Non-invasive Brain-computer Interface Research Articles

Macroscopic brain dynamics beyond contralateral primary motor cortex for movement prediction

This study investigates the complex relationship between upper limb movement direction and macroscopic neural signals in the brain, which is critical for understanding brain-computer interfaces (BCI). Conventional BCI research has primarily focused on a local area, such as the contralateral primary motor cortex (M1), relying on the population-based decoding method with microelectrode arrays. In contrast, macroscopic approaches such as electroencephalography (EEG) and magnetoencephalography (MEG) utilize numerous electrodes to cover broader brain regions. This study probes the potential differences in the mechanisms of microscopic and macroscopic methods. It is important to determine which neural activities effectively predict movements. To investigate this, we analyzed MEG data from nine right-handed participants while performing arm-reaching tasks. We employed dynamic statistical parametric mapping (dSPM) to estimate source activity and built a decoding model composed of long short-term memory (LSTM) and a multilayer perceptron to predict movement trajectories. This model achieved a high correlation coefficient of 0.79 between actual and predicted trajectories. Subsequently, we identified brain regions sensitive to predicting movement direction using the integrated gradients (IG) method, which assesses the predictive contribution of each source activity. The resulting salience map demonstrated a distribution without significant differences across motor-related regions, including M1. Predictions based solely on M1 activity yielded a correlation coefficient of 0.42, nearly half as effective as predictions incorporating all source activities. This suggests that upper limb movements are influenced by various factors such as movement coordination, planning, body and target position recognition, and control, beyond simple muscle activity. All of the activities are needed in the decoding model using macroscopic signals. Our findings also revealed that contralateral and ipsilateral hemispheres contribute equally to movement prediction, implying that BCIs could potentially benefit patients with brain damage in the contralateral hemisphere by utilizing brain signals from the ipsilateral hemisphere. In conclusion, this study demonstrates that macroscopic activity from large brain regions significantly contributes to predicting upper limb movement. Non-invasive BCI systems would require a comprehensive collection of neural signals from multiple brain regions.

EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification.

Motor imagery EEG classification plays a crucial role in non-invasive Brain-Computer Interface (BCI) research. However, the performance of classification is affected by the non-stationarity and individual variations of EEG signals. Simply pooling EEG data with different statistical distributions to train a classification model can severely degrade the generalization performance. To address this issue, the existing methods primarily focus on domain adaptation, which requires access to the test data during training. This is unrealistic and impractical in many EEG application scenarios. In this paper, we propose a novel multi-source domain generalization framework called EEG-DG, which leverages multiple source domains with different statistical distributions to build generalizable models on unseen target EEG data. We optimize both the marginal and conditional distributions to ensure the stability of the joint distribution across source domains and extend it to a multi-source domain generalization framework to achieve domain-invariant feature representation, thereby alleviating calibration efforts. Systematic experiments conducted on a simulative dataset, BCI competition IV 2a, 2b, and OpenBMI datasets, demonstrate the superiority and competitive performance of our proposed framework over other state-of-the-art methods. Specifically, EEG-DG achieves average classification accuracies of 81.79% and 87.12% on datasets IV-2a and IV-2b, respectively, and 78.37% and 76.94% for inter-session and inter-subject evaluations on dataset OpenBMI, which even outperforms some domain adaptation methods. Our code is available at https://github.com/zxchit2022/EEG-DG for evaluation.

Intentional binding for noninvasive BCI control.

