Feature: Filterbank Canonical Correlation Analysis (FBCCA)#107
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…to missing keepdims=True parameter.
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This PR relies upon features introduced in #105 and #106
This PR adds an implementation of a canonical-correlation (CCA) signal decoder for detection of periodic activity in multi-channel timeseries recordings. It is particularly useful for detecting the presence of steady-state evoked responses in multi-channel EEG data. Please see Lin et. al. 2007 for a description on the use of CCA to detect the presence of SSVEP in EEG data.
This implementation also includes the "Filterbank" extension of the CCA decoding approach which utilizes a filterbank to decompose input multi-channel EEG data into several frequency sub-bands; each of which is analyzed with CCA, then combined using a weighted sum; allowing CCA to more readily identify harmonic content in EEG data. Read more about this approach in Chen et. al. 2015.
Additionally, this feature includes a
StreamingFBCCAunit that decomposes the input multi-channel timeseries data into multiple sub-bands using aFilterbankDesignTransformer, then accumulates data usingWindowinto short-time observations for analysis using anFBCCATransformer.This PR is currently marked as draft because it could use some unit tests.