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. This paper presents an alternative neural information processing architecture using Schrödinger wave equation for enhancement of the raw EEG signal. The raw EEG signal obtained from the motor imagery (MI) of a BCI user is intrinsically embedded with non-Gaussian noise while the actual signal is still a mystery. The proposed recurrent quantum neural network (RQNN) is designed to filter such non-Gaussian noise using an unsupervised learning scheme without making any assumption about the signal type. The proposed learning architecture has been modified to do away with the Hebbian learning associated with the existing RQNN architecture [1] as this learning scheme was found to be unstable for complex signals such as EEG. Besides this the soliton behaviour of the non-linear Schrödinger wave equation was not properly preserved in the existing scheme. The unsupervised learning algorithm proposed in this paper is able to efficiently capture the statistical behaviour of the input signal while making the algorithm robust to parametric sensitivity. 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 much higher compared to that using the raw EEG signal. The improvement in classification accuracy computed over nine subjects is found to be statistically significant.

Electroencephalogram (EEG) signals are one of the most widely used non-invasive signals in Brain Computer Interfaces (BCI). Large dimensional EEG recordings suffer from poor SNR (Signal to Noise Ratio). These signals are very much prone to artifacts and noise, so sufficient preprocessing is done on raw EEG signals before using them for classification or regression. Properly selected spatial filters enhance the signal quality and subsequently improve the rate and accuracy of classifiers, but their applicability to solve regression problems is quite an unexplored objective. This paper extends Common Spatial Patterns (CSP) to EEG state-space using fuzzy time delay and thereby proposes a novel approach for spatial filtering. The approach also employs a novel fuzzy information theoretic framework for filter selection. Experimental performance on EEG based reaction time prediction from a Lane-keeping task data from 12 subjects, demonstrated that the proposed spatial filters can significantly increase the EEG signal quality. A comparison based on Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation to true responses is made for all the subjects. In comparison to the baseline fuzzy CSPROVR (Common Spatial Patterns Regression One Versus Rest), the proposed Fuzzy Time-delay Common Spatio-Spectral (FTDCSSP) filters reduced the RMSE on an average by 9.94%, increased the correlation to true reaction time on an average by 7.38% and reduced the MAPE by 7.09%.

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In this work, we study generative adversarial networks (GANs) as a tool to learn the distribution of spin configurations and to generate samples, conditioned on external tuning parameters or other quantities associated with individual configurations. For concreteness, we focus on two examples of conditional variables---the temperature of the system and the energy of the samples. We show that temperature-conditioned models can not only be used to generate samples across thermal phase transitions, but also be employed as unsupervised indicators of transitions. To this end, we introduce a GAN-fidelity measure that captures the model’s susceptibility to external changes of parameters. The proposed energy-conditioned models are integrated with Monte Carlo simulations to perform over-relaxation steps, which break the Markov chain and reduce auto-correlations. We propose ways of efficiently representing the physical states in our network architectures, e.g., by exploiting symmetries, and to minimize the correlations between generated samples. A detailed evaluation, using the two-dimensional XY model as an example, shows that these incorporations bring in considerable improvements over standard machine-learning approaches. We further study the performance of our architectures when no training data is provided near the critical region.

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