Noninvasive brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system.
Making BCIs efficient and accurate is paramount to improve the reliability of real-life and clinical applications, from open-loop device control to closed-loop neurorehabilitation. By promoting sense of agency and embodiment, realistic setups including multimodal channels of communication, such as eye-gaze, and robotic prostheses aim to improve BCI performance. However, how the mental imagery command should be integrated in those hybrid systems so as to ensure the best interaction is still poorly understood.
To address this question, we performed a hybrid EEG-based BCI training involving healthy volunteers enrolled in a reach-and-grasp action operated by a robotic arm. Main results showed that the hand grasping motor imagery timing significantly affects the BCI accuracy evolution as well as the spatiotemporal brain dynamics. Larger accuracy improvement was obtained when motor imagery is performed just after the robot reaching, as compared to before or during the movement. The proximity with the subsequent robot grasping favored intentional binding, led to stronger motor-related brain activity, and primed the ability of sensorimotor areas to integrate information from regions implicated in higher-order cognitive functions.
Taken together, these findings provided fresh evidence about the effects of intentional binding on human behavior and cortical network dynamics that can be exploited to design a new generation of efficient brain-machine interfaces.

Scaled Elastic Hydrogel Interfaces for Brain Electrophysiology

AbstractThe advancement in brain–computer interface technology has facilitated real‐time communication between the brain and external devices, rapidly expanding invasive brain–computer interfaces. Nevertheless, little progress has been made in improving the one‐way, noninvasive brain–computer interface technology using conductive methods, necessitating enhancements to the current paste contact electrodes. In this paper, a captivating spherical electrode is fabricated that exhibits noncytotoxic properties, combining alginate hydrogel with indium tin oxide conductor components. The electrode's remarkable and consistent electrical conductivity enables it to generate stable adaptive deformations that conform to diverse scalp topographies. Most impedance values fall below 10 kΩ, indicating heightened hydrophilicity compared to traditional conductive pastes, as evidenced by contact angle measurements. An empirical electroencephalography data collection study confirms its conductivity and signal transmission reliability. The acquired electroencephalography signals are utilized for emotion recognition utilizing band energy ratio analysis and support vector machine (SVM) algorithms. The operation of this system is both straightforward and convenient, requiring minimal preparation before and after the collection of electroencephalography signals. Utilizing an extensive range of raw materials and straightforward preparatory procedures enhances the potential industrial application of hydrogel electrodes, thereby significantly contributing to advancing civilian electroencephalography equipment and investigating depression‐related pathologies.

Transcranial focused ultrasound to V5 enhances human visual motion brain-computer interface by modulating feature-based attention

A brain-computer interface (BCI) enables users to control devices with their minds. Despite advancements, non-invasive BCIs still exhibit high error rates, prompting investigation into the potential reduction through concurrent targeted neuromodulation. Transcranial focused ultrasound (tFUS) is an emerging non-invasive neuromodulation technology with high spatiotemporal precision. This study examines whether tFUS neuromodulation can improve BCI outcomes, and explores the underlying mechanism of action using high-density electroencephalography (EEG) source imaging (ESI). As a result, V5-targeted tFUS significantly reduced the error in a BCI speller task. Source analyses revealed a significantly increase in theta and alpha activities in the tFUS condition at both V5 and downstream in the dorsal visual processing pathway. Correlation analysis indicated that the connection within the dorsal processing pathway was preserved during tFUS stimulation, while the ventral connection was weakened. These findings suggest that V5-targeted tFUS enhances feature-based attention to visual motion.

An optimized EEGNet decoder for decoding motor image of four class fingers flexion

As a cutting-edge technology of connecting biological brain and external devices, brain-computer interface (BCI) exhibits promising applications on extensive fields such as medical and military. As for the disable individuals with four limbs losing the motor functions, it is a potential treatment way to drive mechanical equipments by the means of non-invasive BCI, which is badly depended on the accuracy of the decoded electroencephalogram (EEG) singles. In this study, an explanatory convolutional neural network namely EEGNet based on SimAM attention module was proposed to enhance the accuracy of decoding the EEG singles of index and thumb fingers for both left and right hand using sensory motor rhythm (SMR). An average classification accuracy of 72.91% the data of eight healthy subjects was obtained, which were captured from the one second before finger movement to two seconds after action. Furthermore, the character of event-related desynchronization (ERD) and event related synchronization (ERS) of index and thumb fingers was also studied in this study. These findings have significant importance for controlling external devices or other rehabilitation equipment using BCI in a fine way.

