Introduction

A disorder of consciousness (DOC) is an altered consciousness state generally caused by injury or dysfunction of the neural systems regulating arousal and awareness [1, 2]. When patients fall into coma after brain injury, they show an absence both of arousal (i.e., eye opening even when stimulated) and awareness (unaware of themselves and the environment) [3]. After the transition from coma, some patients remain unresponsive to external stimulation (or only show simple reflex movements that are uncorrelated with command), and they will be diagnosed as being in a vegetative state/unresponsive wakefulness syndrome (VS/UWS) [4]. Moreover, patients who show fluctuating but definite behavioral evidence of self or environmental awareness can be diagnosed as in minimally conscious state (MCS) [5]. The current ‘gold standard’ for establishing a diagnosis of VS/UWS or MCS is still behavioral scales like the JFK Coma Recovery Scale—Revised (CRS-R) [6]. However, it is well accepted that this scale has significant limitations [7]. Due to fluctuations of consciousness and fatiguability of the patients and subjective interpretation, assessments based exclusively on behavioral scales can lead to misdiagnosis rate as high as 40% [8].

Clinicians and neuroscientists now increasingly often seek to obtain information of brain function from neuroimaging or electrophysiological measurements for a more precise assessment of DOC patients. Compared with neuroimaging techniques, electroencephalogram (EEG) recordings are a more widely applicable, less expensive, and more practical technique for application in DOC patients. Many EEG studies in DOC patients have been conducted already and provided valuable information for clinicians to improve the diagnosis, monitoring and prognosis of these patients. However, the EEG paradigms, analysis algorithms and feature extraction are complex. Many EEG-based characteristics have been proposed to describe the pathophysiology underlying DOCs. They have enriched clinical methods for DOC assessment, but at the same time, they have confused clinicians when it comes to selecting the appropriate ones in practice. The purpose of this systematic review is to classify the EEG literature in DOC patients according to protocols, analysis methods and clinical utility.

Literature sources

English-language articles were searched in PubMed, Medline and Web of Science databases published up to 15 May 2020. Key search terms were ‘disorders of consciousness; vegetative state; unresponsive wakefulness syndrome and minimally conscious state’ combined with ‘electroencephalogram; event-related potentials; brain–computer interfaces; sleep; EEG reactivity; TMS-EEG’. A total of 753 articles were identified. Title and abstract of the retrieved articles were then screened for the following inclusion criteria: (1) original peer-reviewed article; (2) subjects diagnosed as DOC; (3) application of EEG measurement and analysis. Exclusion criteria were: (1) review and guideline articles, and conference proceedings and abstracts; (2) studies on children, acute comatose state, or without explicit EEG results. The retained 119 articles provide the basis for this review and were classified according to EEG measures, paradigms, analysis methods and clinical utility.

Results

EEG measurements include spontaneous (resting-state EEG: Tables 1, 2, 3, and sleep EEG: Table 4) and evoked EEG (event-related potentials, brain-computer interfaces: Table 5, EEG-reactivity and transcranial magnetic stimulation evoked EEG responses: Table 6). The different paradigms, EEG characteristics and their potential clinical utility are summarized in Fig. 1.

Table 1 EEG spectral power measurements in disorders of consciousness
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Table 2 Nonlinear EEG measurements in disorders of consciousness patients
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Table 3 EEG networks in disorders of consciousness
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Table 4 EEG sleep patterns in disorders of consciousness
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Table 5 Evoked EEG potential measurements in disorders of consciousness
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Table 6 TMS-EEG measurements in disorders of consciousness
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Fig. 1
figure 1

