Signal processing and analysis

Using extracted features to classify or decode the user's intent. It is typically the next step after feature extraction, using algorithms to categorize features into meaningful classes for BCI applications.

Transforming time-domain signals into the frequency domain to analyse the signals' characteristics at different frequencies.

A statistical method used to separate a multivariate signal into its independent, constituent sources. In BCI, it is most commonly used for artifact removal, such as separating eye blinks and muscle noise from the underlying brain signals.

A neural coding scheme where information is encoded in the frequency or rate of neuronal action potentials.

Reconstruction of neural signal origins using inverse modeling techniques.

Computing the physical layout or spatial distribution of brain activity as measured by sensors

Observing how the amplitude or value of the neural signals changes as a function of time.

Dividing continuous neural data into short segments (windows) for localized or real-time analysis.