METHODS: We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.
RESULTS: The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.
COMPARISON WITH EXISTING METHODS: Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.
CONCLUSIONS: The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.
NEW METHOD: In this study the presence of reactive astrocytes and NG2 proteoglycans was investigated in two ex vivo models of SCI; stab injury and transection injury. Stereological analysis to measure immunohistochemical staining was performed on the scar and injury zones to detect astrocytes and the chondroitin sulphate proteoglycan NG2.
RESULTS: The volume fraction (Vv) of reactive astrocytes and NG2 proteoglycans increased significantly between day 3 and day 10 post injury in both ex vivo models. This data shows how ex vivo SCI models are a useful research tool allowing reduction of research cost and time involved in carrying out animal studies, as well as reducing the numbers of animals used.
COMPARISON WITH EXISTING METHOD: This is the first evidence of an ex vivo stab injury model of SCI and also the first comparison of immunohistochemical staining for injury markers within stab injured and transection injured ex vivo slice cultures.
CONCLUSIONS: The use of organotypic slice culture models provide a simple way to study the cellular consequences following SCI and they can also be used as a platform for potential therapeutics regimes for the treatment of SCI.
NEW METHOD: To overcome these limitations, we propose a new statistical model that smooths out the noise by exploiting the geometric structure of correlation matrices. The dynamic correlation matrix is modeled as a linear combination of symmetric positive-definite matrices combined with cosine series representation. The resulting smoothed dynamic correlation matrices are clustered into disjoint brain connectivity states using the k-means clustering algorithm.
RESULTS: The proposed model preserves the geometric structure of underlying physiological dynamic correlation, eliminates unwanted noise in connectivity and obtains more accurate state spaces. The difference in the estimated dynamic connectivity states between males and females is identified.
COMPARISON WITH EXISTING METHODS: We demonstrate that the proposed statistical model has less rapid state changes caused by noise and improves the accuracy in identifying and discriminating different states.
CONCLUSIONS: We propose a new regression model on dynamically changing correlation matrices that provides better performance over existing windowed correlation and is more reliable for the modeling of dynamic connectivity.