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The determinants and driving forces of communication abilities in the locked-in state are poorly understood so far. Results from an experimental-clinical study on a completely paralyzed person involved in communication sessions after the implantation of a microelectrode array were retrospectively analyzed. The aim was to focus on the prerequisites and determinants for learning to control a brain-computer interface for communication in paralysis. A comparative examination of the communication results with the current literature was carried out in light of an ideomotor theory of thinking. We speculate that novel skill learning took place and that several aspects of the wording of sentences during the communication sessions reflect preserved cognitive and conscious processing. We also present some speculations on the operant learning procedure used for communication, which argues for the reformulation of the previously postulated hypothesis of the extinction of response planning and goal-directed ideas in the completely locked-in state. We highlight the importance of feedback and reinforcement in the thought-action-consequence associative chain necessary to maintain purposeful communication. Finally, we underline the necessity to consider the psychosocial context of patients and the duration of complete immobilization as determinants of the 'extinction of thinking' theory and to identify the actual barriers preventing communication in these patients.

Patients with amyotrophic lateral sclerosis (ALS) can lose all muscle-based routes of communication as motor neuron degeneration progresses, and ultimately, they may be left without any means of communication. While others have evaluated communication in people with remaining muscle control, to the best of our knowledge, it is not known whether neural-based communication remains possible in a completely locked-in state. Here, we implanted two 64 microelectrode arrays in the supplementary and primary motor cortex of a patient in a completely locked-in state with ALS. The patient modulated neural firing rates based on auditory feedback and he used this strategy to select letters one at a time to form words and phrases to communicate his needs and experiences. This case study provides evidence that brain-based volitional communication is possible even in a completely locked-in state., (© 2022. The Author(s).)

The rapid evolution of Brain-Computer Interface (BCI) technology and the exponential growth of BCI literature during the past 20 years is a consequence of increasing computational power and the achievements of statistical learning theory and machine learning since the 1960s. Despite this rapid scientific progress, the range of successful clinical and societal applications remained limited, with some notable exceptions in the rehabilitation of chronic stroke and first steps towards BCI-based assisted verbal communication in paralysis. In this contribution, we focus on the effects of noninvasive and invasive BCI-based verbal communication on the quality of life (QoL) of patients with amyotrophic lateral sclerosis (ALS) in the locked-in state (LIS) and the completely locked-in state (CLIS). Despite a substantial lack of replicated scientific data, this paper complements the existing methodological knowledge and focuses future investigators' attention on (1) Social determinants of QoL and (2) Brain reorganization and behavior. While it is not documented in controlled studies that the good QoL in these patients is a consequence of BCI-based neurorehabilitation, the proposed determinants of QoL might become the theoretical background needed to develop clinically more useful BCI systems and to evaluate the effects of BCI-based communication on QoL for advanced ALS patients and other forms of severe paralysis.

Brain-computer interfaces (BCIs) aim to help paralysed patients to interact with their environment by controlling external devices using brain activity, thereby bypassing the dysfunctional motor system. Some neuronal disorders, such as amyotrophic lateral sclerosis (ALS), severely impair the communication capacity of patients. Several invasive and non-invasive brain-computer interfaces (BCIs), most notably using electroencephalography (EEG), have been developed to provide a means of communication to paralysed patients. However, except for a few reports, all available BCI literature for the paralysed (mostly ALS patients) describes patients with intact eye movement control, i.e. patients in a locked-in state (LIS) but not a completely locked-in state (CLIS). In this article we will discuss: (1) the fundamental neuropsychological learning factors and neurophysiological factors determining BCI performance in clinical applications; (2) the difference between LIS and CLIS; (3) recent development in BCIs for communication with patients in the completely locked-in state; (4) the effect of BCI-based communication on emotional well-being and quality of life; and (5) the outlook and the methodology needed to provide a means of communication for patients who have none. Thus, we present an overview of available studies and recent results and try to anticipate future developments which may open new doors for BCI communication with the completely paralysed., (© 2020 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.)

Patients in completely locked-in state (CLIS) due to amyotrophic lateral sclerosis (ALS) lose the control of each and every muscle of their body rendering them motionless and without any means of communication. Though some studies have attempted to develop brain-computer interface (BCI)-based communication methods with CLIS patients, little information is available of the neuroelectric brain activity of CLIS patients. However, because of the difficulties with and often loss of communication, the neuroelectric signature may provide some indications of the state of consciousness in these patients. We recorded electroencephalography (EEG) signals from 10 CLIS patients during resting state and compared their power spectral densities with those of healthy participants in fronto-central, central, and centro-parietal channels. The results showed significant power reduction in the high alpha, beta, and gamma bands in CLIS patients, indicating the dominance of slower EEG frequencies in their oscillatory activity. This is the first study showing group-level EEG change of CLIS patients, though the reason for the observed EEG change cannot be concluded without any reliable communication methods with this population., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Copyright © 2020 Elsevier B.V. and Japan Neuroscience Society. All rights reserved.)

Brain-machine interfaces (BMI) permit bypass motor system disruption by coupling contingent neuroelectric signals related to motor activity with prosthetic devices that enhance afferent and proprioceptive feedback to the somatosensory cortex. In this study, we investigated neural plasticity in the motor network of severely impaired chronic stroke patients after an EEG-BMI-based treatment reinforcing sensorimotor contingency of ipsilesional motor commands. Our structural connectivity analysis revealed decreased fractional anisotropy in the splenium and body of the corpus callosum, and in the contralesional hemisphere in the posterior limb of the internal capsule, the posterior thalamic radiation, and the superior corona radiata. Functional connectivity analysis showed decreased negative interhemispheric coupling between contralesional and ipsilesional sensorimotor regions, and decreased positive intrahemispheric coupling among contralesional sensorimotor regions. These findings indicate that BMI reinforcing ipsilesional brain activity and enhancing proprioceptive function of the affected hand elicits reorganization of contralesional and ipsilesional somatosensory and motor-assemblies as well as afferent and efferent connection-related motor circuits that support the partial re-establishment of the original neurophysiology of the motor system even in severe chronic stroke.