Main EEG measures obtained in patients with disorders of consciousness: rest-state EEG, sleep EEG, event-related potentials (ERP), brain-computer interface (BCI) and transcranial magnetic stimulation–electroencephalography (TMS-EEG). These measures provide various EEG characteristics based on different information extraction algorithms. The right panel summarizes the main EEG characteristics with respect to their potential values in classifying minimally conscious state (MCS) and vegetative state/unresponsive wakefulness syndrome (VS/UWS) (diagnostic value), outcomes prediction (prognostic value) and treatment monitoring (response to treatment). The colors identify EEG measures belonging to the main EEG techniques as indicated by the same colors in the left panel. ± in the ‘diagnostic value’ column indicate that EEG measures are larger (+)/smaller (–) in MCS than in VS/UWS. ± in the ‘prognostic value’ column indicate that larger EEG measures correspond to better (+)/worse (–) outcome. Finally, ± in the ‘response to treatment’ column means that EEG measures increased/decreased during treatment. KCC Kolmogorov–Chaitin complexity, wSMI weighted symbolic mutual information, dwPLI debiased weighted phase lag index, STEn symbolic transfer entropy, LPC late positive components, VMI visuomotor integration, VEP visual evoked potentials, MMN mismatch negativity, TEP TMS evoked potentials, GMFP global mean field power, PCI perturbational complexity index

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Spontaneous electroencephalogram

Resting state

Spectral power

Spontaneous EEG oscillations can be divided into several frequency bands, which are generally as follows: delta (1.5–3.5 Hz), theta (3.5–7.5 Hz), alpha (7.5–12.5 Hz), beta (12.5–30 Hz) and gamma (> 30 Hz), according to their specific roles in different brain functions [9, 10]. The power of these frequency bands is typically highly abnormal in DOC patients (Table 1). Enhanced delta and suppressed alpha activities have been the highlighted markers of low consciousness level [11]. The delta power in DOC patients is higher than in healthy controls (HC) [12, 13], and in MCS patients [14]. Spectral power is dominated by the delta band in nearly 80% of VS/UWS patients [15, 16]. In addition, power in the theta band is increased in VS/UWS patients compared with HC [12] and in MCS patients compared with HC or conscious patients [17]. However, MCS patients show higher theta power compared with VS/UWS patients [14].

The depression of alpha power in VS/UWS patients is much stronger than in MCS patients [18]. The alpha power in MCS patients is significantly higher than in VS/UWS patients [19, 20], sometimes even twice as high [15]. Alpha power is higher in the central, parietal and occipital regions in MCS patients compared with VS/UWS patients [11, 18]. Residual consciousness of DOC patients tightly correlates with alpha power in the frontal and parietal networks [16, 21]. Furthermore, increase of alpha power over time is associated with the chance of consciousness recovery [19, 22, 23].

High-frequency bands (beta and gamma) have been rarely considered in DOC studies. One study found significantly higher low beta (12–17.75 Hz) power in MCS compared with VS/UWS [14], but in the whole beta band, normalized beta power does not discriminate between MCS and VS/UWS [13]. Gamma power in DOC patients is lower than in HC [18]. Gamma activity has a critical role in assessing DOC patients’ responses to specific brain stimulation: transcranial alternating current stimulation [24], transcranial direct current stimulation [25] and spinal cord stimulation [26] can all significantly increase gamma power.

Quantified indexes based on spectral power in more than one frequency band are often used for diagnosis, treatment monitoring and outcome prediction of DOC patients. Ratios between lower frequencies (delta and theta) and higher frequencies (alpha and beta) show close association with regional glucose metabolism in MCS but not VS/UWS [27]. Moreover, there is a positive correlation between the power ratio (above 8 to below 8 Hz) and the CRS-R score, and the peak frequency of the spectrum is also strongly related to the CRS-R score [12].

Non-linear dynamics

Non-linearity in the time domain

In time domain analyses, VS/UWS patients always show the lowest EEG complexity, while MCS patients have higher but still moderately lower complexity than HC [28]. Approximate entropy (ApEn) is one of the frequently used non-linear indices for capturing consciousness-related EEG complexity. The ApEn values of VS/UWS patients are commonly lower than in HC (sensitivity: 95%, specificity: 100%) [29]. ApEn values in VS/UWS are also lower than in MCS over a broad frequency band [19], which is consistent with the Lempel–Ziv complexity analysis [28]. In addition, ApEn and Lempel–Ziv complexity are closely associated with outcome [28]. Most patients with the lowest ApEn values die or remain in persistent VS/UWS at 6-month follow-up, while patients with higher ApEn values are either in MCS, or partially or completely recovered [29].

