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Categorizing Imagined Right and Left Motor Imagery BCI tasks for low-cost Robotic Neuroprosthesis

Publication Type : Conference Paper

Thematic Areas : Learning-Technologies, Medical Sciences, Biotech

Publisher : 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), IEEE, Chennai, India

Source : 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), IEEE, Chennai, India (2016)

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Keywords : Articulation control, bioelectric potentials, brain-computer interface, brain-computer interfaces, Classification algorithms, EEG, EEG patterns, EEG signals, Electroencephalography, electroencephalography-based brain computer interfaces, Feature extraction, frequency-based categorization, instantaneous band power, ipsilateral areas, learning (artificial intelligence), low-cost robotics, medical robotics, medical signal processing, motor imagery, motor imagery tasks, Neurophysiology, one-sided hand movement imagination, Prosthetics, right-left motor imagery BCI tasks, Robot sensing systems, robotic articulator, robotic neuroprosthesis, spectral classification, standard machine learning algorithms, Support vector machines, visualized imagery.

Campus : Amritapuri

School : School of Biotechnology

Center : Amrita Mind Brain Center, Biotechnology, Computational Neuroscience and Neurophysiology

Department : biotechnology

Year : 2016

Abstract : Focusing on low-cost articulation control for neuroprosthesis, electroencephalography (EEG)-based brain computer interfaces require rapid and reliable discrimination of EEG patterns associated with motor imagery generated via imagined or real movement. The objective of this study was to characterize EEG signals of two different motor imagery tasks used to control a robotic articulator. With one-sided hand movement imagination resulting in EEG changes located contra and ipsilateral areas, time-courses of two different imagery tasks were investigated via instantaneous band power changes. We compared the features extracted from the EEG patterns with standard machine learning algorithms. We report frequency-based categorization of visualized imagery more relevant than machine learning methods.

Cite this Research Publication : Sandeep Bodda, Harikrishnan Chandranpillai, Pooja Viswam, Swathy Krishna, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Categorizing imagined right and left motor imagery BCI tasks for low-cost robotic neuroprosthesis”, in Proceedings of IEEE International Conference on Electrical, Electronics and Optimization Techniques (ICEEOT 2016), Chennai, March 3-5, 2016.

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