EEG Based Patient Monitoring System for Mental Alertness Using Adaptive Neuro-Fuzzy Approach
Dilshad Begum1, K. M. Ravikumar2, James. Mathew3
, Sanjeev Kubakaddi3, and Rajeev Yadav4
1.Dept of ISE, Ghousia College of Engineering, Ramanagar, Bangalore, India
2.VTU-Regional Office, Mysore, India
3.ITIE Knowledge Solutions, Bangalore, India
4.Genia Photonics Inc, Laval, Canada
2.VTU-Regional Office, Mysore, India
3.ITIE Knowledge Solutions, Bangalore, India
4.Genia Photonics Inc, Laval, Canada
Abstract—Recent electrophysiological studies support command-specific changes in the electroencephalography (EEG) that have promoted their intensive application in the noninvasive brain computer interfaces (BCI). However, EEG is plagued by a variety of interferences and noises, thereby demanding better accuracy and stability for its application in the neuroprosthetic devices. Here we investigate wavelets and adaptive neuro-fuzzy classification algorithms to enhance the classification accuracy of cognitive tasks. Using a standard cognitive EEG dataset, we demonstrate improved performance in the classification accuracy with the proposed system.
Index Terms— BCI, EEG, wavelets, adaptive neuro fuzzy interface system (ANFIS)
Cite: Dilshad Begum, K. M. Ravikumar, James. Mathew, Sanjeev Kubakaddi, and Rajeev Yadav, "EEG Based Patient Monitoring System for Mental Alertness Using Adaptive Neuro-Fuzzy Approach," Journal of Medical and Bioengineering, Vol. 4, No. 1, pp. 59-66, February 2015. Doi: 10.12720/jomb.4.1.59-66
Index Terms— BCI, EEG, wavelets, adaptive neuro fuzzy interface system (ANFIS)
Cite: Dilshad Begum, K. M. Ravikumar, James. Mathew, Sanjeev Kubakaddi, and Rajeev Yadav, "EEG Based Patient Monitoring System for Mental Alertness Using Adaptive Neuro-Fuzzy Approach," Journal of Medical and Bioengineering, Vol. 4, No. 1, pp. 59-66, February 2015. Doi: 10.12720/jomb.4.1.59-66
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