Permutation entropy is effective in differentiating MCS from VS/UWS [13, 19]. A comparative study on EEG markers, including three non-linear indices, showed that only permutation entropy and Kolmogorov–Chaitin complexity significantly differentiate between VS/UWS and MCS [13]. Consistently, permutation entropy is as one of the top ranking among over 20 potential biomarkers in the classification of different consciousness states of DOC patients [30]. Permutation entropy is also a sensitive index responding to brain stimulation treatment [31]. Moreover, permutation entropy in the delta and theta bands shows better performance than ApEn as a prognostic index in predicting outcome of DOC patients [19] (Table 2).

Non-linearity in the frequency domain

Spectral entropy is a non-linear quantitative parameter of specific spectral EEG powers. VS/UWS patients have lower spectral entropy values than MCS patients and fully conscious patients [13]. MCS exhibits periodic (over 70 min) fluctuations of spectral entropy similar to HC. In contrast, VS/UWS does not show any distinct periodic fluctuations [14]. State entropy, reaction entropy and bispectral index (BIS), which were used in detecting the depth of anesthesia, are also effective in distinguishing VS/UWS from MCS with sensitivity of 89% and specificity of 90% [32] (Table 2). The state entropy values of VS/UWS and MCS are 49% and 18% lower, respectively, than of HC. Consistently, lower consciousness levels correspond to lower BIS in DOC patients [33]. Moreover, BIS correlates positively with the CRS-R, and the 1-year outcome of DOC patients [33].

Brain networks

Functional connectivity

DOC patients with a better state of consciousness tend to show stronger functional connectivity among brain regions [34, 35] (Table 3). The prefrontal cortex plays a crucial role in the formation of consciousness [36, 37]. Disconnection between the frontal and parietal and occipital regions discriminates between MCS patients and individuals with severe neurocognitive disorders [38]. MCS has a higher phase lag index and imaginary coherence between the frontal and posterior regions than VS/UWS [20]. Coherence between the frontal and occipital regions in the ipsilesional hemisphere is lower than in the contralesional hemisphere in VS/UWS [39]. Symbolic transfer entropy analysis also shows that the feedback interaction between the frontal-parietal and frontal–temporal regions is particularly affected in DOC patients [40]. MCS patients show less connectivity between temporal and parietal-occipital regions than patients with severe neurocognitive disorders [41].

Furthermore, MCS and VS/UWS show significantly abnormal connectivity dynamics, especially in the frontotemporal, frontocentral and centro-parietooccipital regions [15]. Connectivity measured by weighted symbolic mutual information increases over central-posterior regions with increasing levels of consciousness [42].

Connectivity measured in theta, alpha and gamma bands shows significance in distinguishing consciousness levels. Compared with VS/UWS, MCS has a higher phase lag index in the alpha band [20], an increasing debiased estimator of the squared weighted phase lag index in the gamma band, and a lower coherence in the theta band [15, 35]. In addition to traditional connectivity measurements, bicoherence measures the quadratic phase coupling characteristics of the EEG oscillations within the same sources. Quadratic phase self-coupling in the delta, theta and alpha bands is closely correlated with the CRS-R score [43].

Functional connectivity measurement also showed utility in the outcome prediction of DOC patients. In a 3-month prognostic study, the number and intensity of cortical functional connections of recovery-conscious patients were higher than those of unrecovered patients [44]. The residual coherence in the parietal and frontoparietal regions provides strong evidence for early recovery of VS/UWS, with a high predictive sensitivity and specificity (parietal coherence at theta: sensitivity = 73%, specificity = 79%; frontoparietal coherence at alpha: sensitivity = 64%, specificity = 77%) [45]. Symbolic transfer entropy in the delta and alpha bands also shows a significant prognostic effect [19]. In addition, lower frontal quadratic phase self-coupling in the theta band indicates increased probability of consciousness recovery [43].

Some treatments modify the connectivity properties in DOC patients. MCS vs. VS/UWS patients show significantly different responses of frontoparietal connectivity induced by transcranial direct current stimulation or transcranial alternating current stimulation [24, 25, 46, 47]. Coherence of the prefrontal cortex can be modified by spinal cord stimulation [26]. And parieto-occipital connectivity can be modified by transcranial direct current stimulation [23]. Increasing spatial coherence within one cerebral hemisphere and between the hemispheres is also considered as a critical predictor of DOC patients responding to zolpidem [48].

