from Motor Imagery of Hand Grasp Movement Based on Neural Network Approach

Improving single-hand open/close motor imagery classification by error-related potentials correction

“…It could also lead to better hand rehabilitation for stroke patients. The current dichotomy of different movements of the single-hand is at a random level [ 14 ], and multi-category classification is more complex and the classification effect is worse. In this article, we propose a new hybrid paradigm based on MI and ErrP, which can significantly improve the decoding accuracy of hand open and closed actions using simple classification strategies.…”

“…Similar movements of the ipsilateral limbs, such as hand opening and closing, assume important functions in life, and decoding hand movements by MI signals is important for hand rehabilitation training in stroke patients. The current dichotomous classification of MI signals for single-hand open and closed movements is slightly higher than that of the random level [ 14 ]. Aleksandra and Francisco classified four different movements of the right wrist (extension, flexion, pronation, and supination), obtaining a classification accuracy of 58–82% [ 15 ].…”

Effect of hand grip actions on object recognition process: a machine learning-based approach for improved motor rehabilitation

“…The EEG recordings contain cortical potentials, which occur during various mental processes [2]. These signals comprise of different frequency sub-bands: Delta (4 Hz), Theta (4-7 Hz), Alpha or mu (8-12 Hz), Beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and Gamma (30-100 Hz) bands, to facilitate ease of analysis. Studies presented in [3,4] found out that mu and beta rhythms are more sensitive to correct and incorrect hand grips and respond strongly especially over motor and pre-motor cortex areas of brain.…”