SSVEP recognition by modeling brain activity using system identification based on Box-Jenkins model

• Published in: Computers in biology and medicine Vol. 101; pp. 82 - 89
• Main Authors: Safi, Seyed Mohammad Mehdi, Pooyan, Mohammad, Motie Nasrabadi, Ali
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.10.2018; Elsevier Limited
• Subjects: Accuracy; Autoregressive moving average; Autoregressive processes; Box-jenkins model (BJM); Brain; Brain-computer interface (BCI); Canonical correlation analysis (CCA); Computer applications; Correlation analysis; EEG; Electroencephalography; Fourier transforms; Human-computer interface; Implants; Least absolute shrinkage and selection operator (LASSO); Methods; Modelling; Multivariate linear regression (MLR); Noise; Pattern recognition; Recognition; Regression analysis; Signal processing; Statistical analysis; Steady-state visual evoked potential (SSVEP); Stimulation; System identification; Visual evoked potentials; Visual signals; Visual stimuli; Wavelet transforms; Brain-computer interface (BCI); Steady-state visual evoked potential (SSVEP); Multivariate linear regression (MLR); Least absolute shrinkage and selection operator (LASSO); Box-jenkins model (BJM); Canonical correlation analysis (CCA)
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2018.08.011

The steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has received increasing attention in recent years. The present study proposes a new method for recognition based on system identification. The method relies on modeling the electroencephalogram (EEG) signals using...