While EEG allows for both good spatial and temporal resolution and has contributed to scientific knowledge about brain function, what is one confound of studying people with epilepsy and applying the findings to the general population?

Electroencephalography and functional magnetic resonance imaging EEG-fMRI

Explanation:

Brain activity data in epilepsy can be represented as a data that includes measures of electrophysiology, anatomy and behaviour. Every of these measures have a complex interaction depending upon the state of the brain e.g. rest, cognition, seizures and interictal periods.

Successful treatments in epilepsy depends on the sensitivity and specificity of methods used to examine the nature and site of underlying physiological pathological networks and understanding of the neurobiology of epilepsy.

Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI), by overriding some limitations of EEG in terms of sensitivity, can allow the drawing out of haemodynamic networks over the entire brain that triggered epileptic events by so doing providing localising information which will help in the treatment of epilepsy better.

Epilepsy is an important cause of amenable mortality but risk factors for death in epilepsy are not well understood.

Explanation:

Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy.

All seizure detection algorithms involve two main steps. First, appropriate quantitative values or features, such as EEG features, movements, or other biomarkers, must be computed from the data. Second, a threshold or model-based criteria must be applied to the features to determine the presence or absence of a seizure. This second step, called classification, might be as simple as thresholding a value or might require models derived from modern machine learning algorithms [51], [52]. Features are computed in a manner that is generally a compromise between the need for speed and the need for detection accuracy and might be preceded by a preprocessing or filtering step

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