DEELT - Departamento de Engenharia Elétrica
URI permanente desta comunidadehttp://www.hml.repositorio.ufop.br/handle/123456789/5266
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2 resultados
Resultados da Pesquisa
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 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.