Signal processing and analysis

An unwanted signal that is not related to the brain activity of interest.

The process of identifying and discarding segments of data that contain artifacts.

The process of identifying and eliminating unwanted signals that are not related to the brain activity of interest.

A statistical approach where signal values are predicted using weighted sums of prior values, useful for characterization of rhythms.

Probabilistic approach for updating the probability estimate for a hypothesis as more neural data becomes available.

A standard for organizing, annotating, and describing data collected during neuroimaging experiments.

widely used spatial filtering algorithm, especially in motor imagery BCIs. It is designed to find optimal projections of the EEG data to maximize the variance between two different mental tasks (e.g., imagining left vs. right-hand movement).

A distinguishable and separable neural signal source or pattern identified within the raw neural

A common challenge in BCI where the statistical properties of brain signals change over time (due to fatigue, electrode movement, etc.), which can degrade the performance of a pre-trained classifier.

Techniques used specifically to reduce background noise in neural data and improve signal quality.

A section of a time series that carries specific neural information.

Neural signals are divided into phased time segments.

Measurement of magnetic fields generated by brain activity, often used in MEG-based BCI systems.

Changes in the power spectrum of brain signals in response to an event or stimulus.

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.

The process of identifying and isolating relevant signals from brain activity data, transforming raw signals into a reduced set of features of interest.

A signal processing tool used to enhance the quality of the recorded brain signals. Filters work by allowing certain frequencies to pass through while reducing or eliminating others.

A mathematical measure used to describe the complexity or self-similarity of EEG or physiological signals.

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

Features based on the power or distribution of the signal across different frequency bands.