A study on temporal segmentation strategies for extracting common spatial patterns for brain computer interfacing

Brain computer interfaces (BCI) create a new approach to human computer communication, allowing the user to control a system simply by performing mental tasks such as motor imagery. This paper proposes and analyses different strategies for time segmentation in extracting common spatial patterns of the brain signals associated to these tasks leading to an improvement of BCI performance

Wavelet design by means of multi-objective GAs for motor imagery EEG analysis

Wavelet-based analysis has been broadly used in the study of brain-computer interfaces (BCI), but in most cases these wavelet functions have not been designed taking into account the requirements of this field. In this study we propose a method to automatically generate wavelet-like functions by means of genetic algorithms. Results strongly indicate that it is possible to generate (evolve) wavelet functions that improve the classification accuracy compared to other well-known wavelets (e.g. Daubechies and Coiflets)

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection

Background: Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. Methods: This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. Results and conclusion: The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed

An experimental DUAL model of advanced liver damage

Individuals exhibiting an intermediate alcohol drinking pattern in conjunction with signs of metabolic risk present clinical features of both alcohol-associated and metabolic-associated fatty liver diseases. However, such combination remains an unexplored area of great interest, given the increasing number of patients affected. In the present study, we aimed to develop a preclinical DUAL (alcohol-associated liver disease plus metabolic-associated fatty liver disease) model in mice. C57BL/6 mice received 10% vol/vol alcohol in sweetened drinking water in combination with a Western diet for 10, 23, and 52 weeks (DUAL model). Animals fed with DUAL diet elicited a significant increase in body mass index accompanied by a pronounced hypertrophy of adipocytes, hypercholesterolemia, and hyperglycemia. Significant liver damage was characterized by elevated plasma alanine aminotransferase and lactate dehydrogenase levels, extensive hepatomegaly, hepatocyte enlargement, ballooning, steatosis, hepatic cell death, and compensatory proliferation. Notably, DUAL animals developed lobular inflammation and advanced hepatic fibrosis. Sequentially, bridging cirrhotic changes were frequently observed after 12 months. Bulk RNA-sequencing analysis indicated that dysregulated molecular pathways in DUAL mice were similar to those of patients with steatohepatitis. Conclusion: Our DUAL model is characterized by obesity, glucose intolerance, liver damage, prominent steatohepatitis and fibrosis, as well as inflammation and fibrosis in white adipose tissue. Altogether, the DUAL model mimics all histological, metabolic, and transcriptomic gene signatures of human advanced steatohepatitis, and therefore serves as a preclinical tool for the development of therapeutic targets

Extracting common spatial patterns based on wavelet lifting for brain computer interface design

Brain computer interfacing (BCI) offers the possibility to interact with machines uniquely relying on the user's thoughts. Although wavelet analysis has been used in the BCI field there is evidence that standard wavelet families, such as Daubechies, may not be the optimal approach. In this study, we developed a novel wavelet lifting scheme, specifically for BCI design. The lifting transform in this new approach is based on common spatial patterns (CSP), which allows to exploit the signal characteristics in temporal, spectral and spatial domains simultaneously. Experimental results show that in BCI applications the new wavelet outperforms several first generation wavelet families in terms of classification accuracy and resource consumption

Multiresolution analysis of an information based EEG graph representation for motor imagery brain computer interfaces

Brain computer interfaces are control systems that allow the interaction with electronic devices by analysing the user's brain activity. The analysis of brain signals, more concretely, electroencephalographic data, represents a big challenge due to its noisy and low amplitude nature. Many researchers in the field have applied wavelet transform in order to leverage the signal analysis benefiting from its temporal and spectral capabilities. In this study we make use of the so-called second generation wavelets to extract features from temporal, spatial and spectral domains. The complete multiresolution analysis operates over an enhanced graph representation of motor imaginary trials, which uses per-subject knowledge to optimise the spatial links among the electrodes and to improve the filter design. As a result we obtain a novel method that improves the performance of classifying different imaginary limb movements without compromising the low computational resources used by lifting transform over graphs