Feature Selection in BCIs (section 5 and 6 of Review paper)

PCA PCA – unsupervised dimensionality reduction (by removing correlations between variables) + simple deterministic algorithm (using eigendecomposition of the covariance matrix of the data)

Dimensionality Reduction PCA PCA – unsupervised dimensionality reduction (by removing correlations between variables) + simple deterministic algorithm (using eigendecomposition of the covariance matrix of the data)

ICA ICA – unsupervised method for decomposing signal into (maximally) independent components - iterative algorithm dependent on initial processing + good for removing artifacts + some versions are very computationally expensive

ICA

CSP CSP finds filters that maximize projected variance for one class (and minimize for the other) and vice-versa (Supervised algorithm, deterministic eigenanalysis algorithm)

CSP

Representing Signal Can represent as temporal samples (time-domain signal) Can represent in the spectral domain (power in different frequency bands) Can represent in a hybrid way

AR models Autoregressive (AR) models are good for representing signals that have a spectrum that is smooth with some peaks (as is often seen in EEG)

AR models

MF approach Looks for specific signal (to match predetermined signal (or template)). Knowing what you are looking for helps a lot. Look at correlation between signal and filters (good for SSVEP – match frequency (and phase) of flashing stimuli)

Wavelet Transforms Wavelet transforms provide temporal and frequency information at multiple resolutions (with FFT you have to trade off temporal resolution with frequency resolution based on your temporal window size) (basically run a matched filter algorithm at many shifts and scales of a wavelet function)

Artifacts in BCIs EMG - high frequency from muscle activity EOG (eye movements) - caused by voltage difference between the cornea and retina low frequency for sustained gaze changes, high frequency for blinks EKG (heart activity) – at heart rate Avoid as much as possible and can remove manually or automatically especially if monitored separately (or using ICA)

Artifacts in BCIs Hybrid BCIs can make use of non-brain signals to give better performance in those that can make muscle/eye movements