Graph theory analysis

Graph theory analysis provides an approach to quantify the features of functional neural networks. Brain networks of DOC patients show the characteristics of impairment of global information processing (network integration) and increase of local information processing (network isolation) compared with HC [49, 50]. With a decreased consciousness level, the integration of large-scale brain function networks decreases [49], but at the same time, segmentation and local efficiency increase [51]. Path length and clustering coefficient can successfully distinguish MCS from VS/UWS [19]. DOC patients show significantly higher local efficiency, lower modularization and higher centrality in the delta and theta band networks compared with HC [16]. Furthermore, patients misdiagnosed as VS/UWS have a similarly powerful frontal lobe brain network as MCS patients: the participation coefficient can define the existence of a hub node in the alpha network, and it is possible to clarify whether a given patient was misdiagnosed as VS/UWS based on clinical consensus [52]. Finally, a support vector machine (SVM) trained by alpha participation coefficient and delta band power could diagnose VS/UWS, MCS minus, MCS plus with 74%, 100% and 71% accuracy. And a SVM trained by delta modularity and clustering coefficient was able to predict outcomes of VS/UWS and MCS with accuracy of 80% and 87%, respectively [52].

Microstates

Reduction of the number of EEG microstate types is associated with an altered state of consciousness (Table 3). The lack of awareness may be due to the lack of diversity of microstates in the alpha band. This highlights the importance of the fast alpha-rhythm microstate in the formation of consciousness, and also demonstrates that probability and duration of delta, theta and slow-alpha microstates are associated with unconsciousness [53]. Also, the percentage time of alpha-rhythm microstates can distinguish MCS and VS/UWS [19].

Sleep patterns

The sequential alternation of sleep stages, the presence of sleep cycles, and characteristic sleep-stage dependent EEG patterns such as sleep spindles or slow-wave oscillations are always found in healthy subjects [54]. However, they are disturbed in DOC patients (Table 4). DOC patients do not show systematic variance of sleep spindles and slow-wave oscillations between day and night [55]. Moreover, a reduction in the build-up of slow-wave oscillations during sleep over parietal brain areas is commonly found in DOC patients, which is considered a characteristic topographical marker for network dysfunction [56].

Whether sleep EEG patterns are evident in VS/UWS patients is still unclear and a matter of ongoing debate. Some studies have suggested that most VS/UWS patients retain sleep behavior but do not have sleep EEG patterns (slow-wave/rapid eye movement (REM) sleep phases or synchronized regulation of slow-wave activity) associated with night-time sleep [57]. However, several other studies have suggested that VS/UWS patients can exhibit preserved sleep behaviors and EEG sleep patterns [58, 59]. Several sleep EEG components, such as spindles, slow-wave oscillations, and REM sleep are preserved in some VS/UWS patients [60,61,62]. Slow-wave sleep characteristics were demonstrated as the main factor that significantly correlates with the CRS-R score [63] and distinguishes between conscious patients (MCS and lock-in syndrome [LIS]) and VS/UWS [64, 65]. Some MCS patients have alternating non-REM (NREM)/REM sleep patterns with isotactic reduction in their night-time EEG [57], which is predictive of good recovery. A machine learning study showed that MCS patients are more likely to wake up during the day and have more complex patterns of sleep–wake stages at night, while VS/UWS patients do not show any accumulation of specific conditions during the day or night [66]. In addition, the number of sleep spindles and K-complexes in DOC patients increases with consciousness recovery over a 6-month follow-up [61].

Evoked electroencephalogram

Event-related potentials

Auditory evoked potentials

The majority of MCS and, to a lesser extent, VS/UWS have preserved cortical responses under auditory stimulation [67, 68] (Table 5). The most commonly used auditory paradigm is the oddball paradigm, which usually applies standard stimulation with a 1-kHz tone and deviation stimulation with an 800 Hz, 1200 Hz or 2 kHz tone with a period of 50–100 ms. However, due to the low levels of consciousness and cognitive ability, pure tones may not elicit any differential responses among DOC patients of different severity. When adding more cognitive content in auditory stimulation, this can provide valuable information for detecting covert cognitive ability of behaviorally unresponsive patients. In DOC studies, the oddball experiment is usually designed with a combination of the subject’s own name (SON) vs. other first name (OFN) [69].