Patients with completely locked-in syndrome (CLIS) are incapable of any voluntary muscle movement and do not have any means of communication. Recently functional near infrared spectroscopy (fNIRS) based brain computer interface (BCI) has been successfully used to enable communication with these patients. The developed fNIRS-BCI system relies on the intactness of language comprehension in these patients in all dimensions of language. Interwoven language and motor cortex in brain, and lack of muscular activity in long run, can cause language attrition due to complete immobility in CLIS patients. In this study we have investigated effects of semantic content of sentences presented to a CLIS patient on the performance of the BCI system during a YES/NO paradigm. Comparison of communication success rate in BCI classification between different semantic categories indicate that semantic content of sentences presented to a CLIS patient can affect the BCI performance. Affected concepts are mostly associated with executive words. These findings can be beneficial towards development of more reliable communication device for patients in CLIS. In addition, these results may assist in elucidating the cognitive changes in completely paralyzed patients with the passage of time since the onset of total immovability., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Competing Interests: The authors have declared that no competing interests exist.

Background: Brain-machine interfaces (BMIs) have been recently proposed as a new tool to induce functional recovery in stroke patients., Objective: Here we evaluated long-term effects of BMI training and physiotherapy in motor function of severely paralyzed chronic stroke patients 6 months after intervention., Methods: A total of 30 chronic stroke patients with severe hand paresis from our previous study were invited, and 28 underwent follow-up assessments. BMI training included voluntary desynchronization of ipsilesional EEG-sensorimotor rhythms triggering paretic upper-limb movements via robotic orthoses (experimental group, n = 16) or random orthoses movements (sham group, n = 12). Both groups received identical physiotherapy following BMI sessions and a home-based training program after intervention. Upper-limb motor assessment scores, electromyography (EMG), and functional magnetic resonance imaging (fMRI) were assessed before (Pre), immediately after (Post1), and 6 months after intervention (Post2)., Results: The experimental group presented with upper-limb Fugl-Meyer assessment (cFMA) scores significantly higher in Post2 (13.44 ± 1.96) as compared with the Pre session (11.16 ± 1.73; P = .015) and no significant changes between Post1 and Post2 sessions. The Sham group showed no significant changes on cFMA scores. Ashworth scores and EMG activity in both groups increased from Post1 to Post2. Moreover, fMRI-BOLD laterality index showed no significant difference from Pre or Post1 to Post2 sessions., Conclusions: BMI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis and represents a promising strategy in severe stroke neurorehabilitation.

Objective: In light of the shortcomings of current restorative brain-computer interfaces (BCI), this study investigated the possibility of using EMG to detect hand/wrist extension movement intention to trigger robot-assisted training in individuals without residual movements., Methods: We compared movement intention detection using an EMG detector with a sensorimotor rhythm based EEG-BCI using only ipsilesional activity. This was carried out on data of 30 severely affected chronic stroke patients from a randomized control trial using an EEG-BCI for robot-assisted training., Results: The results indicate the feasibility of using EMG to detect movement intention in this severely handicapped population; probability of detecting EMG when patients attempted to move was higher (p 0.001) than at rest. Interestingly, 22 out of 30 (or 73%) patients had sufficiently strong EMG in their finger/wrist extensors. Furthermore, in patients with detectable EMG, there was poor agreement between the EEG and EMG intent detectors, which indicates that these modalities may detect different processes., Conclusion: A substantial segment of severely affected stroke patients may benefit from EMG-based assisted therapy. When compared to EEG, a surface EMG interface requires less preparation time, which is easier to don/doff, and is more compact in size., Significance: This study shows that a large proportion of severely affected stroke patients have residual EMG, which yields a direct and practical way to trigger robot-assisted training.

Brain-computer interfaces (BCIs) enable communication with others and allow machines or computers to be controlled in the absence of motor activity. Clinical studies evaluating neural prostheses in amyotrophic lateral sclerosis (ALS) patients have been performed; however, to date, no study has reported that ALS patients who progressed from locked-in syndrome (LIS), which has very limited voluntary movement, to a completely locked-in state (CLIS), characterized by complete loss of voluntary movements, were able to continue controlling neural prostheses. To clarify this, we used a BCI system to evaluate three late-stage ALS patients over 27 months. We employed steady-state visual evoked brain potentials elicited by flickering green and blue light-emitting diodes to control the BCI system. All participants reliably controlled the system throughout the entire period (median accuracy: 83.3%). One patient who progressed to CLIS was able to continue operating the system with high accuracy. Furthermore, this patient successfully used the system to respond to yes/no questions. Thus, this CLIS patient was able to operate a neuroprosthetic device, suggesting that the BCI system confers advantages for patients with severe paralysis, including those exhibiting complete loss of muscle movement.

