Recurrent Quantum Neural Network filters EEG signal for an improved Brain-Computer Interface

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 explain the tracking of 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 de-noising the raw data. Thus the filtered EEG signal is a wave packet which glides along and moves like a particle. This estimated EEG signal is then fed as an input to the feature extractor to obtain the Hjorth features. These features are then used to train the Linear Discriminant Analysis (LDA) and the Support Vector Machine (SVM) classifiers. The results show that the accuracy of the classifier output using the filtered EEG and the wave packet generated feature is better compared to that using the raw EEG signal. Also, the proposed scheme has been effectively used to predict the user intention which is not clearly observed in the raw EEG data.