Canonical Correlation Analysis and Its Extension for SSVEP-based BCI Detection: A Systematic Review

Muhamad Agung Suhendra (Universitas Mandiri, Subang)
Iqbal Robiyana (Universitas Mandiri, Subang)
Tedi Sumardi (Universitas Mandiri, Subang)
Ahmad Sofyan Sulaeman (Universitas Mandiri, Subang)
Permono Adi Putro (Universitas Mandiri, Subang)
Nurizati Nurizati (Indo Global Mandiri University)
Usep Tatang Suryadi (Indo Global Mandiri University)
Anderias Eko Wijaya (Universitas Mandiri, Subang)
Sunanto Ajidarmo (Cakra Vimana Diinamycx)
Arief Budiman (Cakra Vimana Diinamycx)
M. Faizal Amri (National Research and Innovation Agency, Bandung)

Jurnal Penelitian Pendidikan IPA (JPPIPA)
Vol 10 No 12 (2024): December

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.