Motor rehabilitation based on brain-machine interfaces (BMI) has been shown as a feasible option for stroke patients with complete paralysis. However, the pathologic EEG activity after a stroke makes the detection of movement intentions in these patients challenging, especially in those with damages involving the motor cortex. Residual electromyographic activity in those patients has been shown to be decodable, even in cases when the movement is not possible. Hybrid BMIs combining EEG and EMG activity have been recently proposed, although there is little evidence about how they work for completely paralyzed stroke patients. In this study we propose a neural interface, relying on EEG, EMG or EEG+EMG features, to detect movement attempts. Twenty patients with a chronic stroke affecting their motor cortex were recruited, and asked to open and close their paralyzed hand while their electrophysiological signals were recorded. We show how EEG and EMG activities provide complementary information for detecting the movement intentions, being the accuracy of the hybrid BMI significantly higher than the EEG-based system. The obtained results encourage the integration of hybrid BMI systems for motor rehabilitation of patients with paralysis due to stroke.

Background: Motor paralysis after stroke has devastating consequences for the patients, families and caregivers. Although therapies have improved in the recent years, traditional rehabilitation still fails in patients with severe paralysis. Brain-machine interfaces (BMI) have emerged as a promising tool to guide motor rehabilitation interventions as they can be applied to patients with no residual movement., Objective: This paper reviews the efficiency of BMI technologies to facilitate neuroplasticity and motor recovery after stroke., Methods: We provide an overview of the existing rehabilitation therapies for stroke, the rationale behind the use of BMIs for motor rehabilitation, the current state of the art and the results achieved so far with BMI-based interventions, as well as the future perspectives of neural-machine interfaces., Results: Since the first pilot study by Buch and colleagues in 2008, several controlled clinical studies have been conducted, demonstrating the efficacy of BMIs to facilitate functional recovery in completely paralyzed stroke patients with noninvasive technologies such as the electroencephalogram (EEG)., Conclusions: Despite encouraging results, motor rehabilitation based on BMIs is still in a preliminary stage, and further improvements are required to boost its efficacy. Invasive and hybrid approaches are promising and might set the stage for the next generation of stroke rehabilitation therapies.

Objective: Two-dimensional movement control is a popular issue in brain-computer interface (BCI) research and has many applications in the real world. In this paper, we introduce a combined control strategy to a binary class-based BCI system that allows the user to move a cursor in a two-dimensional (2D) plane. Users focus on a single moving vector to control 2D movement instead of controlling vertical and horizontal movement separately., Approach: Five participants took part in a fixed-target experiment and random-target experiment to verify the effectiveness of the combination control strategy under the fixed and random routine conditions. Both experiments were performed in a virtual 2D dimensional environment and visual feedback was provided on the screen., Main Results: The five participants achieved an average hit rate of 98.9% and 99.4% for the fixed-target experiment and the random-target experiment, respectively., Significance: The results demonstrate that participants could move the cursor in the 2D plane effectively. The proposed control strategy is based only on a basic two-motor imagery BCI, which enables more people to use it in real-life applications.

Objective: Brain-computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation., Approach: Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared., Main Results: Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier., Significance: Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices using EEG data. These results have important implications towards assistive and rehabilitative neuroprostheses control in paralyzed patients.

Including supplementary information from the brain or other body parts in the control of brain-machine interfaces (BMIs) has been recently proposed and investigated. Such enriched interfaces are referred to as hybrid BMIs (hBMIs) and have been proven to be more robust and accurate than regular BMIs for assistive and rehabilitative applications. Electromyographic (EMG) activity is one of the most widely utilized biosignals in hBMIs, as it provides a quite direct measurement of the motion intention of the user. Whereas most of the existing non-invasive EEG-EMG-hBMIs have only been subjected to offline testings or are limited to one degree of freedom (DoF), we present an EEG-EMG-hBMI that allows the simultaneous control of 7-DoFs of the upper limb with a robotic exoskeleton. Moreover, it establishes a biologically-inspired hierarchical control flow, requiring the active participation of central and peripheral structures of the nervous system. Contingent visual and proprioceptive feedback about the user's EEG and EMG activity is provided in the form of velocity modulation during functional task training. We believe that training with this closed-loop system may facilitate functional neuroplastic processes and eventually elicit a joint brain and muscle motor rehabilitation. Its usability is validated during a real-time operation session in a healthy participant and a chronic stroke patient, showing encouraging results for its application to a clinical rehabilitation scenario.

Brain-machine interfaces (BMI) can be used to control robotic and prosthetic devices for rehabilitation of motor disorders, such as stroke. The calibration of these BMI systems is of paramount importance in order to establish a precise contingent link between the brain activity related to movement intention and the peripheral feedback. However, electroencephalographic (EEG) activity, commonly used to build non-invasive BMIs, can be easily contaminated by artifacts of electrical or physiological origin. The way these interferences can affect the performance of movement intention decoders has not been deeply studied, especially when dealing with severely paralyzed patients, which often generate more artifacts by compensatory movements. This paper evaluates the effects of removing artifacts from the data used to train a BMI decoder on a dataset of 28 severely paralyzed stroke patients. We show that cleaning the training datasets reduces the global BMI performance for decoding attempts of movement. Further, we demonstrate that this performance drop especially affects the test trials contaminated by artifacts (i.e., trials that might not reflect cortical activity but noise), but not the clean test trials (i.e., trials representing correct cortical activity). This paper underlines the importance of cleaning the datasets used to train BMI systems to improve their efficacy for decoding movement intention and maximize their neurorehabilitative potential.

