SSVEP Frequency Detection β€” Methods Comparison
Classical FFT  Β·  CCA  Β·  TRCA  β€”  Observe how each method handles noise, epoch length and number of channels
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πŸ“Š FFTClassical Fourier
β€”

βœ” Pros

  • Simple & computationally fast
  • No training data needed
  • Interpretable PSD visualization

✘ Cons

  • Single channel β€” no spatial filtering
  • Resolution Ξ”f = 1/T (needs long epochs)
  • Poor robustness at low SNR
  • Ignores harmonic structure
πŸ”— CCACanonical Correlation
β€”

βœ” Pros

  • Multi-channel β€” better SNR than FFT
  • Exploits harmonics (f, 2f, 3f…)
  • No training data required
  • Closed-form solution

✘ Cons

  • Reference signals are generic (not subject-specific)
  • No optimized spatial filter per subject
  • Still degrades at low SNR
🧠 TRCATask-Related Component
β€”

βœ” Pros

  • Subject-specific spatial filter
  • Maximises inter-trial reproducibility
  • Highest accuracy especially at low SNR
  • Works with short epochs

✘ Cons

  • Requires training trials per subject
  • More complex implementation
  • Sensitive to non-stationarities
  • Needs multiple EEG channels
Detection Accuracy vs SNR β€” Theoretical Comparison  (4-class Β· 25% chance level Β· typical literature values)
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