Disorders of consciousness (DoC) refer to a spectrum of conditions characterized by impaired levels of consciousness due to various underlying causes. DoC are primarily classified into coma, vegetative state (VS), and minimally conscious state (MCS) [1]. Despite advances in clinical practice, accurately determining the specific level of consciousness in DoC patients remains a significant challenge. The precise assessment and differentiation between these states are crucial for guiding diagnosis and treatment but continue to present difficulties in the management of DoC patients.

Several standardized assessment methods have been developed specifically for evaluating DoC patients, among which the Coma Recovery Scale-Revised (CRS-R) is widely recognized for its practicality and effectiveness. The CRS-R is particularly sensitive in detecting the MCS, making the CRS-R a valuable tool in the assessment of DoC patients. However, the reliability of these neurobehavioral measures can be compromised due to the subjective nature of the evaluation process, resulting in variable accuracy of the assessment outcomes [2]. Therefore, while neurobehavioral scales are useful for diagnosing DoC, neurobehavioral scales are not entirely dependable.

Neuroimaging techniques, such as functional Magnetic Resonance Imaging (fMRI) and Positron Emission Tomography (PET), provide objective assessment of DoC by probing underlying brain function, detecting covert awareness, and revealing behaviorally undetectable neural correlates of consciousness [3, 4]. However, routine clinical use of neuroimaging techniques is significantly limited by high costs, restricted accessibility, technical demands (e.g., patient transport and MRI compatibility), and challenges in scanning critically ill patients [5]. In contrast, electroencephalogram (EEG)-based approaches offer substantial advantages, including bedside monitoring of spontaneous brain activity, delivering high temporal resolution, portability, repeatability, and lower cost [6]. Advances in EEG analysis enhance diagnostic accuracy and covert consciousness detection [7, 8], making EEG a critical tool for complementing behavioral and neuroimaging assessments. Within the EEG methodologies, amplitude-integrated EEG (aEEG) presents a streamlined solution. aEEG compresses raw EEG data into min-max amplitude bands on a semi-log scale, enabling rapid visual trend assessment [9]. Although originally developed for neonatal monitoring [9], the artifact tolerance, simplicity, and suitability of aEEG for continuous monitoring facilitate effective translation to adult critical care [10], for which aEEG is gradually being applied.

EEG signals are classified into low-frequency bands, including delta, theta, and alpha, as well as high-frequency bands, such as beta and gamma. The brain activity state is assessed based on the amplitude and fluctuations of EEG waveforms. Under normal awake conditions EEG amplitudes and waveforms exhibit regular patterns. However, the spectral power across these frequency bands is significantly disrupted in patients with DoC [11]. Klimesch [12] reported that variations in alpha and theta reflect cognitive function and memory capabilities. Patients with DoC typically display enhanced delta and suppressed alpha compared to healthy individuals [5]. Naro et al. [13] further noted that patients with MCS exhibit higher power in theta and alpha than patients with VS in whom alpha wave activity is closely associated with consciousness recovery. Specifically, alpha power in the parietal and occipital regions is significantly increased in patients with MCS [13, 14]. In addition, Piarulli et al. observed that beta power is notably higher in patients with MCS compared to patients with VS. However, research on high-frequency bands, such as beta and gamma in DoC, is limited [15].

Consciousness-related information in the brain is often complex and multifaceted. The spectral power of EEG and the amplitude changes in specific frequency bands reflect the differences and correlations between various states of consciousness. Numerous studies have examined the relationship between EEG and consciousness states and shown that consciousness is not simply a “present or absent” condition. EEG-based neurofunctional imaging techniques offer an innovative approach to tackling this challenge. Cavinato et al. [16] observed that patients in a VS exhibit impaired cortical integration with significant disruption of the associations between the frontal and parietal lobes. In contrast, Wu et al. [17] reported that patients in a MCS exhibit superior functional connectivity between brain regions with enhanced cortical peripheral connectivity compared to patients with VS [16, 17, 18, 19]. Increasing evidence suggests that resting-state EEG coherence serves as a robust tool for quantifying the relationship between conscious awareness and cerebral information processing and provides more granular insight into differentiating between various levels of consciousness [20, 21]. Patients who regained consciousness demonstrated higher levels of cortical functional connectivity in quantity and strength in a clinical trial assessing the prognosis of patients with DoC over a 3-month period [22]. Schorr et al. [23] proposed that the coherence between frontal and parietal regions serve as a predictive marker for dynamic shifts in consciousness states, such as recovery from VS-to-MCS with delta and theta in the parietal cortex showing high sensitivity and specificity in forecasting recovery in patients with VS.