Despite partial success, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a state called complete locked-in state (CLIS). Based on a motor learning theoretical context and on the failure of neuroelectric brain-computer interface (BCI) communication attempts in CLIS, we here report BCI communication using functional near-infrared spectroscopy (fNIRS) and an implicit attentional processing procedure. Four patients suffering from advanced amyotrophic lateral sclerosis (ALS)-two of them in permanent CLIS and two entering the CLIS without reliable means of communication-learned to answer personal questions with known answers and open questions all requiring a "yes" or "no" thought using frontocentral oxygenation changes measured with fNIRS. Three patients completed more than 46 sessions spread over several weeks, and one patient (patient W) completed 20 sessions. Online fNIRS classification of personal questions with known answers and open questions using linear support vector machine (SVM) resulted in an above-chance-level correct response rate over 70%. Electroencephalographic oscillations and electrooculographic signals did not exceed the chance-level threshold for correct communication despite occasional differences between the physiological signals representing a "yes" or "no" response. However, electroencephalogram (EEG) changes in the theta-frequency band correlated with inferior communication performance, probably because of decreased vigilance and attention. If replicated with ALS patients in CLIS, these positive results could indicate the first step towards abolition of complete locked-in states, at least for ALS., Competing Interests: The authors have declared that no competing interests exist.

Goal: Stroke survivors usually require motor rehabilitation therapy as, due to the lesion, they completely or partially loss mobility in the limbs. Brain-computer interface technology offers the possibility of decoding the attempt to move paretic limbs in real time to improve existing motor rehabilitation. However, a major difficulty for the practical application of the BCI to stroke survivors is that the brain rhythms that encode the motor states might be diminished due to the lesion. This study investigates the continuous decoding of natural attempt to move the paralyzed upper limb in stroke survivors from electroencephalographic signals of the unaffected contralesional motor cortex., Results: Experiments were carried out with the aid of six severely affected chronic stroke patients performing/attempting self-selected reaching movements of the unaffected/affected upper limb. The electroencephalographic (EEG) analysis showed significant cortical activation on the uninjured motor cortex when moving the contralateral unaffected arm and in the attempt to move the ipsilateral affected arm. Using this activity, significant continuous decoding of movement was obtained in six out of six participants in movements of the unaffected limb, and in four out of six participants in the attempt to move the affected limb., Conclusion: This study showed that it is possible to construct a decoder of the attempt to move the paretic arm for chronic stroke patients using the EEG activity of the healthy contralesional motor cortex., Significance: This decoding model could provide to stroke survivors with a natural, easy, and intuitive way to achieve control of BCIs or robot-assisted rehabilitation devices.

Direct brain control of advanced robotic systems promises substantial improvements in health care, for example, to restore intuitive control of hand movements required for activities of daily living in quadriplegics, like holding a cup and drinking, eating with cutlery, or manipulating different objects. However, such integrated, brain- or neural-controlled robotic systems have yet to enter broader clinical use or daily life environments. We demonstrate full restoration of independent daily living activities, such as eating and drinking, in an everyday life scenario across six paraplegic individuals (five males, 30 ± 14 years) who used a noninvasive, hybrid brain/neural hand exoskeleton (B/NHE) to open and close their paralyzed hand. The results broadly suggest that brain/neural-assistive technology can restore autonomy and independence in quadriplegic individuals' everyday life., (Copyright © 2016, American Association for the Advancement of Science.)

Brain-computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.

Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain-Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity.

Objective: We investigated neurophysiological brain responses elicited by a tactile event-related potential paradigm in a sample of ALS patients. Underlying cognitive processes and neurophysiological signatures for brain-computer interface (BCI) are addressed., Methods: We stimulated the palm of the hand in a group of fourteen ALS patients and a control group of ten healthy participants and recorded electroencephalographic signals in eyes-closed condition. Target and non-target brain responses were analyzed and classified offline. Classification errors served as the basis for neurophysiological brain response sub-grouping., Results: A combined behavioral and quantitative neurophysiological analysis of sub-grouped data showed neither significant between-group differences, nor significant correlations between classification performance and the ALS patients' clinical state. Taking sequential effects of stimuli presentation into account, analyses revealed mean classification errors of 19.4% and 24.3% in healthy participants and ALS patients respectively., Conclusions: Neurophysiological correlates of tactile stimuli presentation are not altered by ALS. Tactile event-related potentials can be used to monitor attention level and task performance in ALS and may constitute a viable basis for future BCIs., Significance: Implications for brain-computer interface implementation of the proposed method for patients in critical conditions, such as the late stage of ALS and the (completely) locked-in state, are discussed., (Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Brain-computer interfaces (BCIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this chapter, we discuss the historical perspective of development of BCIs and the current advances of noninvasive BCIs for communication in patients with amyotrophic lateral sclerosis and for restoration of motor impairment after severe stroke. Distinct techniques have been explored to control a BCI in patient population especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS) because of their noninvasive nature and low cost. Previous studies demonstrated successful communication of patients with locked-in state (LIS) using EEG- and invasive electrocorticography-BCI and intracortical recordings when patients still showed residual eye control, but not with patients with complete LIS (ie, complete paralysis). Recently, a NIRS-BCI and classical conditioning procedure was introduced, allowing communication in patients in the complete locked-in state (CLIS). In severe chronic stroke without residual hand function first results indicate a possible superior motor rehabilitation to available treatment using BCI training. Here we present an overview of the available studies and recent results, which open new doors for communication, in the completely paralyzed and rehabilitation in severely affected stroke patients. We also reflect on and describe possible neuronal and learning mechanisms responsible for BCI control and perspective for future BMI research for communication in CLIS and stroke motor recovery., (© 2016 Elsevier B.V. All rights reserved.)