Classification of Spatial Temporal Patterns Based on Neuromorphic Networks

This work is devoted to the problems of developing neuromorphic classifiers of spatiotemporal patterns, as well as their application in neurointerfaces. Classifiers of spatiotemporal patterns based on neural networks, support vector machines, deep neural networks, and Riemannian geometry are considered. A comparative study of these classifiers is carried out in the plane of the accuracy of multiclass recognition of electroencephalographic signals showing time-dependent bioelectrical activity in different areas of the brain during the imagination of different movements. It is shown that such classifiers can provide an accuracy of 60-80% when recognizing from two to four classes of imaginary movements. A new type of classifier based on a neuromorphic network, based on the biosimilar neurons built on the Izhikevich model, is proposed. The network processes input spike sequences and generates pulse streams of different frequencies at the outputs. The network is trained using the Supervised STDP algorithm based on labeled information containing examples of the correct recognition of the required pattern classes. The recognized pattern class is determined by the maximum frequency of the output sequence. The neuromorphic classifier showed an average classification accuracy of 90% for 4 classes of imaginary commands and a maximum of 95%. By modeling the robot control task in the virtual environment it is shown that such accuracy is sufficient for the effective use of the classifier as part of a non-invasive brain-computer interface for non-contact control of robotic devices.

Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs.

Channel selection has become the pivotal issue affecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside effective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems. In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA. The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA. The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.

Advancements in brain-computer interfaces for the rehabilitation of unilateral spatial neglect: a concise review.

This short review examines recent advancements in neurotechnologies within the context of managing unilateral spatial neglect (USN), a common condition following stroke. Despite the success of brain-computer interfaces (BCIs) in restoring motor function, there is a notable absence of effective BCI devices for treating cerebral visual impairments, a prevalent consequence of brain lesions that significantly hinders rehabilitation. This review analyzes current non-invasive BCIs and technological solutions dedicated to cognitive rehabilitation, with a focus on visuo-attentional disorders. We emphasize the need for further research into the use of BCIs for managing cognitive impairments and propose a new potential solution for USN rehabilitation, by combining the clinical subtleties of this syndrome with the technological advancements made in the field of neurotechnologies.

Design and Evaluation of CPU-, GPU-, and FPGA-Based Deployment of a CNN for Motor Imagery Classification in Brain-Computer Interfaces

Brain–computer interfaces (BCIs) have gained popularity in recent years. Among noninvasive BCIs, EEG-based systems stand out as the primary approach, utilizing the motor imagery (MI) paradigm to discern movement intentions. Initially, BCIs were predominantly focused on nonembedded systems. However, there is now a growing momentum towards shifting computation to the edge, offering advantages such as enhanced privacy, reduced transmission bandwidth, and real-time responsiveness. Despite this trend, achieving the desired target remains a work in progress. To illustrate the feasibility of this shift and quantify the potential benefits, this paper presents a comparison of deploying a CNN for MI classification across different computing platforms, namely, CPU-, embedded GPU-, and FPGA-based. For our case study, we utilized data from 29 participants included in a dataset acquired using an EEG cap for training the models. The FPGA solution emerged as the most efficient in terms of the power consumption–inference time product. Specifically, it delivers an impressive reduction of up to 89% in power consumption compared to the CPU and 71% compared to the GPU and up to a 98% reduction in memory footprint for model inference, albeit at the cost of a 39% increase in inference time compared to the GPU. Both the embedded GPU and FPGA outperform the CPU in terms of inference time.

Comprehensive Review of Noninvasive Brain-Computer Interfaces for Controlling Robotic Arms

A robotic arm is a mechanical device with a given number of Degrees of Freedom (DoFs) that mimics the functions of a human arm and performs any desired task, such as grasping and moving objects. Current research is directed toward the design of robots and artificial human body parts controlled by brain signals, translating human thoughts into actions. Brain-Computer Interface (BCI) systems have been used to enable people with motor disabilities to control assistive robotic equipment that replaces the lost functions. This paper presents a review of the state-of-the-art of the latest papers dealing with the control of a robotic arm based on Electroencephalogram (EEG). A comparative study of the different methods and techniques used in different blocks of the robotic arm’s noninvasive BCI controlling system is conducted. These blocks include signal acquisition using noninvasive electrodes, signal preprocessing, feature extraction, classification, and command. Additionally, this paper presents a performance comparison of the reviewed controlling systems of robotic arms using EEG signals.

Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain-Computer Interface Study.

In the field of neuroscience, brain-computer interfaces (BCIs) are used to connect the human brain with external devices, providing insights into the neural mechanisms underlying cognitive processes, including aesthetic perception. Non-invasive BCIs, such as EEG and fNIRS, are critical for studying central nervous system activity and understanding how individuals with cognitive deficits process and respond to aesthetic stimuli. This study assessed twenty participants who were divided into control and impaired aging (AI) groups based on MMSE scores. EEG and fNIRS were used to measure their neurophysiological responses to aesthetic stimuli that varied in pleasantness and dynamism. Significant differences were identified between the groups in P300 amplitude and late positive potential (LPP), with controls showing greater reactivity. AI subjects showed an increase in oxyhemoglobin in response to pleasurable stimuli, suggesting hemodynamic compensation. This study highlights the effectiveness of multimodal BCIs in identifying the neural basis of aesthetic appreciation and impaired aging. Despite its limitations, such as sample size and the subjective nature of aesthetic appreciation, this research lays the groundwork for cognitive rehabilitation tailored to aesthetic perception, improving the comprehension of cognitive disorders through integrated BCI methodologies.

Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface.

Brain-computer interfaces (BCI) using electroencephalography provide a noninvasive method for users to interact with external devices without the need for muscle activation. While noninvasive BCIs have the potential to improve the quality of lives of healthy and motor-impaired individuals, they currently have limited applications due to inconsistent performance and low degrees of freedom. In this study, we use deep learning (DL)-based decoders for online continuous pursuit (CP), a complex BCI task requiring the user to track an object in 2D space. We developed a labeling system to use CP data for supervised learning, trained DL-based decoders based on two architectures, including a newly proposed adaptation of the PointNet architecture, and evaluated the performance over several online sessions. We rigorously evaluated the DL-based decoders in a total of 28 human participants, and found that the DL-based models improved throughout the sessions as more training data became available and significantly outperformed a traditional BCI decoder by the last session. We also performed additional experiments to test an implementation of transfer learning by pretraining models on data from other subjects, and midsession training to reduce intersession variability. The results from these experiments showed that pretraining did not significantly improve performance, but updating the models' midsession may have some benefit. Overall, these findings support the use of DL-based decoders for improving BCI performance in complex tasks like CP, which can expand the potential applications of BCI devices and help to improve the quality of lives of healthy and motor-impaired individuals.

Customizing the human-avatar mapping based on EEG error related potentials

Objective. A key challenge of virtual reality (VR) applications is to maintain a reliable human-avatar mapping. Users may lose the sense of controlling (sense of agency), owning (sense of body ownership), or being located (sense of self-location) inside the virtual body when they perceive erroneous interaction, i.e. a break-in-embodiment (BiE). However, the way to detect such an inadequate event is currently limited to questionnaires or spontaneous reports from users. The ability to implicitly detect BiE in real-time enables us to adjust human-avatar mapping without interruption. Approach. We propose and empirically demonstrate a novel brain computer interface (BCI) approach that monitors the occurrence of BiE based on the users’ brain oscillatory activity in real-time to adjust the human-avatar mapping in VR. We collected EEG activity of 37 participants while they performed reaching movements with their avatar with different magnitude of distortion. Main results. Our BCI approach seamlessly predicts occurrence of BiE in varying magnitude of erroneous interaction. The mapping has been customized by BCI-reinforcement learning (RL) closed-loop system to prevent BiE from occurring. Furthermore, a non-personalized BCI decoder generalizes to new users, enabling ‘Plug-and-Play’ ErrP-based non-invasive BCI. The proposed VR system allows customization of human-avatar mapping without personalized BCI decoders or spontaneous reports. Significance. We anticipate that our newly developed VR-BCI can be useful to maintain an engaging avatar-based interaction and a compelling immersive experience while detecting when users notice a problem and seamlessly correcting it.