It is indisputable that significant challenges remain in the diagnosis and management of DoC, primarily due to an insufficient understanding of the neurophysiologic mechanisms underlying consciousness. Therefore, a comprehensive analysis of EEG signals was performed in DoC patients with a focus on the whole-brain power spectral distribution, amplitude fluctuations within distinct frequency bands, and the functional state of brain connectivity in the resting state. By integrating these brain-machine data, a more cohesive model of the consciousness network within the brain was constructed. Furthermore, the most valuable EEG features related to consciousness were identified, thereby advancing the use of resting-state EEG in DoC. This approach will enhance the accuracy of diagnosis, enable better monitoring of therapeutic interventions, and facilitate the prediction of recovery in DoC patients.

Recent studies have demonstrated that multidimensional EEG indicators can effectively distinguish patients’ levels of consciousness and provide an important basis for clinical assessment and prognosis. Our analysis revealed characteristic electrophysiological patterns across different states of impaired consciousness. A significant difference in aEEG center values was observed between coma and MCS patients. In terms of relative spectral power, significant differences were found in the $\delta$ and $\gamma$ bands between VS and MCS patients, while relative power in the $\alpha$ and $\beta$ bands showed significant differences among coma, VS, and MCS groups. Regarding functional connectivity, parieto-occipital connectivity matrices in the theta band exhibited more pronounced differences between VS and coma patients. Notably, in absolute power topography, activity over the left DLPFC (electrode F3) remained consistently elevated across all groups, following a gradient of coma MCS VS.

The results of the current study aligned closely with the existing literature and further refined the spectrum of EEG features associated with different levels of consciousness. Significant intergroup differences in quantitative scores were detected. The low amplitude range in the coma group indicated a significant decrease in brain activity levels, which reflected a severe disruption of brain network integration [46]. The “Theory of Consciousness Index” proposed by Casali et al. [47] suggests that changes in conscious states may be accompanied by reorganization of some neural networks. This reorganization may manifest as the partial preservation of cortical-thalamic connections. Notably, while pediatric EEG studies typically use limited-channel setups due to cranial size constraints, this adult-focused investigation utilized high-density 30-channel EEG [48]. This approach compensated for the spatial resolution limitations inherent in low-channel configurations [49], thereby enabling precise characterization of the dynamic reorganization of the prefrontal-parietal network [50]. Even if overall functional connectivity weakens, connections in some key areas may persist. The medium amplitude observed in the VS group may reflect this partial preservation of cortical-thalamic connections, which align with the Casali perspective [47]. The highest amplitude observed in the MCS group was associated with the enhanced functional connectivity of the default mode network (DMN) [30]. Patients in the MCS state may exhibit limited consciousness or responsiveness, demonstrating some degree of reaction to certain external stimuli, such as sound or touch [51]. The aEEG data from the three groups in the current study showed significant differences and exhibited a change corresponding to the level of consciousness. This finding indicated that aEEG may be a useful quantitative indicator for assessing levels of consciousness. The thalamo-cortical network functional integrity model (ABCD model) is an EEG-based power spectral density classification system that assesses the functional integrity of the thalamo-cortical network through EEG dynamic characteristics [52]. The ABCD model is used to describe neural activity patterns in different states of consciousness, as proposed by Curley et al. [52] in a 2022 study. Type A is characterized by dominance in the delta band, reflecting a complete lack of cortical input, and is commonly observed in patients with severe brain injuries that disrupt thalamocortical connections [53]. The dominance of delta band power in coma patients aligned with the “A-type power spectrum” described in the literature, which is characterized by a loss of integrity and reduced excitability, reflecting severe inhibition of brain function [52, 54]. In contrast, the preservation and enhancement of high-frequency activity (alpha/beta/gamma) in the MCS group supported the theory in which restoration of consciousness depended on high-frequency oscillations [7, 55]. In addition, the elevated power in the gamma frequency band within the MCS group may be linked to higher-order cognitive processing [56]. The aEEG and power spectrum characteristics aligned with the classic pattern of low-frequency dominance and high-frequency attenuation observed in patients with DoC.