Stroke is among the leading causes of long-term disabilities leaving an increasing number of people with cognitive, affective and motor impairments depending on assistance in their daily life. While function after stroke can significantly improve in the first weeks and months, further recovery is often slow or non-existent in the more severe cases encompassing 30-50% of all stroke victims. The neurobiological mechanisms underlying recovery in those patients are incompletely understood. However, recent studies demonstrated the brain's remarkable capacity for functional and structural plasticity and recovery even in severe chronic stroke. As all established rehabilitation strategies require some remaining motor function, there is currently no standardized and accepted treatment for patients with complete chronic muscle paralysis. The development of brain-machine interfaces (BMIs) that translate brain activity into control signals of computers or external devices provides two new strategies to overcome stroke-related motor paralysis. First, BMIs can establish continuous high-dimensional brain-control of robotic devices or functional electric stimulation (FES) to assist in daily life activities (assistive BMI). Second, BMIs could facilitate neuroplasticity, thus enhancing motor learning and motor recovery (rehabilitative BMI). Advances in sensor technology, development of non-invasive and implantable wireless BMI-systems and their combination with brain stimulation, along with evidence for BMI systems' clinical efficacy suggest that BMI-related strategies will play an increasing role in neurorehabilitation of stroke., (Copyright © 2014. Published by Elsevier Inc.)

Background: Eye trackers are widely used among people with amyotrophic lateral sclerosis, and their benefits to quality of life have been previously shown. On the contrary, Brain-computer interfaces (BCIs) are still quite a novel technology, which also serves as an access technology for people with severe motor impairment., Objective: To compare a visual P300-based BCI and an eye tracker in terms of information transfer rate (ITR), usability, and cognitive workload in users with motor impairments., Methods: Each participant performed 3 spelling tasks, over 4 total sessions, using an Internet browser, which was controlled by a spelling interface that was suitable for use with either the BCI or the eye tracker. At the end of each session, participants evaluated usability and cognitive workload of the system., Results: ITR and System Usability Scale (SUS) score were higher for the eye tracker (Wilcoxon signed-rank test: ITR T = 9, P = .016; SUS T = 12.50, P = .035). Cognitive workload was higher for the BCI (T = 4; P = .003)., Conclusions: Although BCIs could be potentially useful for people with severe physical disabilities, we showed that the usability of BCIs based on the visual P300 remains inferior to eye tracking. We suggest that future research on visual BCIs should use eye tracking-based control as a comparison to evaluate performance or focus on nonvisual paradigms for persons who have lost gaze control., (© The Author(s) 2015.)

Objective: Two-dimensional (2D) movement control is an important issue in brain-computer interfaces (BCIs) research because being able to move, for example, a cursor with the brain will enable patients with motor disabilities to control their environment. However, it is still a challenge to continuously control 2D movement with a non-invasive BCI system. In this paper, we developed a 2D cursor control with motor imagery BCI tasks allowing users to move a cursor to any position by using a combination strategy. With this strategy, a user can combine multiple motor imagery tasks, alternatively or simultaneously, to control 2D movements., Approach: After a training session, six participants took part in the first control strategy experiment (the center-out experiment) to verify the effectiveness of the cursor control. Three of the six participants performed an additional experiment, in which they were required to move the cursor to hit five targets in a given sequence., Main Results: The average hit rate was more than [Formula: see text] and the trajectories were close to the shortest path. The average hit rate was more than 95.6% and the trajectories were close to the shortest path in the center-out experiment. In the additional experiment, three participants achieved a 100% hit rate with a short trajectory., Significance: The results demonstrated that users were able to effectively control the 2D movement using the proposed strategy. The present system may be used as a tool to interact with the external world.

The loss of hand function can result in severe physical and psychosocial impairment. Thus, compensation of a lost hand function using assistive robotics that can be operated in daily life is very desirable. However, versatile, intuitive, and reliable control of assistive robotics is still an unsolved challenge. Here, we introduce a novel brain/neural-computer interaction (BNCI) system that integrates electroencephalography (EEG) and electrooculography (EOG) to improve control of assistive robotics in daily life environments. To evaluate the applicability and performance of this hybrid approach, five healthy volunteers (HV) (four men, average age 26.5 ± 3.8 years) and a 34-year-old patient with complete finger paralysis due to a brachial plexus injury (BPI) used EEG (condition 1) and EEG/EOG (condition 2) to control grasping motions of a hand exoskeleton. All participants were able to control the BNCI system (BNCI control performance HV: 70.24 ± 16.71%, BPI: 65.93 ± 24.27%), but inclusion of EOG significantly improved performance across all participants (HV: 80.65 ± 11.28, BPI: 76.03 ± 18.32%). This suggests that hybrid BNCI systems can achieve substantially better control over assistive devices, e.g., a hand exoskeleton, than systems using brain signals alone and thus may increase applicability of brain-controlled assistive devices in daily life environments.

Introduction: Brain-machine interfaces (BMIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this review, we focus on the current advances of non-invasive BMIs for communication in patients with amyotrophic lateral sclerosis (ALS) and for restoration of motor impairment after severe stroke., Bmi for Als Patients: BMI represents a promising strategy to establish communication with paralyzed ALS patients as it does not need muscle engagement for its use. Distinct techniques have been explored to assess brain neurophysiology to control BMI for patients' communication, especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS). Previous studies demonstrated successful communication with ALS patients using EEG-BMI when patients still showed residual eye control, but patients with complete paralysis were unable to communicate with this system. We recently introduced functional NIRS (fNIRS)-BMI for communication in ALS patients in the complete locked-in syndrome (i.e., when ALS patients are unable to engage any muscle), opening new doors for communication in ALS patients after complete paralysis., Bmi for Stroke Motor Recovery: In addition to assisted communication, BMI is also being extensively studied for motor recovery after stroke. BMI for stroke motor recovery includes intensive BMI training linking brain activity related to patient's intention to move the paretic limb with the contingent sensory feedback of the paretic limb movement guided by assistive devices. BMI studies in this area are mainly focused on EEG- or magnetoencephalography (MEG)-BMI systems due to their high temporal resolution, which facilitates online contingency between intention to move and sensory feedback of the intended movement. EEG-BMI training was recently demonstrated in a controlled study to significantly improve motor performance in stroke patients with severe paresis. Neural basis for BMI-induced restoration of motor function and perspectives for future BMI research for stroke motor recovery are discussed., (Copyright © 2015 Elsevier Masson SAS. All rights reserved.)

