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Year : 2020  |  Volume : 10  |  Issue : 4  |  Page : 228-238

Thought-actuated wheelchair navigation with communication assistance using statistical cross-correlation-based features and extreme learning machine

1 Department of Mechatronics Engineering, AMA International University, Salmabad, Bahrain
2 Department of Computer Science and Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, Tamil Nadu, India
3 Electrical, Electronic and Automation Section, Universiti Kuala Lumpur Malaysian Spanish Institute, Kedah, Malaysia
4 School of Mechatronic Engineering, Universiti Malaysia Perlis, Perlis, Malaysia

Correspondence Address:
Dr. Sathees Kumar Nataraj
Department of Mechatronics Engineering, AMA International University
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jmss.JMSS_52_19

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Background: A simple data collection approach based on electroencephalogram (EEG) measurements has been proposed in this study to implement a brain–computer interface, i.e., thought-controlled wheelchair navigation system with communication assistance. Method: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd-ball paradigm. The proposed system records EEG signals from 10 individuals at eight-channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross-correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross-correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures (“minimum,” “mean,” “maximum,” and “standard deviation”) were derived from the cross-correlated signals. Finally, the extracted feature sets were validated through online sequential-extreme learning machine algorithm. Results and Conclusion: The results of the classification networks were compared with each set of features, and the results indicated that μ (r) feature set based on cross-correlation signals had the best performance with a recognition rate of 91.93%.

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