DEELT - Artigos publicados em periódicos
URI permanente para esta coleçãohttp://www.hml.repositorio.ufop.br/handle/123456789/5267
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Item Space-time filter for SSVEP brain-computer interface based on the minimum variance distortionless response.(2021) Leite, Sarah Negreiros de Carvalho; Vargas, Guilherme Vettorazzi; Costa, Thiago Bulhões da Silva; Leite, Harlei Miguel de Arruda; Coradine, Luis Cláudius; Boccato, Levy; Soriano, Diogo Coutinho; Attux, Romis Ribeiro de FaissolBrain-computer interfaces (BCI) based on steady-state visually evoked potentials (SSVEP) have been increasingly used in different applications, ranging from entertainment to rehabilitation. Filtering techniques are crucial to detect the SSVEP response since they can increase the accuracy of the system. Here, we present an analysis of a space-time filter based on the Minimum Variance Distortionless Response (MVDR). We have compared the performance of a BCISSVEP using the MVDR filter to other classical approaches: Common Average Reference (CAR) and Canonical Correlation Analysis (CCA). Moreover, we combined the CAR and MVDR techniques, totalling four filtering scenarios. Feature extraction was performed using Welch periodogram, Fast Fourier transform, and CCA (as extractor) with one and two harmonics. Feature selection was performed by forward wrappers, and a linear classifier was employed for discrimination. The main analyses were carried out over a database of ten volunteers, considering two cases: four and six visual stimuli. The results show that the BCI-SSVEP using the MVDR filter achieves the best performance among the analysed scenarios. Interestingly, the system’s accuracy using the MVDR filter is practically constant even when the number of visual stimuli was increased, whereas degradation was observed for the other techniques.Item Channel capacity in brain-computer interfaces.(2020) Costa, Thiago Bulhões da Silva; Suarez Uribe, Luisa Fernanda; Leite, Sarah Negreiros de Carvalho; Soriano, Diogo Coutinho; Castellano, Gabriela; Suyama, Ricardo; Attux, Romis Ribeiro de Faissol; Panazio, Cristiano MagalhãesObjective. Adapted from the concept of channel capacity, the information transfer rate (ITR) has been widely used to evaluate the performance of a brain–computer interface (BCI). However, its traditional formula considers the model of a discrete memoryless channel in which the transition matrix presents very particular symmetries. As an alternative to compute the ITR, this work indicates a more general closed-form expression—also based on that channel model, but with less restrictive assumptions—and, with the aid of a selection heuristic based on a wrapper algorithm, extends such formula to detect classes that deteriorate the operation of a BCI system. Approach. The benchmark is a steady-state visually evoked potential (SSVEP)-based BCI dataset with 40 frequencies/classes, in which two scenarios are tested: (1) our proposed formula is used and the classes are gradually evaluated in the order of the class labels provided with the dataset; and (2) the same formula is used but with the classes evaluated progressively by a wrapper algorithm. In both scenarios, the canonical correlation analysis (CCA) is the tool to detect SSVEPs. Main results. Before and after class selection using this alternative ITR, the average capacity among all subjects goes from 3.71 1.68 to 4.79 0.70 bits per symbol, with p -value <0.01, and, for a supposedly BCI-illiterate subject, her/his capacity goes from 1.53 to 3.90 bits per symbol. Significance. Besides indicating a consistent formula to compute ITR, this work provides an efficient method to perform channel assessment in the context of a BCI experiment and argues that such method can be used to study BCI illiteracy.Item Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs.(2015) Leite, Sarah Negreiros de Carvalho; Costa, Thiago Bulhões da Silva; Suarez Uribe, Luisa Fernanda; Soriano, Diogo Coutinho; Yared, Glauco Ferreira Gazel; Coradine, Luis Cláudius; Attux, Romis Ribeiro de FaissolBrain–computer interface (BCI) systems based on electroencephalography have been increasingly usedin different contexts, engendering applications from entertainment to rehabilitation in a non-invasiveframework. In this study, we perform a comparative analysis of different signal processing techniquesfor each BCI system stage concerning steady state visually evoked potentials (SSVEP), which includes: (1)feature extraction performed by different spectral methods (bank of filters, Welch’s method and the mag-nitude of the short-time Fourier transform); (2) feature selection by means of an incremental wrapper,a filter using Pearson’s method and a cluster measure based on the Davies–Bouldin index, in additionto a scenario with no selection strategy; (3) classification schemes using linear discriminant analysis(LDA), support vector machines (SVM) and extreme learning machines (ELM). The combination of suchmethodologies leads to a representative and helpful comparative overview of robustness and efficiency ofclassical strategies, in addition to the characterization of a relatively new classification approach (definedby ELM) applied to the BCI-SSVEP systems.