| Autor: |
Carvalho SN; Institute of Exact and Applied Sciences, Federal University of Ouro Preto, UFOP, Ouro Preto, Brazil. sarah@ufop.edu.br.; Brazilian Institute of Neuroscience and Neurotechnology, BRAINN, Campinas, Brazil. sarah@ufop.edu.br., Vargas GV; School of Computer and Electrical Engineering, University of Campinas, UNICAMP, Campinas, Brazil., da Silva Costa TB; Brazilian Institute of Neuroscience and Neurotechnology, BRAINN, Campinas, Brazil.; School of Computer and Electrical Engineering, University of Campinas, UNICAMP, Campinas, Brazil., de Arruda Leite HM; Institute of Exact and Applied Sciences, Federal University of Ouro Preto, UFOP, Ouro Preto, Brazil.; Brazilian Institute of Neuroscience and Neurotechnology, BRAINN, Campinas, Brazil., Coradine L; Institute of Computing, Federal University of Alagoas, UFAL, Maceió, Brazil., Boccato L; School of Computer and Electrical Engineering, University of Campinas, UNICAMP, Campinas, Brazil., Soriano DC; Brazilian Institute of Neuroscience and Neurotechnology, BRAINN, Campinas, Brazil.; Engineering, Modeling and Applied Social Sciences Center, Federal University of ABC, UFABC, Santo André, Brazil., Attux R; Brazilian Institute of Neuroscience and Neurotechnology, BRAINN, Campinas, Brazil.; School of Computer and Electrical Engineering, University of Campinas, UNICAMP, Campinas, Brazil. |
| Abstrakt: |
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 BCI-SSVEP 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. |
