Key result
Canonical Correlation Analysis and extensions provide gold-standard SSVEP recognition and EEG classification in brain-computer interfaces.
Why the study?
The study was conducted to identify analytical methods for steady-state visual evoked potential spellers.
Systematic Review (n=73)
CCA and its extensions are effective, gold-standard methods for SSVEP recognition and signal classification in EEG-based brain-computer interfaces.
Recommends CCA extensions for SSVEP-EEG classification in clinical BCIs; confirms gold-standard performance across aggregated evidence.
SSVEP-based Brain-Computer Interfaces (BCIs) utilize steady-state visual evoked potentials, which are brain responses triggered by visual stimuli flickering at specific frequencies. Users can focus on these stimuli, allowing the system to interpret their intent based on the brain's electrical activity. This technology has applications in communication for individuals with disabilities, gaming, and neuro-feedback, offering an ultimate means of interaction through thought alone. In this study, systematic literature review was conducted to identify analytical methods for SSVEP spellers with PRISMA method from the eligibility criteria. CCA and its extension become gold-standar method that give excellent performances for SSVEP recognition and signal classification. Some uniques features also found such as MsetCCA, FB-CCA, MF-CCA, TW-CCA, CP-CCA, IIS-CCA, TT-CCA and RLS-CCA. Therefore, we have various options for choosing the best method for recognizing SSVEP from EEG signals based BCI.
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Suhendra et al. (2024) conducted a systematic review in SSVEP-based Brain-Computer Interface (BCI) detection (n=73). Canonical Correlation Analysis (CCA) and its extensions was evaluated on Analytical methods for SSVEP spellers. Canonical Correlation Analysis (CCA) and its extensions serve as gold-standard methods providing excellent performance for SSVEP recognition and EEG signal classification in brain-computer interfaces.
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