exact low-resolution brain electromagnetic tomography (eLORETA)

Classification of healthy subjects and Alzheimer's disease patients with dementia from cortical sources of resting state EEG rhythms. A study using artificial neural networks

Previous evidence showed a 75.5% best accuracy in the classification of 120 Alzheimer's disease (AD) patients with dementia and 100 matched normal elderly (Nold) subjects based on cortical source current density and linear lagged connectivity estimated by eLORETA freeware from resting state eyes-closed electroencephalographic (rsEEG) rhythms (Babiloni et al., 2016a). Specifically, that accuracy was reached using the ratio between occipital delta and alpha1 current density for a linear univariate classifier (receiver operating characteristic curves).

Frontal functional connectivity of electrocorticographic delta and theta rhythms during action execution versus action observation in humans

We have previously shown that in seven drug-resistant epilepsy patients, both reachinggrasping
of objects and the mere observation of those actions did desynchronize
subdural electrocorticographic (ECoG) alpha (8–13 Hz) and beta (14–30) rhythms as
a sign of cortical activation in primary somatosensory-motor, lateral premotor and
ventral prefrontal areas (Babiloni et al., 2016a). Furthermore, that desynchronization was
greater during action execution than during its observation. In the present exploratory