Title: EEG signals classification based on autoregressive and inherently quantum recurrent neural network

Authors: Saleem M.R. Taha; Zahraa K. Taha

Addresses: Electrical Engineering Department, College of Engineering, University of Baghdad, Al-Jadiryah, Baghdad, Iraq ' Department of Network Engineering, College of Engineering, Al-Iraqia University, Baghdad, Iraq

Abstract: This paper shows a novel hybrid approach using an Auto-Regressive (AR) model and a Quantum Recurrent Neural Network (QRNN) for classification of two classes of Electroencephalography (EEG) signals. The QRNN-AR has been shown to be capable to capture and quantify the uncertainty inherently in EEG signals because it uses fuzzy decision boundaries to partition the feature space. Two diverse element extraction techniques were used to extract the features from EEG signals; AR coefficients are processed with Levinson-Durbin algorithm and mean square error. AR provides a better frequency resolution and good spectral data of short EEG segment. The QRNN trained by the back propagation algorithm is compared with Quantum Neural Network (QNN) and Quantum Wavelet Neural Network (QWNN). The average accuracy of the proposed QRNN model is 88.28452% at 6 seconds. The Accuracy to Time Ratio (ATR) value is 14.714086, which shows the superiority of the proposed model. Experimental results demonstrate that the QRNN-AR gives the highest overall accuracy and short processing time. In addition, the structure of the proposed method is more reliable.

Keywords: biomedical signal processing; EEG; electroencephalography; feature extraction; quantum computing; recurrent neural networks.

DOI: 10.1504/IJCAT.2018.095942

International Journal of Computer Applications in Technology, 2018 Vol.58 No.4, pp.340 - 351

Received: 16 Nov 2016
Accepted: 26 Sep 2017
Published online: 05 Nov 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article