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Python code explaining why Canonical Correlation Analysis (CCA) works in detecting Steady State Visually Evoked Potentials (SSVEP).

MIT license

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cca_tutorial_01.py		cca_tutorial_01.py
cca_tutorial_02.py		cca_tutorial_02.py

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ssvep_cca

Python code explaining why Canonical Correlation Analysis (CCA) works in detecting Steady State Visually Evoked Potentials (SSVEP).

Data

The data used in this repository is freely available here

Example

I used the dataset data_s19_64.mat and set the block ID to 2; frequency = 12 Hz; condition = low depth. The plot shows the comparison when using reference signals of 8 Hz, 12 Hz, and 16 Hz. As can be seen, despite the attempts to find linear combinations of a multi-channel EEG data for each of the reference signals, the target frequency (12 Hz) achieves the highest canonical correlation.