In recent years, there has been an increasing interest in using electroencephalographic (EEG) activity to close the loop between brain oscillations and movement to induce functional motor rehabilitation. Rehabilitation robots or exoskeletons have been controlled using EEG activity. However, all studies have used a 2-class or one-dimensional decoding scheme. In this study we investigated EEG decoding of 5 functional movements of the same limb towards an online scenario. Six healthy participants performed a three-dimensional center-out reaching task based on direction movements (four directions and rest) wearing a 32-channel EEG cap. A BCI design based on multiclass extensions of Spectrally Weighted Common Spatial Patterns (Spec-CSP) and a linear discriminant analysis (LDA) classifier was developed and tested offline. The decoding accuracy was 5-fold cross-validated. A decoding accuracy of 39.5% on average for all the six subjects was obtained (chance level being 20%). The results of the current study demonstrate multiple functional movements decoding (significantly higher than chance level) from the same limb using EEG data. This study represents first steps towards a same limb multi degree of freedom (DOF) online EEG based BCI for motor restoration.

Brain-Machine Interfaces (BMI) allow manipulation of external devices and computers directly with brain activity without involvement of overt motor actions. The neurophysiological principles of such robotic brain devices and BMIs follow Hebbian learning rules as described and realized by Valentino Braitenberg in his book "Vehicles," in the concept of a "thought pump" residing in subcortical basal ganglia structures. We describe here the application of BMIs for brain communication in totally locked-in patients and argue that the thought pump may extinguish-at least partially-in those people because of extinction of instrumentally learned cognitive responses and brain responses. We show that Pavlovian semantic conditioning may allow brain communication even in the completely paralyzed who does not show response-effect contingencies. Principles of skill learning and habit acquisition as formulated by Braitenberg are the building blocks of BMIs and neuroprostheses.

With the advent of brain computer interfaces based on real-time fMRI (rtfMRI-BCI), the possibility of performing neurofeedback based on brain hemodynamics has become a reality. In the early stage of the development of this field, studies have focused on the volitional control of activity in circumscribed brain regions. However, based on the understanding that the brain functions by coordinated activity of spatially distributed regions, there have recently been further developments to incorporate real-time feedback of functional connectivity and spatio-temporal patterns of brain activity. The present article reviews the principles of rtfMRI neurofeedback, its applications, benefits and limitations. A special emphasis is given to the discussion of novel developments that have enabled the use of this methodology to achieve self-regulation of the functional connectivity between different brain areas and of distributed brain networks, anticipating new and exciting applications for cognitive neuroscience and for the potential alleviation of neuropsychiatric disorders., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Despite considerable growth in the field of brain-computer or brain-machine interface (BCI/BMI) research reflected in several hundred publications each year, little progress was made to enable patients in complete locked-in state (CLIS) to reliably communicate using their brain activity. Independent of the invasiveness of the BCI systems tested, no sustained direct brain control and communication was demonstrated in a patient in CLIS so far. This suggested a more fundamental theoretical problem of learning and attention in brain communication with BCI/BMI, formulated in the extinction-of-thought hypothesis. While operant conditioning and goal-directed thinking seems impaired in complete paralysis, classical conditioning of brain responses might represent the only alternative. First experimental studies in CLIS using semantic conditioning support this assumption. Evidence that quality-of-life in locked-in-state is not as limited and poor as generally believed draise doubts that "patient wills" or "advanced directives"signed long-before the locked-in-state are useful. On the contrary, they might be used as an excuse to shorten anticipated long periods of care for these patients avoiding associated financial and social burdens. Current state and availability of BCI/BMI systems urge a broader societal discourse on the pressing ethical challenges associated with the advancements in neurotechnology and BCI/BMI research.

Brain-computer interfaces (BCIs) provide a non-muscular communication channel for persons with severe motor impairments. Previous studies have shown that the aptitude with which a BCI can be controlled varies from person to person. A reliable predictor of performance could facilitate selection of a suitable BCI paradigm. Eleven severely motor impaired participants performed three sessions of a P300 BCI web browsing task. Before each session auditory oddball data were collected to predict the BCI aptitude of the participants exhibited in the current session. We found a strong relationship of early positive and negative potentials around 200 ms (elicited with the auditory oddball task) with performance. The amplitude of the P2 (r  =  -0.77) and of the N2 (r  =  -0.86) had the strongest correlations. Aptitude prediction using an auditory oddball was successful. The finding that the N2 amplitude is a stronger predictor of performance than P3 amplitude was reproduced after initially showing this effect with a healthy sample of BCI users. This will reduce strain on the end-users by minimizing the time needed to find suitable paradigms and inspire new approaches to improve performance.

Our objective was to investigate the relationship between brain-computer interface (BCI) communication skill and disease progression in amyotrophic lateral sclerosis (ALS). We sought also to assess stability of BCI communication performance over time and whether it is related to the progression of neurological impairment before entering the locked-in state. A three years follow-up, BCI evaluation in a group of ALS patients (n = 24) was conducted. For a variety of reasons only three patients completed the three years follow-up. BCI communication skill and disability level, using the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised, were assessed at admission and at each of the three follow-ups. Multiple non-parametric statistical methods were used to ensure reliability of the dependent variables: correlations, paired test and factor analysis of variance. Results demonstrated no significant relationship between BCI communication skill (BCI-CS) and disease evolution. The patients who performed the follow-up evaluations preserved their BCI-CS over time. Patients' age at admission correlated positively with the ability to achieve control over a BCI. In conclusion, disease evolution in ALS does not affect the ability to control a BCI for communication. BCI performance can be maintained in the different stages of the illness.