The P300 in auditory stimulation paradigms is one of main components in DOC research. It could be detected in some MCS patients, but is less evident or absent in VS/UWS [70]. In the SON vs. OFN paradigm, both MCS and VS/UWS show reduced modulation of spectral activity in the delta band, which indicates dissociation in the P300-related neural networks [71]. When comparing active (count SON or OFN) and passive (just listen) task conditions, P300 of some MCS patients was higher in the active than passive conditions, suggesting their capability of voluntarily complying with task instructions like HC [72]. In contrast, no P300 differences between passive and active conditions were observed in VS/UWS. In addition, the novelty P300 in active task had a wider spatial distribution than during passive listening in MCS. There was no such effect in VS/UWS [73]. Furthermore, P300 also shows prognostic value in DOC. Patients with a present P300 in some modified paradigms, such as using chord music, or calling names to the music background, tended to have better outcomes [74]. And the P300 latency, which may represent an objective index of higher-order processing integration, could predict recovery of consciousness from VS/UWS to MCS [75]. However, absence of the P300 does not exclude a possible recovery of patients.

N400 is absent in the majority of DOC patients [76]. It does not reliably distinguish VS/UWS from MCS (in three levels: listening, cognition and speech) [77]. Both MCS and VS/UWS who have a preserved N400 show increased N400 peaks and amplitudes in the middle of the forehead during inconsistent speech stimulation, but the latency of N400 in VS/UWS is longer than that in MCS [78]. The relationship of N400 with the outcome of DOC patients is noticeable: In a clinical follow-up of 53 VS/UWS and 39 MCS, patients with preserved N400 had a highly significant relationship with the recovery of their communication ability (in MCS: sensitivity = 40%, specificity = 100%; in VS/UWS: sensitivity = 60%, specificity = 97%) [79]. The highest probability of recovery (97%) is observed for MCS with both detectable N400 and P300. In contrast, the lowest recovery chance (around 10%) is found for VS/UWS with neither a N400 nor a P300 [80]. Another study confirmed that presence of N400 is a predictive marker of communication recovery with sensitivity of 50% and specificity of 90% [81].

Moreover, some other cortical responses could also be evoked by auditory stimulation in some DOC patients, including mismatch negativity (MMN) [13, 67], late positive components [81] and lateralized readiness potential [82]. Late positive components and lateralized readiness potential were demonstrated as only existing in some MCS but not VS/UWS, which may be valuable in DOC diagnosis.

Visual evoked potentials

The flash visual evoked potential (VEP) is generated when subjects observe moving balls or objects. Compared with conscious states, the VEP amplitude in DOC patients is smaller and latency is longer. Initial VEP delays have important values in predicting the long-term prognosis (up to 3 years) [83]. However, as flash VEP requires active cooperation from patients, it is almost always difficult to be properly conducted in DOC patients. Some researchers use a novel approach to address this problem by employing an associative stimulation protocol combining transcranial magnetic stimulation (TMS) with visual stimulation through transorbital alternating current stimulation. This can serve to evaluate the visuomotor integration (VMI) and visual P300 patterns in DOC patients. MCS patients exhibit preserved patterns of VMI and P300, whereas nearly all VS/UWS patients did not show significant VMI [84].

