Publication Type : Conference Paper
Thematic Areas : Learning-Technologies, Medical Sciences, Biotech
Publisher : Proceedings of the 2nd International Conference on Cybernetics, Cognition and Machine Learning Applications, Goa (India) .
Source : Proceedings of the 2nd International Conference on Cybernetics, Cognition and Machine Learning Applications, Goa (India) August 29-30, 2020.
Url : https://link.springer.com/chapter/10.1007/978-981-33-6691-6_13
ISBN : 9789813366916
Keywords : Brain–computer interfaces, Electroencephalography, Human arm movement, Motor execution, motor imagery
Campus : Amritapuri
School : School of Biotechnology
Center : Amrita Mind Brain Center, Biotechnology, Computational Neuroscience and Neurophysiology
Department : Computational Neuroscience Laboratory, biotechnology
Year : 2020
Abstract : Motor behavior in human arm movement is characterized by high number of degrees of freedom for executing most actions of daily life. Neuroscience studies have facilitated acquisition of generalized neural representations in arm movements and revealed corresponding neural networks as pre-supplementary motor area, anterior prefrontal cortex and parietal cortex as functional regions underlying voluntary action. Understanding cognitive brain functions is associated with motor functions; motor execution (ME) and motor imagery (MI) and visual imagery (VI) cognitive tasks have wider applications in complex skill learning and motor rehabilitation and for implementing prosthetic devices for amputees. The present study focused on understanding the cortical activation similarities in ME, MI, and VI using a familiar marble board game-based upper limb motor tasks among left- and right-handed healthy volunteers (N\thinspace=\thinspace16), using non-invasive electroencephalography (EEG) technique. The study investigated neural dynamics associated with complex and linear movement patterns in motor imagery, motor execution, and visual imagery and functionally mapped the cortical areas of activation associated with motor planning and motor execution phase of different motor performances. Although beta and alpha rhythms were functionally present during motor performance, this study revealed theta and gamma rhythm patterns in temporal and frontal regions as biosignatures for planning and execution of movement and imagery tasks of linear and complex movement patterns. The features extracted from the EEG data could be further compared with standard machine learning algorithms. Initial findings will be applicable in designing brain–computer interface (BCI)-based rehabilitation therapies in neuroprosthetics.
Cite this Research Publication : Rakhi Radhamani, Alna Anil, Gautham Manoj, Gouri Babu Ambily, Praveen Raveendran, Vishnu Hari, and Dr. Shyam Diwakar, “Decoding Motor Behavior Biosignatures of Arm Movement Tasks Using Electroencephalography”, in Proceedings of the 2nd International Conference on Cybernetics, Cognition and Machine Learning Applications, Goa (India) August 29-30, 2020.