Glossary ======== This glossary defines CaliBrain-specific vocabulary together with general inverse-imaging and neuroimaging terms used throughout the documentation. .. glossary:: :sorted: aggregated calibration Calibration performed on source summaries that have been reduced across time rather than on the full source-by-time posterior output. adaptive_joint_learning A noise-variance workflow mode in which no fixed sensor-noise variance is supplied to the inverse solver. Instead, a joint-learning solver estimates the noise level during fitting. baseline noise variance Noise-variance estimate obtained from the pre-stimulus segment of the simulated sensor data. BMN Bayesian minimum norm. In CaliBrain, this refers to a minimum-norm-style Bayesian inverse solver that returns posterior means and covariance summaries under a common source-variance model. BMN_joint A BMN variant that can learn a common sensor-noise variance jointly with the source hyperparameter. calibration The process of comparing nominal uncertainty levels with empirical coverage and, when needed, learning a recalibration map. calibration curve A curve showing empirical coverage as a function of nominal coverage. coil type MNE/FIFF metadata describing the sensor hardware type, for example EEG electrodes or MEG magnetometers. credible interval An interval derived from a posterior distribution. In CaliBrain, scalar credible intervals are used for fixed-orientation uncertainty evaluation. credible set A posterior uncertainty region, such as an interval or ellipsoid, associated with a nominal coverage level. empirical coverage The observed fraction of cases in which the true source value or vector lies inside the nominal posterior credible set. EMD Earth mover's distance. In CaliBrain, this is used as a source-space distributional metric for comparing estimated and true source activity. EEG Electroencephalography. In CaliBrain, EEG can be treated in fixed orientation or in free orientation with three local source components. forward model The mapping from source activity to sensor measurements, represented by the leadfield. free orientation A source model in which each source location has multiple orientation components rather than a single fixed scalar coefficient. full_cov CaliBrain's name for the free-orientation uncertainty representation that uses local posterior covariance blocks to define multivariate ellipsoidal credible sets. gamma_map_sflex A sparse Bayesian inverse solver in CaliBrain based on Gamma-MAP and an sFLEX basis construction. gamma_lambda_map_sflex An sFLEX Gamma-MAP variant that jointly learns a noise-related regularization parameter. head Informal workflow term for one subject-specific or geometry-specific simulation context used when pooling or splitting calibration data. inverse problem The problem of recovering latent neural source activity from EEG/MEG sensor measurements. isotonic regression A monotone regression method used in CaliBrain to recalibrate nominal coverage levels while preserving ordering. leadfield The matrix or tensor that maps source amplitudes to sensor measurements. marginal CaliBrain's name for the free-orientation uncertainty representation that calibrates component-wise intervals using marginal variances only. MEG Magnetoencephalography. In CaliBrain, MEG can be represented in fixed orientation or in a reduced free-orientation form with tangential components. nominal coverage The target coverage level attached to a credible interval or credible set, for example 0.9 for a nominal 90% credible set. noise-variance strategy The rule used to provide or estimate the sensor-noise variance for source reconstruction. In CaliBrain, the main strategies are ``oracle``, ``baseline``, and ``adaptive_joint_learning``. oracle noise variance The true sensor-noise variance computed from the injected simulation noise. post_fixed A calibration workflow mode in which one recalibration map is fit at a reference condition and then reused across a sweep of evaluation conditions. post_oracle A calibration workflow mode in which recalibration is fit and evaluated on matched train and evaluation conditions. post_pooled A calibration workflow mode in which recalibration is fit on pooled training data and evaluated on a target condition. post_pooled_mismatch A calibration workflow mode in which recalibration is fit on pooled but intentionally mismatched training conditions and evaluated on the target condition. posterior covariance The covariance matrix returned by an inverse solver to quantify posterior uncertainty in source space or coefficient space. posterior mean The mean of the posterior distribution returned by an inverse solver and used as the point estimate of source activity. posterior summary The stored solver output used downstream in CaliBrain, typically including posterior mean, posterior covariance, and associated metadata. precal A workflow mode that evaluates raw empirical coverage without fitting a recalibration map. recalibration The post-hoc correction of nominal coverage levels using a fitted mapping such as isotonic regression. reduced free-orientation MEG A free-orientation MEG representation with two tangential components per source location. run manifest A tabular index of generated runs used to locate posterior summaries and their metadata in downstream workflow stages. sensor noise Additive noise at the sensor level, simulated in CaliBrain before source reconstruction. source activity Latent neural current amplitudes at source locations over time. source space The discrete set of candidate source locations used for inverse source imaging. source_estimator The high-level CaliBrain class that wraps an inverse solver and applies it to a leadfield and sensor data. uncertainty representation The geometric object used for calibration, such as a scalar interval, component-wise interval family, or local ellipsoid.