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A RECURRENT QUANTUM NEURAL NETWORK MODEL ENHANCES THE EEG SIGNAL FOR AN IMPROVED BRAIN-COMPUTER INTERFACE

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Gandhi, V, Arora, V, Behera, Laxmidhar, Prasad, G, Coyle, DH and McGinnity, TM (2011) A RECURRENT QUANTUM NEURAL NETWORK MODEL ENHANCES THE EEG SIGNAL FOR AN IMPROVED BRAIN-COMPUTER INTERFACE. In: Assisted Living 6 April 2011 IET London: Savoy Place, UK. IET. 6 pp. [Conference contribution]

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URL: http://conferences.theiet.org/assisted-living/programme/index.cfm

Abstract

The brain-computer interface (BCI) technology is a means of communication that allows individuals with severe movement disability to communicate with external assistive devices using the electroencephalogram (EEG) or other brain signals. The human mind and mental processes are inherently quantum in nature. It is therefore logical to investigate the possibility of designing new approaches to Brain-computer interface (BCI) with the amalgamation of quantum and classical approaches. This paper presents an intelligent information processing paradigm to enhance the raw electroencephalogram (EEG) data. A Recurrent Quantum Neural Network (RQNN) model using a non linear Schrodinger Wave Equation (SWE) is proposed here to filter the Motor Imagery (MI) based EEG signal of the BCI user. It is shown that if the potential field of the SWE is excited by the raw EEG data using a self-organized learning scheme, then the probability density function (pdf) associated with the EEG signal is transferred to the probability amplitude function which is the response of the SWE. In this scheme, the EEG data is encoded in terms of a particle like wave packet which helps to recover the EEG signal by denoising the raw data. Thus the filtered EEG signal is a wave packet which glides along and moves like a particle. This denoised EEG signal is then fed as an input to the feature extractor to obtain the Hjorth features. These features are then used to train a Linear Discriminant Analysis (LDA) classifier. It is shown that the accuracy of the classifier output over the training and the evaluation datasets using the filtered EEG is enhanced compared to that using the raw EEG signal for six of the nine subjects with a fixed set of parameters for all the subjects.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:18526
Deposited By:Professor Girijesh Prasad
Deposited On:19 May 2011 12:48
Last Modified:23 May 2011 10:31

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