Objective: Regardless of the paradigm used to implement a brain-computer interface (BCI), all systems suffer from BCI-inefficiency. In the case of patients the inefficiency can be high. Some solutions have been proposed to overcome this problem, however they have not been completely successful yet., Methods: EEG from 10 healthy users was recorded during neuromuscular electrical stimulation (NMES) of hands and feet and during motor imagery (MI) of the same limbs. Features and classifiers were computed using part of these data to decode MI., Results: Offline analyses showed that it was possible to decode MI using a classifier based on afferent patterns induced by NMES and even infer a better model than with MI data., Conclusion: Afferent NMES motor patterns can support the calibration of BCI systems and be used to decode MI., Significance: This finding might be a new way to train sensorimotor rhythm (SMR) based BCI systems for healthy users having difficulties to attain BCI control. It might also be an alternative to train MI-based BCIs for users who cannot perform real movements but have remaining afferents (ALS, stroke patients)., (Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Objective: Chronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain-machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double-blind sham-controlled design proof of concept study., Methods: Thirty-two chronic stroke patients with severe hand weakness were randomly assigned to 2 matched groups and participated in 17.8 ± 1.4 days of training rewarding desynchronization of ipsilesional oscillatory sensorimotor rhythms with contingent online movements of hand and arm orthoses (experimental group, n = 16). In the control group (sham group, n = 16), movements of the orthoses occurred randomly. Both groups received identical behavioral physiotherapy immediately following BMI training or the control intervention. Upper limb motor function scores, electromyography from arm and hand muscles, placebo-expectancy effects, and functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent activity were assessed before and after intervention., Results: A significant group × time interaction in upper limb (combined hand and modified arm) Fugl-Meyer assessment (cFMA) motor scores was found. cFMA scores improved more in the experimental than in the control group, presenting a significant improvement of cFMA scores (3.41 ± 0.563-point difference, p = 0.018) reflecting a clinically meaningful change from no activity to some in paretic muscles. cFMA improvements in the experimental group correlated with changes in fMRI laterality index and with paretic hand electromyography activity. Placebo-expectancy scores were comparable for both groups., Interpretation: The addition of BMI training to behaviorally oriented physiotherapy can be used to induce functional improvements in motor function in chronic stroke patients without residual finger movements and may open a new door in stroke neurorehabilitation., (Copyright © 2013 American Neurological Association.)

Objectives: The main objective of the current study was to implement and evaluate a P300 based brain-computer interface (BCI) speller that uses directional cues of auditory stimuli, which are presented over headphones. The interstimulus interval (ISI) was successively reduced to determine the optimal combination of speed and accuracy. The study further aimed at quantifying the differences in subjective workload between the auditory and the visual P300 spelling application. The influence of workload, mood and motivation on BCI performance and P300 amplitude was investigated., Methods: Twenty healthy participants performed auditory and visual spelling tasks in an EEG experiment with online feedback., Results: Sixteen of twenty participants performed at or above a level necessary for satisfactory communication (≥70% spelling accuracy) with the auditory BCI. Average bit rates of up to 2.76 bits/min (best subject 7.43 bits/min) were achieved. A significantly higher workload was reported for the auditory speller compared to the visual paradigm. Motivation significantly influenced P300 amplitude at Pz in the auditory condition., Conclusions: The results of the online study suggest that the proposed paradigm offers a means of communication for most healthy users., Significance: The described auditory BCI can serve as a communication channel for completely paralyzed patients., (Copyright © 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Objective: Brain-computer interfaces (BCIs) provide a non-muscular communication channel for patients with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS)) or otherwise motor impaired people and are also used for motor rehabilitation in chronic stroke. Differences in the ability to use a BCI vary from person to person and from session to session. A reliable predictor of aptitude would allow for the selection of suitable BCI paradigms. For this reason, we investigated whether P300 BCI aptitude could be predicted from a short experiment with a standard auditory oddball., Methods: Forty healthy participants performed an electroencephalography (EEG) based visual and auditory P300-BCI spelling task in a single session. In addition, prior to each session an auditory oddball was presented. Features extracted from the auditory oddball were analyzed with respect to predictive power for BCI aptitude., Results: Correlation between auditory oddball response and P300 BCI accuracy revealed a strong relationship between accuracy and N2 amplitude and the amplitude of a late ERP component between 400 and 600 ms. Interestingly, the P3 amplitude of the auditory oddball response was not correlated with accuracy., Conclusions: Event-related potentials recorded during a standard auditory oddball session moderately predict aptitude in an audiory and highly in a visual P300 BCI. The predictor will allow for faster paradigm selection., Significance: Our method will reduce strain on patients because unsuccessful training may be avoided, provided the results can be generalized to the patient population.

