Abstract
Recently, neuroimaging technologies have been developed as important methods for assessing the brain condition of patients with disorders of consciousness (DOC). Among these technologies, resting-state electroencephalography (EEG) recording and analysis has been widely applied by clinicians due to its relatively low cost and convenience. EEG reflects the electrical activity of the underlying neurons, and it contains information regarding neuronal population oscillations, the information flow pathway, and neural activity networks. Some features derived from EEG signal processing methods have been proposed to describe the electrical features of the brain with DOC. The computation of these features is challenging for clinicians working to comprehend the corresponding physiological meanings and then to put them into clinical applications. This paper reviews studies that analyze spontaneous EEG of DOC, with the purpose of diagnosis, prognosis, and evaluation of brain interventions. It is expected that this review will promote our understanding of the EEG characteristics in DOC.
Highlights
Following severe damage to the brain, caused by trauma, stroke, or anoxia, patients may fall into a coma (1, 2)
The characteristics that have been applied in disorders of consciousness (DOC)-related studies could be generally classified into five categories: the spectrum, entropy, connectivity, the network, and the sleeping pattern
We summarize the primary features that are frequently used in DOC studies (Figure 1)
Summary
Following severe damage to the brain, caused by trauma, stroke, or anoxia, patients may fall into a coma (1, 2). EEG in DOC following severe brain injuries (2, 18) The active paradigms, such as motor imagery, require the active participation of patients. A study of coherence performed by Leon-Carrion et al showed significant differences in full bandwidth (delta, theta, alpha, and beta) in MCS patients with severe neurocognitive disorders (34). WSMI has demonstrated a dissociation with consciousness levels in DOC patients (36), and it was significantly lower in VS in theta and alpha bands (18). Connectivity and network parameters measured by dwPLI in delta and alpha bands provided valuable approaches to discriminate different consciousness levels in DOC patients (46). The occurrence of EEG patterns, including sleep spindles, slow wave activity, and the variability of brain rhythms (theta, alpha, and beta), were demonstrated to have significant correlations with the patients’ behavioral diagnoses (37).
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