The close correlation between the power distribution of different frequency bands in various brain regions and levels of consciousness was highly consistent with recent studies on the neural mechanisms of consciousness [7]. The coma group exhibited the highest relative power in the delta band across all brain regions among the three patient groups. This finding supported the classic conclusion that low-frequency oscillations serve as markers of conscious inhibition, suggesting that the pathomechanism may be related to the generalization of slow-wave activity due to disintegration of the thalamo-cortical loop. The difference in theta power in the parietal lobe suggests that this area has a crucial role in maintaining consciousness [57]. Engemann DA et al. [58] reported that alpha band power was one of the most prominently performing indicators for distinguishing MCS from UWS among more than 100 EEG recordings. Stefan et al. [14] reported the neural characteristics of enhanced alpha band activity and elevated beta coherence in patients with MCS through multidimensional EEG analysis, including power spectra, connectivity, and information entropy, which provided a reliable quantitative index for clinically differentiating between UWS and MCS. Based on our analysis, patients in the MCS group demonstrated significantly higher spectral power in the $\alpha$ and $\beta$ bands across nearly all brain regions compared to the other groups. Elevated power in these frequency bands reflected the preserved cortical synchronization, functional network integration, and neurometabolic coupling capacity of MCS patients, making these features key biomarkers for differentiating states of consciousness [32]. Gamma oscillations are associated with higher-order cognitive processing and information binding across different brain regions [59]. The widespread enhancement of activities in these frequency bands in MCS patients may underpin the residual awareness and cognitive functions, such as the ability to intermittently follow commands. This finding aligns with previous research suggesting that high-frequency gamma activity is a neural correlate of conscious perception [30].

The brain functional connectivity patterns of three patient groups across different frequency bands was systematically analyzed based on wPLI. The five-band wPLI brain connectivity matrix (Fig. 5) for different brain regions revealed the dynamic reorganization characteristics of patients with varying levels of consciousness in large-scale brain network integration and dissociation. The connection strength in the delta frequency band increased as the level of consciousness decreased, which may be related to inhibition of the cortical-thalamus circuit and enhancement of slow-wave activity [60]. All three groups of patients showed strong posterior regional connectivity in the delta frequency band. Delta activity is a hallmark of deep sleep and severe brain injury. The predominant distribution in the occipital cortex may reflect deafferentation of the visual pathways and a generalized suppression of cortical function [61]. This pattern was consistently observed across the three groups, indicating that severe cortical dysfunction is a common pathologic basis for patients with DoC. A particularly intriguing finding emerged in the theta band. The VS group exhibited the strongest frontal-temporal-parietal connectivity in the theta frequency, surpassing that of the MCS group. This result challenges the simplistic linear model that “stronger connectivity equals higher level of consciousness” and supports the “over-activation of thalamocortical circuits” hypothesis. The highest theta connectivity in the VS group may reflect enhanced thalamic drive leading to pathologic synchronization between cortical regions, whereas the relatively lower theta connectivity in MCS patients suggests that the thalamocortical system is beginning to break free from this rigid synchrony, transitioning toward more flexible and complex dynamic activity [62]. This finding is consistent with several fMRI studies, which have reported that thalamic functional connectivity abnormalities in VS patients are even more severe than in coma patients [63]. The alpha-band connectivity pattern, which is centered on the frontal-right prefrontal regions, may underlie the network foundation that sustains a basic level of consciousness [43]. The dense connections in the beta band among temporal, parietal, and frontal areas observed in the VS and MCS groups could correspond to residual cognitive processing and self-awareness capacities [64]. These findings echo fMRI studies that report damage to the DMN and executive control networks in DoC [65]. Gamma-band activity is considered the neural basis for consciousness integration and the “binding problem” [66]. Only diffuse, disorganized gamma connectivity was observed among all patients with DoC, strongly suggesting that the breakdown of high-frequency connections may be a key neurophysiologic substrate for the loss of conscious content.

