EEG controlled remote robotic system from motor imagery classification

Saugat Bhattacharyya, Abhronil Sengupta, Tathagatha Chakraborti, Dhrubojyoti Banerjee, Anwesha Khasnobish, Amit Konar, D. N. Tibarewala, R. Janarthanan

Research output: Contribution to conferencePaper

12 Scopus citations

Abstract

This paper aims at laying a foundation towards the development of a robust platform for efficient control of the motion of autonomous mobile robots. Electroencephalographic (EEG) signals liberated during motor imagery of a human controller have been used to design the control mechanism. The proposed scheme can find widespread applications in the defense sector as secrecy of generated commands can be maintained efficiently. With decoding of the brain signals for various limb movements being a major area of research for EEG based BCI, the paper employs the usage of finger-elbow-shoulder movement classification in addition to the left-right arm movement classification. The proposed system integrates the advantages of high classification accuracy of EEG measurements (more than 80% for both the folds, i.e., Left-Right classification and Finger-Elbow-Shoulder classification) along with a suitable coding technique of liberated control signals for error detection thereby paving the way for an efficient EEG signal encoded robot control system design.

Original languageEnglish (US)
DOIs
StatePublished - 2012
Event2012 3rd International Conference on Computing, Communication and Networking Technologies, ICCCNT 2012 - Coimbatore, Tamilnadu, India
Duration: Jul 26 2012Jul 28 2012

Conference

Conference2012 3rd International Conference on Computing, Communication and Networking Technologies, ICCCNT 2012
CountryIndia
CityCoimbatore, Tamilnadu
Period7/26/127/28/12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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    Bhattacharyya, S., Sengupta, A., Chakraborti, T., Banerjee, D., Khasnobish, A., Konar, A., Tibarewala, D. N., & Janarthanan, R. (2012). EEG controlled remote robotic system from motor imagery classification. Paper presented at 2012 3rd International Conference on Computing, Communication and Networking Technologies, ICCCNT 2012, Coimbatore, Tamilnadu, India. https://doi.org/10.1109/ICCCNT.2012.6395890