Brain–computer interfaces

Based on an external stimulus, a brain-computer interface (BCI) system directly detects brain responses rather than being limited to the subjective observation of patients and, therefore, increases objectivity and accuracy of conscious expression [85] (Table 5). Thus, BCI are used to assist clinical evaluation, especially CRS-R assessment, to obtain more accurate diagnoses. For example, for the auditory scare item in CRS-R, background noise was taken as the standard stimulus and clapping as the new stimulus. Traditionally, an assessor gives scores based on patients’ behavioral responses. However, by comparing the results of CRS-R and BCI, it was found that some patients cannot respond to the stimulus behaviorally but respond using an auditory BCI system (significant response waveforms following a loud clapping sound) [86, 87]. Similarly, in a visual BCI study some patients show significant evoked waveforms following commands of visual fixation, but fail to get a visual fixation score in CRS-R [88]. In addition, the visual BCI system proved to have better visual tracking ability than behavioral assessment in tasks of following a moving picture or identifying pictures of specific individuals [89, 90]. When using more complex cognitive tasks, BCI is sensitive to the patients’ covert consciousness expression. BCI using happy/sad emotional images shows that some DOC patients with insufficient behavioral response had significant emotional cognitive processing in the BCI system [91]. Moreover, some patients whose consciousness was detected by BCI but not behavioral assessment later have improved communication scores in their CRS-R [92]. Moreover, a gaze-independent BCI system combining auditory and visual inputs outperforms unimodal auditory-only and visual-only BCI systems. Multiple event-related potential (ERP) components, including the P300, N400 and late positive complex, could be observed only using the combined audio-visual BCI system [93]. Most patients, who respond in the audio-visual system, i.e., achieve statistically significant BCI accuracy, regain consciousness 3 months later [94].

In addition to the auditory and visual pathways, the somatosensory pathway shows important value in the DOC-BCI approach. BCI using vibratory tactile stimulation successfully identified patients without behavioral command-following but ‘covert command-following’ in the BCI system [95]. The patient, who responded in BCI showed a higher cerebral glucose metabolism within the language network indicative of MCS compared to other patients without responses in BCI [96]. The somatosensory discrimination BCI system also identified four VS/UWS patients who had clear task-following. The efficacy of somatosensory discrimination strongly correlates with the 6-month outcome [97]. This highlights the values of a somatosensory pathway-based BCI system in detecting ‘hidden command-following’ in patients with lack of behavioral response. It is also promising as an auxiliary method for predicting the clinical outcome of DOC patients. When using motor imagery but not sensory stimulation, some MCS patients show capacity to operate a simple BCI-based communication system, even without any detectable volitional control of movement [98]. Three of 16 VS/UWS patients had repeatedly and reliably appropriate EEG responses to commands in a motor-imagery-based BCI [99]. In addition, most patients with cognitive-motor dissociation show evidence of following different motor imagery commands, which indicates the possibility of re-establishing communication by imagery-based BCI for such patients [100].

Electroencephalogram reactivity

EEG reactivity refers to changes of the brain electrical rhythm (frequency of EEG becoming faster or slower) or amplitude (increase or decrease) in response to external stimulation [101]. In DOC patients, EEG reactivity is more likely to occur in patients with a higher CRS-R [102]. EEG reactivity to intermittent photic stimulation and auditory stimulation is significantly higher in MCS than in VS/UWS. The preserved EEG reactivity evoked by eye opening/closing, auditory stimulation and intermittent photic stimulation has a high diagnostic specificity for the identification of patients with MCS vs. VS/UWS, while EEG reactivity to pain and tactile stimuli does not differentiate between MCS and VS/UWS [103].

Transcranial magnetic stimulation-electroencephalogram

Since transcranial magnetic stimulation-electroencephalography (TMS-EEG) does not rely on the integrity of motor pathways (in contrast to recording TMS responses by electromyography from muscle) and does not require any active participation from the patient, TMS-EEG is now considered one of the most promising techniques in the diagnosis of DOC patients (Table 6).