The analysis of EEG absolute power topography across the entire brain and different brain regions in the three patient groups under five EEG rhythms is essential. This study compared EEG characteristics across frequency bands in patients with coma, VS, and MCS using absolute power topographic maps. The ubiquitous elevation of delta power in the prefrontal regions is consistent with previous literature, potentially stemming from the desynchronization of thalamocortical projections and the hyper-synchronous firing of cortical neurons [30, 67]. VS and MCS patients demonstrated stronger power and more widespread brain involvement in the theta band compared to the coma group, which aligns with the role of theta oscillations in attention, memory, and network integration [68, 69]. Some studies have indicated that the preservation of theta activity is associated with the recovery of consciousness. Theta activity enhancement in MCS patients may reflect the partial functional preservation of thalamocortical circuits and the default mode network [70]. Chennu et al. [30] reported that alpha power is positively correlated with the retention of default network function, supporting use of alpha power as a predictor of recovery of consciousness. De Koninck et al. [71] noted that transcranial alternating current stimulation improves the state of consciousness by enhancing alpha synchronization, suggesting that alpha activity in patients with MCS may reflect residual neuroplasticity. Prefrontal beta activity correlated with dorsal attentional network function. The VS and MCS groups exhibited oscillatory activity in the beta band that was concentrated in the frontal regions with significantly greater power than the coma group. Neural oscillations in the beta frequency band had a key role in frontal-specific activation associated with motor intention. The increase in parietal beta power in patients with MCS may reflect a capacity for sensorimotor integration [72]. Gamma power was low in all three groups but prefrontal localization was active in the MCS and VS groups, which is consistent with the general observation of high-frequency oscillatory suppression in DoC [73, 74]. Notably, the high-power regions across all frequency bands involved the prefrontal lobes. Stender et al. [75] concluded that for every 10% increase in prefrontal metabolic rate, the probability of patients with MCS converting to conscious wakefulness increased 2.3-fold in the PET study. Specifically, the left DLPFC (corresponding to the F3 electrode) functions as a critical hub between the executive control network (ECN) and the DMN. The absolute power at the F3 electrode exhibits a consistent trend of coma    MCS    VS across all frequency bands in the three groups, which is in agreement with the findings herein that the higher power observed in the VS group may arise from network dysregulation leading to excessive synchronization [20].

