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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Driver drowsiness is receiving a lot of deliberation as it is a major cause of traffic accidents. This paper proposes a method which utilizes the fuzzy common spatial pattern optimized differential phase synchrony representations to inspect electroencephalogram (EEG) synchronization changes from the alert state to the drowsy state. EEG-based reaction time prediction and drowsiness detection are formulated as primary and ancillary problems in the context of multi-task learning. Statistical analysis results suggest that our method can be used to distinguish between alert and drowsy state of mind. The proposed Multi-Task DeepNet (MTDNN) performs superior to the baseline regression schemes, like support vector regression (SVR), least absolute shrinkage and selection operator, ridge regression, K-nearest neighbors, and adaptive neuro fuzzy inference scheme (ANFIS), in terms of root mean squared error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) metrics. In particular, the best performing multi-task network MTDNN 5 recorded a 15.49% smaller RMSE, a 27.15% smaller MAPE, and a 10.13% larger CC value than SVR.

Similar to most of the real world data, the ubiquitous presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. In this letter, a novel method is proposed based on Joint Approximate Diagonalization (JAD) to optimize stationarity for multiclass motor imagery Brain Computer Interface (BCI) in an information theoretic framework. Specifically, in the proposed method, we estimate the subspace which optimizes the discriminability between the classes and simultaneously preserve stationarity within the motor imagery classes. We determine the subspace for the proposed approach through optimization using gradient descent on an orthogonal manifold. The performance of the proposed stationarity enforcing algorithm is compared to that of baseline One-Versus-Rest (OVR)-CSP and JAD on publicly available BCI competition IV dataset IIa. Results show that an improvement in average classification accuracies across the subjects over the baseline algorithms and thus essence of alleviating within session nonstationarities.