Biomedical Engineering Research Based on Computer Propagation Technology: Treatment of neurodigestive diseases

With the support of the Institute of Cancer Hospital of Chifeng University, Sarkov and Markov Memorial Health Informatics Laboratory designed a new working concept of innovative Nursing by using neural network and Brain–computer interface technology. The research team proposed a direct connection between human or animal brain (or brain cell culture) and external equipment. In the case of one-way Brain–computer interface, the computer may accept commands from the brain. Using this point to detect the emotion of patients after surgery and carry out necessary gourds, a two-way information exchange method and model of two-way Brain–computer interface are also proposed. The potential non-invasive Brain–computer interface designed by us has been deeply studied, mainly because of its good time resolution, ease of use, portability and relatively low price. Especially in the specific application and working practice of EEG (Electroencephalography), our achievements directly meet the application of clinical institutions.

The role of eye movement signals in non-invasive brain-computer interface typing system.

Brain-Computer Interfaces (BCIs) have shown great potential in providing communication and control for individuals with severe motor disabilities. However, traditional BCIs that rely on electroencephalography (EEG) signals suffer from low information transfer rates and high variability across users. Recently, eye movement signals have emerged as a promising alternative due to their high accuracy and robustness. Eye movement signals are the electrical or mechanical signals generated by the movements and behaviors of the eyes, serving to denote the diverse forms of eye movements, such as fixations, smooth pursuit, and other oculomotor activities like blinking. This article presents a review of recent studies on the development of BCI typing systems that incorporate eye movement signals. We first discuss the basic principles of BCI and the recent advancements in text entry. Then, we provide a comprehensive summary of the latest advancements in BCI typing systems that leverage eye movement signals. This includes an in-depth analysis of hybrid BCIs that are built upon the integration of electrooculography (EOG) and eye tracking technology, aiming to enhance the performance and functionality of the system. Moreover, we highlight the advantages and limitations of different approaches, as well as potential future directions. Overall, eye movement signals hold great potential for enhancing the usability and accessibility of BCI typing systems, and further research in this area could lead to more effective communication and control for individuals with motor disabilities.

Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface

An essential priority of visual brain-computer interfaces (BCIs) is to enhance the information transfer rate (ITR) to achieve high-speed communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, leaving it uncertain whether higher ITRs are achievable. In this study, we used information theory to study the characteristics and capacity of the visual-evoked channel, which leads us to investigate whether and how we can decode higher information rates in a visual BCI system. Using information theory, we estimate the upper and lower bounds of the information rate with the white noise (WN) stimulus. Consequently, we found out that the information rate is determined by the signal-to-noise ratio (SNR) in the frequency domain, which reflects the spectrum resources of the channel. Based on this discovery, we propose a broadband WN BCI by implementing stimuli on a broader frequency band than the steady-state visual evoked potentials (SSVEPs)-based BCI. Through validation, the broadband BCI outperforms the SSVEP BCI by an impressive 7 bps, setting a record of 50 bps. The integration of information theory and the decoding analysis presented in this study offers valuable insights applicable to general sensory-evoked BCIs, providing a potential direction of next-generation human-machine interaction systems.

Abstract TP86: Stroke-Impaired Proprioception Limits Motor Recovery With BCI-FES Device

