Substantial progress has been made over the past two decades in detecting, predicting and
promoting recovery of consciousness in patients with disorders of consciousness (DoC) …

Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …

Background and purpose Patients with acquired brain injury and acute or prolonged
disorders of consciousness (DoC) are challenging. Evidence to support diagnostic decisions …

Loss of consciousness is associated with the disruption of long-range thalamocortical and
corticocortical brain communication. We tested the hypothesis that deep brain stimulation …

Consciousness can be defined by two components: arousal (wakefulness) and awareness
(subjective experience). However, neurophysiological consciousness metrics able to …

Objective. Supervised learning paradigms are often limited by the amount of labeled data
that is available. This phenomenon is particularly problematic in clinically-relevant data …

Adopting the framework of brain dynamics as a cornerstone of human consciousness, we
determined whether dynamic signal coordination provides specific and generalizable …

The electroencephalogram (EEG) has great attraction in emotion recognition studies due to
its resistance to deceptive actions of humans. This is one of the most significant advantages …

Abstract Machine learning (ML) methods have the potential to automate clinical EEG
analysis. They can be categorized into feature-based (with handcrafted features), and end-to …

The analysis of spontaneous EEG activity and evoked potentials is a cornerstone of the
instrumental evaluation of patients with disorders of consciousness (DoC). The past few …