Background: Robot-assisted training may improve motor function in some hemiparetic patients after stroke, but no physiological predictor of rehabilitation progress is reliable. Resting state functional magnetic resonance imaging (RS-fMRI) may serve as a method to assess and predict changes in the motor network., Objective: The authors examined the effects of upper-extremity robot-assisted rehabilitation (MANUS) versus an electroencephalography-based brain computer interface setup with motor imagery (MI EEG-BCI) and compared pretreatment and posttreatment RS-fMRI., Methods: In all, 9 adults with upper-extremity paresis were trained for 4 weeks with a MANUS shoulder-elbow robotic rehabilitation paradigm. In 3 participants, robot-assisted movement began if no voluntary movement was initiated within 2 s. In 6 participants, MI-BCI-based movement was initiated if motor imagery was detected. RS-fMRI and Fugl-Meyer (FM) upper-extremity motor score were assessed before and after training., Results: . The individual gain in FM scores over 12 weeks could be predicted from functional connectivity changes (FCCs) based on the pre-post differences in RS-fMRI measurements. Both the FM gain and FCC were numerically higher in the MI-BCI group. Increases in FC of the supplementary motor area, the contralesional and ipsilesional motor cortex, and parts of the visuospatial system with mostly association cortex regions and the cerebellum correlated with individual upper-extremity function improvement., Conclusion: FCC may predict the steepness of individual motor gains. Future training could therefore focus on directly inducing these beneficial increases in FC. Evaluation of the treatment groups suggests that MI is a potential facilitator of such neuroplasticity.

Minimally conscious state (MCS) is a condition of severely altered consciousness, in which patients appear to be wakeful and exhibit fluctuating but reproducible signs of awareness. MCS patients do not respond and are therefore dependent on others. In agreement with the embodied cognition assumption that motor actions influence our cognition, the absence of movement and the decrease in consequences for any type of covert or overt response may cause an extinction of goal-directed thinking. Brain-computer interfaces, which allow a direct output without muscular involvement, may be used to promote goal-directed thinking by allowing the performance of spatial and motor imagery tasks and could facilitate the interaction of MCS patients with their environment, possibly regaining some degree of communication and autonomy.

Brain computer interface (BCI) technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1) motor imagery of the hand movement without any overt movement and without feedback, (2) motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3) passive (the orthosis passively opens and closes the hand without imagery) and (4) active (overt) movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants). Group 1 (n = 9) received contingent positive feedback (participants' sensorimotor rhythm (SMR) desynchronization was directly linked to hand orthosis movements), group 2 (n = 8) contingent "negative" feedback (participants' sensorimotor rhythm synchronization was directly linked to hand orthosis movements) and group 3 (n = 7) sham feedback (no link between brain oscillations and orthosis movements). We observed that proprioceptive feedback (feeling and seeing hand movements) improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and passive movements. To summarize, we demonstrated that the use of contingent positive proprioceptive feedback BCI enhanced SMR desynchronization during motor tasks.

Brain-computer interfaces (BCIs) provide alternative methods for communicating and acting on the world, since messages or commands are conveyed from the brain to an external device without using the normal output pathways of peripheral nerves and muscles. Alzheimer's disease (AD) patients in the most advanced stages, who have lost the ability to communicate verbally, could benefit from a BCI that may allow them to convey basic thoughts (e.g., "yes" and "no") and emotions. There is currently no report of such research, mostly because the cognitive deficits in AD patients pose serious limitations to the use of traditional BCIs, which are normally based on instrumental learning and require users to self-regulate their brain activation. Recent studies suggest that not only self-regulated brain signals, but also involuntary signals, for instance related to emotional states, may provide useful information about the user, opening up the path for so-called "affective BCIs". These interfaces do not necessarily require users to actively perform a cognitive task, and may therefore be used with patients who are cognitively challenged. In the present hypothesis paper, we propose a paradigm shift from instrumental learning to classical conditioning, with the aim of discriminating "yes" and "no" thoughts after associating them to positive and negative emotional stimuli respectively. This would represent a first step in the development of a BCI that could be used by AD patients, lending a new direction not only for communication, but also for rehabilitation and diagnosis.

Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Results: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Conclusions: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. Significance: This confirms that structural brain traits contribute to individual performance in BCI use. [ABSTRACT FROM AUTHOR]

This study was designed to develop and test an auditory event-related potential (ERP) based spelling system for a brain-computer interface (BCI) and to compare user's performance between the auditory and visual modality. The spelling system, where letters in a matrix were coded with acoustically presented numbers, was tested on a group of healthy volunteers. The results were compared with a visual spelling system. Nine of the 13 participants presented with the auditory ERP spelling system scored above a predefined criterion level control for communication. Compared to the visual spelling system, users' performance was lower and the peak latencies of the auditorily evoked ERPs were delayed. It was concluded that auditorily evoked ERPs from the majority of the users could be reliably classified. High accuracies were achieved in these users, rendering item selection with a BCI based on auditory stimulation feasible for communication. [ABSTRACT FROM AUTHOR]

Abstract: Objective: To investigate the relationship between physical impairment and brain–computer interface (BCI) performance. Method: We present a meta-analysis of 29 patients with amyotrophic lateral sclerosis and six patients with other severe neurological diseases in different stages of physical impairment who were trained with a BCI. In most cases voluntary regulation of slow cortical potentials has been used as input signal for BCI-control. More recently sensorimotor rhythms and the P300 event-related brain potential were recorded. Results: A strong correlation has been found between physical impairment and BCI performance, indicating that performance worsens as impairment increases. Seven patients were in the complete locked-in state (CLIS) with no communication possible. After removal of these patients from the analysis, the relationship between physical impairment and BCI performance disappeared. The lack of a relation between physical impairment and BCI performance was confirmed when adding BCI data of patients from other BCI research groups. Conclusions: Basic communication (yes/no) was not restored in any of the CLIS patients with a BCI. Whether locked-in patients can transfer learned brain control to the CLIS remains an open empirical question. Significance: Voluntary brain regulation for communication is possible in all stages of paralysis except the CLIS. [Copyright &y& Elsevier]