3,754 research outputs found
Mapping the epileptic brain with EEG dynamical connectivity: established methods and novel approaches
Several algorithms rooted in statistical physics, mathematics and machine learning are used to analyze neuroimaging data from patients suffering from epilepsy, with the main goals of localizing the brain region where the seizure originates from and of detecting upcoming seizure activity in order to trigger therapeutic neurostimulation devices. Some of these methods explore the dynamical connections between brain regions, exploiting the high temporal resolution of the electroencephalographic signals recorded at the scalp or directly from the cortical surface or in deeper brain areas. In this paper we describe this specific class of algorithms and their clinical application, by reviewing the state of the art and reporting their application on EEG data from an epileptic patient
Detecting and tracking time-varying causality with applications to EEG data
This paper introduces a novel method called the ERR-Causality, or Error Reduction Ratio Causality test, that can be used to detect and track causal relationships
between two signals using a new adaptive forward
orthogonal least squares (Adaptive-Forward-OLS) algorithm.
In comparison to the traditional Granger method,
one advantage of the new ERR-Causality test is that it
can effectively detect the time-varying direction of linear
or nonlinear causality between two signals without fitting
a complete model. Another important advantage is that
the ERR-Causality test can detect both the direction of
interactions and estimate the relative time shift between
the two signals. Several numerical examples are provided
to illustrate the effectiveness of the new method for causal
relationship detection between two signals. An important
real application, relating to the analysis of the causality
of EEG signals from different cortical sites which can be
very useful for understanding brain activity during an
epileptic seizure by inspecting the high-resolution time varying directed information flow, is also discussed
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