Saha, S. and Konar, A. and Saha, A. and Sadhu, A. and Banerjee, B. and Nagar, Atulya K. (2016) EEG Based Gesture Mimicking by An Artificial Limb Using Cascade-Correlation Learning Architecture. In: 2016 International Joint Conference on Neural Networks, 24-29 July 2016, Vancouver, Canada.
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4. IJCNN-17071-EEG Based Gesture Mimicking by An Artificial Limb Using Cascade-Correlation Learning Architecture.pdf Download (5MB) | Preview |
Abstract
Patients with prosthesis defects find it is very
difficult to perform day-to-day basic tasks which involve
employment of their limbs. This motivates us to develop a system where an artificial limb is employed to mimic the arm gestures of the patients for assisting them. Towards developing this system, we have taken the help from the electroencephalography (EEG) signals acquired from the brain of the patients to build a bypass network (BPN) to direct the artificial limb. Since difficulties are
already present in the arm movements of the patients (here subjects), thus only gestures of those subjects are not sufficient to build the proposed system. This research finds tremendous applications in rehabilitative aid for the disable persons. To concretize our goal we have developed an experimental setup, where the target subject (for training phase healthy subjects are taken into account) is asked to catch a ball while his/her brain (occipital, parietal and motor cortex) signals using EEG acquisition device and body gestures using Kinect sensor are simultaneously acquired. These data are mapped using four cascade-correlation learning architecture (CCLA) to train artificial limb (we have used Jaco robot arm) to move accordingly. Utilizing the mapping results obtained from these four CCLAs, a BPN is developed. When a rehabilitative patient is unable to catch the ball, then in that scenario, the artificial limb is helpful for assisting the patient to catch the ball with a high accuracy of 85.65%. The proposed system can be implemented not only for ball catching experiment but also in several applications where an artificial limb needs to perform a locomotive task based on EEG and body gesture.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information and Comments: | (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works." |
| Keywords: | human machine interface; gesture mimicking; rehabilitation; electroencephalography; Kinect sensor; Jaco robot arm; cascade- correlation learning architecture |
| Faculty / Department: | Faculty of Human and Digital Sciences > Mathematics and Computer Science |
| Depositing User: | Atulya Nagar |
| Date Deposited: | 09 Jun 2016 11:42 |
| Last Modified: | 24 Nov 2016 12:09 |
| URI: | https://hira.hope.ac.uk/id/eprint/1447 |
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