Browsing by Author "Vargas, Guilherme Vettorazzi"

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    Analysis of the spatiotemporal MVDR filter applied to BCI-SSVEP and a filter bank extension.
    (2022) Vargas, Guilherme Vettorazzi; Leite, Sarah Negreiros de Carvalho; Boccato, Levy
    Artifacts inevitably permeate brain signal acquisition by electroencephalography (EEG). Hence, brain-computer interfaces based on steady-state visually evoked potentials (BCI-SSVEP) frequently require a filtering to increase signal-to-noise ratio (SNR) in an attempt to improve its ability to identify a command selected by the user. By combining the signals from different electrodes, the spatiotemporal filtering technique based on the minimum variance distortionless response (MVDR) attenuates undesired frequency components while preserving the spectral content at the visual stimuli frequencies. In this study, we revisit the MVDR filter, further evaluating its behavior with respect to critical factors in a BCI-SSVEP system: proximity amid stimulation frequencies, number of stimuli and stimulation window-length. Additionally, the main parameters of the filter were also varied, such as the order and the number of electrodes to be combined. The experimental analysis confirmed the effectiveness of the MVDR filter for the majority of the scenarios. However, it also revealed a significant difficulty that the MVDR filter has when dealing with short-length time windows, especially when compared with classical filtering techniques, such as CAR and CCA. So, in order to mitigate this limitation, we propose a filter bank MVDR (FBMVDR), where each element is a MVDR filter designed to preserve a single stimulation frequency or harmonic components. This new approach provided an increase of more than 5% in relation to the standard MVDR, reaching a performance of 92.6% in scenarios with 4 visual stimuli and 1s window-length and achieved competitive results with the state-of-the-art technique filter bank CCA (FBCCA).
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    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 Faissol
    Brain-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.