Studies On Spatio-Temporal Modeling Methods For Multichannel EEG Based On Statistical Modeling

Electroencephalography (EEG) is an important tool for recordingfunctional brain activity. It is suited for investigating the mechanism ofbrain functions at large spatial scales. For the purpose of effectivelyextracting rich spatial and temporal information from multichannel EEGsignals, this thesis undertakes a comprehensive and in-depth study ofspatio-temporal modeling approaches for EEG within a statistical modelingframework.Oscillatory signals and event-related potentials (ERPs) are two generalcategories of EEG signals. According to their distinct characteristics, thisthesis employs different strategies to establish their respectivespatio-temporal models.For oscillatory signals, a hierarchical Bayesian model is introduced forlearning their spatio-temporal patterns in a condition-and trial-wise fashion.Data information within multiple trials and conditions are allowed to beshared among one another via proper model constraints. The variationalBayes algorithm, which is developed for model inference, is capable ofautomatically inferring the number of components in the model via sparseBayesian learning, avoiding potential overfit to the data.For ERP signals, since their signal-to-noise ratio is rather low in a single trial, to enhance its estimation a mixed-effect statistical model isproposed to model ERP components and background EEG components in aseparate manner. Moreover, to reveal ERP dynamics across trials, theinter-trial amplitude and latency variability of the ERP components is alsoexplicitly parameterized in the model. The fact that the model offers moreinformation regarding ERPs suggests its high value for practicalapplications.Based on the above proposed spatio-temporal models, three spatialfiltering learning algorithms are designed for feature extraction inbrain-computer interfaces (BCIs). Specifically, the OVR-CSP and R-CSPalgorithms generalize the classic common spatial patterns (CSP) algorithm,extending its use to situations where there are multiple classes or there areoutliers in the data. The SIM algorithm enhances the ERP components bymaximizing their signal-to-noise ratio. All these algorithms cansignificantly improve the performance of current BCI systems, indicatingtheir high potential for a broad range of applications.As a supplement to the spatio-temporal modeling framework, thisthesis also develops an algorithm, termed ISSPL, for joint spatial filtering,spectral filtering, and classification of motor imagery EEG data. In theISSPL algorithm, spectral filters are optimized in conjunction with theclassifier via maximal margin learning, overcoming the potentialoverfitting issue due to the high-dimensionality of the spectral coefficients. The validity of all the proposed models is discussed at full length. Theeffectiveness of all the algorithms is shown through the analysis ofsimulated data and real EEG recordings. The results demonstrate that, incomparison with other contemporary algorithms, the algorithms describedin this thesis yield superior performance in all cases.