Introduction: Non-invasive brain-computer interface (BCI) therapy with contingent (i.e., closed-loop) functional electrical stimulation (FES) shows efficacy improving motor function. However, it is not well understood why some stroke survivors respond better to closed-looped BCI-FES intervention than others. Contingent BCI-FES intervention drives neuroplastic change toward adaptive functional connectivity in stroke-impaired sensorimotor brain areas by enriching the afferent feedback environment. The interruption of afferent-efferent feedback circuits is a neural correlate of long-term acquired disability and intactness of functional connections predicts recovery capacity. Studies suggest that proprioception is essential for motor learning and performance. Therefore, we hypothesize that proprioceptive intactness is essential for recovery of motor performance when using a BCI-FES. Methods: Participants with upper extremity motor loss resulting from stroke (n=35) completed up to 30 hours of closed-loop BCI-FES. Results: Change in ARAT scores over the course of the control period were compared to ARAT change scores over the intervention period at a one-month follow up assessment. On average, participants in the intervention realized greater change in ARAT (M = 2.11, SD = 4.68) than during the control phase (M = 0.294, SD = 3.53). An independent-samples t-test indicated this difference, 1.82, 95%CI [-4.17, 0.534], was not statistically significant, t (40.901) = -1.56, p = 0.126. Preliminary results indicate that Proprioceptive sensory loss has a greater limiting effect on recovery than cutaneous somatosensory loss. Discussion: Rehabilitating motor capacity after stroke depends on the intactness of sensorimotor neural circuits. If afference signals are missing or degraded due to stroke, motor recovery is poor. When the stroke spares afferent connectivity to the peripheral muscles, the sensorimotor cortex can leverage plasticity-based motor rehab strategies such as BCI-FES. Conclusion: Intactness of sensorimotor circuits is related to baseline severity of motor impairment as well as recovery of motor function. Efficacious prescription of BCI-FES intervention is best suited for patients with stroke-spared proprioception.

A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning.

Brain-computer interfaces (BCIs) have a potential to revolutionize human-computer interaction by enabling direct links between the brain and computer systems. Recent studies are increasingly focusing on practical applications of BCIs-e.g., home appliance control just by thoughts. One of the non-invasive BCIs using electroencephalography (EEG) capitalizes on event-related potentials (ERPs) in response to target stimuli and have shown promise in controlling home appliance. In this paper, we present a comprehensive dataset of online ERP-based BCIs for controlling various home appliances in diverse stimulus presentation environments. We collected online BCI data from a total of 84 subjects among whom 60 subjects controlled three types of appliances (TV: 30, door lock: 15, and electric light: 15) with 4 functions per appliance, 14 subjects controlled a Bluetooth speaker with 6 functions via an LCD monitor, and 10 subjects controlled air conditioner with 4 functions via augmented reality (AR). Using the dataset, we aimed to address the issue of inter-subject variability in ERPs by employing the transfer learning in two different approaches. The first approach, "within-paradigm transfer learning," aimed to generalize the model within the same paradigm of stimulus presentation. The second approach, "cross-paradigm transfer learning," involved extending the model from a 4-class LCD environment to different paradigms. The results demonstrated that transfer learning can effectively enhance the generalizability of BCIs based on ERP across different subjects and environments.

Brain-Computer Interfaces for Communication in Patients with Disorders of Consciousness: A Gap Analysis and Scientific Roadmap.

We developed a gap analysis that examines the role of brain-computer interfaces (BCI) in patients with disorders of consciousness (DoC), focusing on their assessment, establishment of communication, and engagement with their environment. The Curing Coma Campaign convened a Coma Science work group that included 16 clinicians and neuroscientists with expertise in DoC. The work group met online biweekly and performed a gap analysis of the primary question. We outline a roadmap for assessing BCI readiness in patients with DoC and for advancing the use of BCI devices in patients with DoC. Additionally, we discuss preliminary studies that inform development of BCI solutions for communication and assessment of readiness for use of BCIs in DoC study participants. Special emphasis is placed on the challenges posed by the complex pathophysiologies caused by heterogeneous brain injuries and their impact on neuronal signaling. The differences between one-way and two-way communication are specifically considered. Possible implanted and noninvasive BCI solutions for acute and chronic DoC in adult and pediatric populations are also addressed. We identify clinical and technical gaps hindering the use of BCI in patients with DoC in each of these contexts and provide a roadmap for research aimed at improving communication for adults and children with DoC, spanning the clinical spectrum from intensive care unit to chronic care.