EEG has made significant progress in the diagnosis and prognostic assessment of DoC, particularly in distinguishing between UWS/ VS and MCS. Toplutaş et al. [76] successfully distinguished between patients with VS and MCS by analyzing the duration, transition probabilities, and spatial topologic features of resting EEG microstates [76, 77, 78, 79]. The EEG spatial-spectral gradient model developed by Colombo et al. [58] in combination with support vector machines, achieved a classification accuracy of 92% in cross-center validation, outperforming the traditional CRS-R, which has an accuracy of approximately 75%. In addition, Pan et al. [80] successfully achieved online consciousness detection for patients with DoC using a P300 brain-computer interface network, attaining a response accuracy of 68%. Portable EEG systems have made significant advances in ICUs and rehabilitation wards, including real-time microstate tracking through mobile-based algorithms, multimodal stimulation paradigms integrating auditory, pain, and visual stimuli to enhance covert consciousness detection by 15%, and the integration of EEG features with clinical data to develop automated prognostic prediction models using elastic net regression [78]. The current study used a systematic analysis of multi-dimensional EEG features to uncover the differences in neural mechanisms among patients with DoC at varying levels of consciousness. The aEEG exhibited an increase corresponding to levels of consciousness and the power of high-frequency bands significantly increased with recovery of consciousness, further elucidating the association between high-frequency activity and consciousness recovery [30]. The elevated gamma-band power in MCS aligns with the existing literature. Gamma oscillations are associated with feature binding, attention, and conscious perception, and preservation of gamma oscillations in MCS may indicate residual cognitive capacity [33]. The increase in gamma power observed herein might reflect partially preserved thalamocortical and frontoparietal circuits, which are critical for consciousness. While previous studies have employed aEEG threshold criteria similar to those used in this research [10]—thereby providing a methodological precedent—it should be noted that standardized adult-specific threshold criteria remain to be established. Against this backdrop, the current study represents a methodologic breakthrough by integrating multi-dimensional EEG features, analyzing mechanisms across frequency bands, and validating findings with networks [81]. This approach transcends the limitations of single biomarkers and constructs a multi-scale model for consciousness recovery. In the future combining real-time neurofeedback with closed-loop regulation technology is expected to advance the assessment of DoC toward precision medicine.

However, although this study has made some advancements in methodology and existing evidence indicates that the level of consciousness serves as the primary determinant of neurophysiologic changes, it is still necessary to acknowledge the existence of certain limitations. First, its sample size was relatively limited, restricting the generalizability of findings. Second, it only focused on EEG features without integrating other neuroimaging modalities or comprehensive clinical variables. Third, the single-center design may introduce selection bias, and the follow-up period was not detailed. Fourth, diverse etiologies of DoC were not fully controlle. Future studies should increase the sample size, integrate multimodal neuroimaging and clinical data, extend the follow-up period, and conduct more explicit subgroup analyses in order to further validate and optimize the proposed model.

In summary, multidimensional EEG contributed to distinguishing different degrees of DoC. The aEEG center amplitude effectively distinguishes MCS from coma patients; relative power in the $\delta$ and $\gamma$ bands shows discriminatory significance between VS and MCS patients; relative power in the $\alpha$ and $\beta$ bands enables differentiation among coma, VS, and MCS patients. Furthermore, the brain network connectivity matrix in the $\theta$ band over the parieto-occipital region exhibits more distinct features when distinguishing coma from VS patients. The absolute power topography of the left DLPFC also demonstrates potential for discriminating levels of impaired consciousness, further supporting the utility of multidimensional EEG features in clinical differentiation.

Multidimensional EEG biomarkers extend prior single-domain EEG approaches by offering a concise, multiparametric framework that complements behavioral assessments and refines diagnostic classification in DoC. Future integration into bedside monitoring systems and automated analysis could enhance diagnostic accuracy, prognostication, and treatment monitoring.

The data presented in this study are available on request from the corresponding author.

SL and JG designed the study. YHS, SYL, JWC, MYZ collected the data. WJY and JZ analyzed the data and prepared the figures. YHS, SYL, JZ, WJY, JWC, MYZ drafted the manuscript. SL and JG brought major revisions in significant proportions of the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

The study was conducted under the Declaration of Helsinki of the World Medical Association and approved by the Ethics Committee of the Affiliated Hospital of Nantong University. The approval number for the study was 2024-K142-01. Before inclusion, the researcher fully informed the legal guardian of each patient about the study protocol and obtained informed consent.

We would like to express our gratitude to all those who acted as peer reviewers, providing us with their valuable opinions and suggestions.

This work was supported by the Jiangsu Province Research Hospital, China [grant numbers YJXYY202204], Natural Science Foundation of Jiangsu Province, China [grant number BK20241839] and Jiangsu Commission of Health, China [grant number M2022052].

The authors declare no conflict of interest.

Supplementary material associated with this article can be found, in the online version, at https://doi.org/10.31083/JIN44233.