The first TMS-EEG work in classifying DOC patients and tracking consciousness recovery was conducted in 2012 [104]. Similar to the findings from sleep and anesthesia research [105, 106], a breakdown of effective connectivity, i.e., a strongly reduced propagation of the initial EEG response at the site of stimulation throughout the brain, was also found in DOC patients. Moreover, the TMS evoked EEG potentials (TEPs) are significantly different between MCS and VS/UWS. MCS exhibits low-amplitude but high-frequency TEPs that propagate widely to distant cortical regions. In contrast, VS/UWS shows TEP waveforms similar to those observed in NREM sleep and deep anesthesia in HC, i.e. high-amplitude local EEG responses of low waveform complexity that do not propagate [104]. Another study confirmed that the TMS evoked cortical reactivity and connectivity are largely preserved in most MCS patients but not in VS/UWS patients [107]. These findings corroborate the notion that MCS patients retain extensive cortico-cortical connections for communication, which are seriously suppressed in VS/UWS patients. One proposition is that cortical circuits fall into silence and then further impair local causal interactions and prevent the build-up of global complexity in VS/UWS patients. Such OFF periods would be similar to those detected in sleep in healthy subjects [108]. This concept was further confirmed by TEP measurements along with modification by transcranial direct current stimulation (tDCS) in DOC patients [109]. More cerebral regions could be re-excited by tDCS (increased temporal and spatial distributions of TEPs after tDCS than before) in MCS than VS/UWS, suggesting global breakdown and silent cortexes in VS/UWS patients.

The perturbation complex index (PCI) was proposed to quantify the temporal-spatial complexity of TEPs (Fig. 2) [110]. PCI reflects the ability of integration information of cortex, which is considered as one of the key neurophysiological bases of human consciousness [111]. PCI could effectively distinguish the consciousness levels as follows: values under 0.31 as VS/UWS, 0.32–0.49 as MCS, 0.37–0.52 as emerged from MCS, 0.51–0.62 as LIS and 0.44–0.67 as awake [110]. A large sample study (43 VS/UWS and 38 MCS) has measured multiple times PCI from each patient [112]. Finally, the threshold of maximum PCI reached 86.4% accuracy of identifying MCS and VS/UWS (total sensitivity = 80%, specificity = 94.4%). In addition, Bodart et al. [113] combined PCI, CRS-R and FDG-PET, and demonstrated congruent data for PCI and PET in 22 of the 24 DOC patients: preserved metabolism in the fronto-parietal network corresponds to PCI above 0.31. Moreover, four patients classified as VS/UWS by CRS-R but high PCI eventually showed good clinical outcome. This is consistent with other findings that most VS/UWS patients with PCI > 0.31 recover to MCS eventually (accuracy = 73%, sensitivity = 54.5%, specificity = 84.2%) [112]. Furthermore, along with a progressive clinical improvement of a MCS patient in a repetitive TMS treatment, the PCI also tended to gradually increase [114]. In summary, PCI is a diagnostic, monitoring and prognostic biomarker of high potential value in the management of DOC patients.

Fig. 2
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Perturbational complexity index (PCI) discriminates the level of consciousness in brain-injured patients. a PCI values for 48 TMS sessions collected from 20 severely brain-injured patients (TMS was targeted to both left and right Brodmann areas 8 and 7, as indicated at top left). Right: distribution of PCI values from healthy individuals. b Box plots for PCI in brain-injured patients with the statistical significance between pairs of conditions (Linear mixed-effects models: *P = 0.002, **P = 0.0001, ***P = 2 × 10−5, ****P = 8 × 10−7). Gray and red dashed lines in (a) and (b) represent the maximum complexity observed during unconsciousness (PCI = 0.31) and the minimum complexity observed during alert wakefulness (PCI = 0.44) in healthy subjects, respectively (with permission, from Casali et al. [110] Sci Transl Med 5:198ra105)

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Discussion and conclusion

Although we still lack full knowledge about the electrophysiological foundations of human consciousness, numerous EEG studies were instrumental in developing current practice in handling DOC patients. Many EEG measures improve classification accuracy of DOC. Especially in patients who are not able to express conscious responses in behavioral assessment, bedside EEG measurements reduce rate of misdiagnosis [97, 99], and even reestablish the capability of DOC patients of communicating with the outside world [87]. At the same time, EEG measures inform physicians about neural responses to specific treatment. EEG offers the opportunity of testing the efficacy of different treatment approaches, as there is still no distinctly effective treatment strategy for DOC patients [115].

