Adaptive EEG Thought Pattern Classifier for Advanced Wheelchair Control

This paper presents a real-time electroencephalogram (EEG) classification system, with the goal of enhancing the control of a head-movement controlled power wheelchair for patients with chronic spinal cord injury (SCI). Using a 32 channel recording device, mental command data was collected from 10 participants. This data was used to classify three different mental commands, to supplement the five commands already available using head movement control. Of the 32 channels that were recorded only 4 were used in the classification, achieving an average classification rate of 82%. This paper also demonstrates that there is an advantage to be gained by doing adaptive training of the classifier. That is, customizing the classifier to a person previously unseen by the classifier caused their average recognition rates to improve from 52.5% up to 77.5%.

[1]  Z. Keirn,et al.  A new mode of communication between man and his surroundings , 1990, IEEE Transactions on Biomedical Engineering.

[2]  E. Crisman,et al.  Using The Eye Wink Control Interface To Control A Powered Wheelchair , 1991, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991.

[3]  Francisco Rodríguez,et al.  Electronic control of a wheelchair guided by voice commands , 1995 .

[4]  A Searle,et al.  The effectiveness of activating electrical devices using alpha wave synchronisation contingent with eye closure. , 2000, Applied ergonomics.

[5]  K. Morita,et al.  Guidance of a wheelchair by voice , 2000, 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies.

[6]  M. Mazo,et al.  System for assisted mobility using eye movements based on electrooculography , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  S. P. Levine,et al.  Voice control of a powered wheelchair , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  Yangsheng Xu,et al.  A cap as interface for wheelchair control , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  H.T. Nguyen,et al.  Real-time head movement system and embedded Linux implementation for the control of power wheelchairs , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  H.T. Nguyen,et al.  Hands-free Head-movement Gesture Recognition using Artificial Neural Networks and the Magnified Gradient Function , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[11]  H.T. Nguyen,et al.  Wireless Real-Time Head Movement System Using a Personal Digital Assistant (PDA) for Control of a Power Wheelchair , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[12]  H.T. Nguyen,et al.  Two Channel EEG Thought Pattern Classifier , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.