Signal processing and analysis
45 termsStatistical dependencies (e.g., PLV, coherence) between brain regions.
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 recursive algorithm for estimating and predicting noisy time-series signals, suitable for smooth BCI control.
The method by which neurons encode and process information. It considers neurons as information-processing channels that take incoming information, integrate it, and produce a signal encoded in the neuronal electrical activity pattern.
The process of interpreting the electrical signals generated by the brain's neurons and translating them into meaningful information.
Identifying rare or abnormal neural patterns that may distort analysis or signal classification.
The amount of time overlap between two events.
A measure that quantifies phase synchrony or the consistency of the phase difference between two signals, specifically designed to be invariant to common sources.
A measure that quantifies the consistency of the phase difference between two signals over time.
Sequential operations for filtering, feature extraction, and artifact removal in neural data.
Initial set of techniques used to clean and standardize raw neural signals before advanced analysis (e.g., filtering, normalization).
A neural coding scheme where information is encoded in the frequency or rate of neuronal action potentials.
The first stage of the BCI pipeline, which involves measuring the brain's raw physiological signals using a specific sensor modality.[3, 45] This process includes amplifying the very weak neural signals to a usable level, converting them from analog to digital form, and transmitting them to a computer for processing.[25]
The transformation, extraction, and classification of brain signals to interpret the user's intent and control an external device.
Reconstruction of neural signal origins using inverse modeling techniques.
Computing the physical layout or spatial distribution of brain activity as measured by sensors
Technique to enhance or isolate neural signals from specific brain regions, such as Laplacian or CSP filters.
A measure of the average firing rate of a neuron in a specific time interval
Directional, causal influence among neural signals estimated by models like Granger causality.
Observing how the amplitude or value of the neural signals changes as a function of time.