EEG studies have important implications for medicolegal decision making in DOC patients. However, the variety and complexity of EEG measures and analyses create obstacles for becoming a practical and widely applicable tool. Resting-state EEG captures spontaneous activities of neuronal assembles. However, these activities have properties of non-linear dynamics, transitions and high complexity. In addition, the signal analysis decodes information from different dimensions based on EEG amplitude, frequency, phase, time frequency, phase amplitude coupling, etc. Combined with external stimulation, EEG information becomes even more complicated. Different types of stimulation evoke responses from different neural pathways and circuits. Moreover, responses at different latencies carry different information. These complicated multitude of EEG characteristics threatens to become a burden rather than a help in the management of DOC patients. A new question need to be answered: Which one of the numerous EEG measures is the best? Some comparative studies were conducted using relatively large samples of DOC patients to test the utility of various EEG measures in diagnostic accuracy or prediction of outcome [13, 30]. Such studies will help to sort out the most valuable consciousness-related EEG characteristics and facilitate their application in DOC clinics.

When using EEG for diagnostic assessment, the lack of a ‘gold standard’ for detection of conscious awareness is the most prominent limitation. EEG classification accuracy is extremely affected by the clinical diagnosis. Multiple studies conducted classification statistics based on diagnosis from behavioral assessment, such as CRS-R. However, a significant fraction of patients is misdiagnosed based on behavioral assessments, which confounds the performance and interpretation of the EEG measures. Combining EEG with other methods, such as fluoro-2-deoxy-d-glucose-positron emission tomography [52] or functional magnetic resonance imaging [100] has highlighted the potentially excellent diagnostic performance of the EEG measures. Especially, with assistance of BCI systems, it was demonstrated that many DOC patients are misdiagnosed by behavioral assessment [99]. Therefore, inconsistency between EEG classification and behavioral diagnosis should not lead to the conclusion of diagnostic inaccuracy of the EEG but rather to seek for additional diagnostic information through other neuroimaging techniques.

Current studies often show differences of EEG measures at the group level (e.g., MCI vs. VS/UWS). The group statistics cannot be used for individual diagnosis. The recently developed TMS-EEG technique is currently considered the most promising diagnostic method at the individual level. The relationship of TME-EEG characteristics with human consciousness was established from sleep and anesthesia research, demonstrating that ranges of the perturbational complexity index reliably classify VS/UWS, MCS and healthy individuals [112]. Determination of objective threshold values is a critical step towards building an individual diagnostic system and its clinical application.

EEG measures show potential value in predicting DOC outcome. Presence of sleep-related EEG components, such as sleep spindles, or the N400 in event-related potential recordings usually indicate good recovery [79, 116]. However, it should be noted is that prognostic studies in DOC patients were confounded by the medical treatment strategies, nursing and complication management of the patients. For example, if MCS patients were misdiagnosed as VS/UWS, they were more likely to be transferred to nursing homes or other facilities unable to provide best standards of medical care. Therefore, the prognostic values of EEG measures in DOC patients still await controlled, large-scale multicentre validation studies.

Finally, EEG studies in DOC patients are difficult to perform. Heterogeneity in the etiology of DOC likely imposes the problem of a lack of unifying EEG characteristics. Simple practical issues, such as motor restlessness, a characteristic feature of many MCS patients, makes EEG measurement difficult or impossible. Moreover, sequelae caused by brain injury, such as epilepsy or paroxysmal sympathetic hyperactivity, may contaminate the EEG signal. In addition, EEG characteristics, which might show diagnostic or prognostic utility in some studies will likely not always perform well in real-world clinical applications due to limited sample sizes and specific patient characteristics in the studies. Despite these limitations, the current review still identified some specific EEG characteristics with consistently reliable performance across studies in identifying the consciousness level and predicting outcome of DOC patients, such as resting-state alpha power in MCS vs. VS/UWS classification [11, 30], N400 in MCS recovery prediction [79, 80], and PCI (from TMS-EEG) in both individual diagnosis and prognosis [110, 112]. Moreover, machine learning trained by comprehensive EEG characteristics, including indexes derived from different information dimensions, could achieve high levels of accuracy in diagnosis and prognosis of DOC patients [30, 52].

Despite all difficulty and immaturity, this review has provided manifold evidence that the EEG can assess the dysfunctional brain networks of DOC patients, resulting in relevant improvement in diagnostic accuracy, and opening up the opportunity of monitoring treatment effects and predicting long-term outcome. This will help the professional handling of DOC patients.