Chaotic behaviour of EEG responses with an identical grasp posture. (August 2020) - Roy, Rinku; Sikdar, Debdeep; Mahadevappa, Manjunatha
Abstract: Individuals with severe neuromuscular ailments can benefit from restoring their grasp activities with a brain-controlled upper-limb neuroprosthesis. EEG signals can be utilized as the driving source, and to implement natural human-like grasping abilities. Although good accuracy has already been achieved in classifying the various grasp patterns for specific sets of objects, unseen objects are still a hurdle in real-life implementation. Generalization of grasp patterns should be explored without any prior knowledge of the objects. In this regard, the similarity of motor imagery for different objects requiring similar grasp pattern can be utilized. It is also necessary to identify the brain regions that exhibit prominent distinguishability during different grasp patterns. In this study, we propose a chaos-based method to decode the motor imagery of two quite similar Power grasp patterns-cylindrical and spherical-for holding various objects. Three distinct suitable objects were chosen for each of the two patterns, and a 29-channel EEG was taken of 18 healthy participants to explore motor imagery for grasping the objects. Nonlinear correlation dimension was employed on the EEG data, at sub-band levels α, upper β, and γ, to analyse the distinguishability, as well as the similarity of grasp patterns for the objects. ANOVA was subsequently performed on the obtained CD parameters to identify the contribution of each electrode channel. Furthermore, using an SVM classifier, more than 80% accuracy was obtained in classifying the grasping patterns at the upper β sub-band. The outcome may lead to identification of optimum feature sets of motor imagery from specific brain regions for random objects grasps. Highlights: Holding various shaped objects with same grasp pattern can be recognized from EEG. CD parameters were found promising in distinguishing grasp imageries. Generalization of decoding grasp imagery independent of object shapes is achievable. CD-SVM achieved 80.6% accuracy in classifying grasp patterns at upper β subband.