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

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.

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

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

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

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

  4. 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

  5. ICA

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

  7. CSP

  8. 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

  9. 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)

  10. AR models

  11. 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)

  12. 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)

  13. 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)

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

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