This study explores the optimization of classical Brain-Computer Interface (BCI) paradigms—Motor Imagery (MI) and Steady-State Visual Evoked Potentials (SSVEP)—using a portable, dry-electrode EEG system in operating conditions. By adjusting key parameters, such as stimulation frequency, window length, and classifier type, we aim to bridge the gap between controlled laboratory settings and practical BCI applications. Experiments were conducted across varied environments and times to capture ecological variability. Our results reveal strong inter- and intra-subject performance variability, emphasizing the need for personalized adaptation. In MI, we evaluated three classification methods, with TSLDA offering the best trade-off for real-time use. For SSVEP, we assessed target properties and high-frequency stimuli to enhance comfort without sacrificing signal quality. The dry EEG device demonstrated performance comparable to wet systems, with certain participants achieving high classification accuracy and Information Transfer Rates in real-time conditions. These findings highlight the feasibility of deploying dry EEG-based BCIs outside laboratory settings, provided careful tuning of paradigm-specific parameters. This work lays a foundation for developing adaptive BCI systems designed for operational deployment.

Electroencephalography (EEG) signal cleaning has long been a critical challenge in the research community. The presence of artifacts can significantly degrade EEG data quality, complicating analysis and potentially leading to erroneous interpretations. While various artifact rejection methods have been proposed, the gold standard remains manual visual inspection by human experts-a process that is time-consuming, subjective, and impractical for large-scale EEG studies. Existing techniques are often hindered by a strong reliance on manual hyperparameter tuning, sensitivity to outliers, and high computational costs. In this paper, we introduce the improved Riemannian Potato Field (iRPF), a fast and fully automated method for EEG artifact rejection that addresses key limitations of current approaches. We evaluate iRPF against several state-of-the-art artifact rejection methods, using two publicly available EEG databases, labeled for various artifact types, comprising 226 EEG recordings. Our results demonstrate that iRPF outperforms all competitors across multiple metrics, with gains of up to 22 % in recall, 102 % in specificity, 54 % in precision, and 24 % in F1-score, compared to Isolation Forest, Autoreject, Riemannian Potato, and Riemannian Potato Field, respectively. Statistical analysis confirmed the significance of these improvements (p < 0.001) with large effect sizes (Cohen's d > 0.8) in most comparisons. Additionally, on a typical EEG recording iRPF performs artifact cleaning in under 8 ms per epoch using a standard laptop, highlighting its efficiency for large-scale EEG data processing and real-time applications. iRPF offers a robust and data-driven artifact rejection solution for high-quality EEG pre-processing in brain-computer interfaces and clinical neuroimaging applications.

Background: Exercise duration at maximum oxygen uptake (V˙O2max) appears to be influenced not only by metabolic factors but also by the interplay between brain dynamics and ventilatory regulation. This study examined how cortical activity, assessed via electroencephalography (EEG), relates to performance and acute fatigue regulation during a constant-load cycling test. We hypothesized that oscillatory activity in the theta, alpha, and beta bands would be associated with ventilatory coordination and endurance capacity. Methods: Thirty trained participants performed a cycling test to exhaustion at 90% maximal aerobic power. EEG and gas exchange were continuously recorded; ratings of perceived exertion were assessed immediately after exhaustion. Results: Beta power was negatively correlated with time spent at V˙O2max (r = −0.542, p = 0.002). Theta and Alpha power alone showed no direct associations with endurance, but EEG–metabolic ratios revealed significant correlations. Specifically, the time to reach V˙O2max correlated with Alpha/V˙O2 (p < 0.001), Alpha/V˙CO2 (p < 0.001), and Beta/V˙CO2 (p = 0.002). The time spent at V˙O2max correlated with Theta/V˙O2 (p = 0.002) and Theta/V˙CO2 (p < 0.001). The time-to-exhaustion was correlated with Theta/V˙CO2 (p < 0.001) and Alpha/V˙CO2 (p < 0.001). Conclusions: These findings indicate that cortical oscillations were associated with different aspects of acute fatigue regulation. Beta activity was associated with fatigue-related neural strain, whereas Theta and Alpha bands, when normalized to metabolic load, were consistent with a role in ventilatory coordination and motor control. EEG–metabolic ratios may provide exploratory indicators of brain–metabolism interplay during high-intensity exercise and could help guide future brain-body interactions in endurance performance.

Magneto-encephalography (MEG) provides a higher spatial resolution than electro-encephalography (EEG) to measure human auditory responses. However, conventional cryogenic MEG systems (SQUID-MEG) suffer from severe technological restrictions limiting, for instance, routine clinical use. Fortunately, a new generation of MEG sensors, optically pumped magnetometers (OPMs), has been developed to bridge the gap, combining the wearability of EEG with the benefits of MEG signal acquisition. We aim to assess their potential for studying auditory mismatch processing. The auditory mismatch negativity (MMN) is a well-characterized evoked component observable using a passive oddball paradigm with two-tone sound sequences. It has been extensively described using both EEG and MEG and is part of many EEG-based clinical applications, such as the assessment of patients with disorders of consciousness. MMN is therefore a relevant candidate to evaluate OPM performance. We use recently developed Helium-OPMs, which are high dynamic range MEG sensors that operate at room temperature. We compare their performance with cryogenic SQUID-MEG and EEG in a passive frequency oddball paradigm. Results show a significant MMN across subjects in all modalities as well as a high temporal similarity between modalities. Signal-to-noise ratios were also similar, and detection of significant individual MMN (within-subjects) using the OPM system was equal to or better than EEG. Given that the OPM system tested here is a prototype comprised of only five sensors, these results are a promising step towards wearable MEG that combines the advantages of MEG and EEG.

Neurofeedback training (NFT) aims to teach self-regulation of brain activity through real-time feedback, but suffers from highly variable outcomes and poorly understood mechanisms, hampering its validation. To address these issues, we propose a formal computational model of the NFT closed loop. Using Active Inference, a Bayesian framework modelling perception, action, and learning, we simulate agents interacting with an NFT environment. This enables us to test the impact of design choices (e.g., feedback quality, biomarker validity) and subject factors (e.g., prior beliefs) on training. Simulations show that training effectiveness is sensitive to feedback noise or bias, and to prior beliefs (highlighting the importance of guiding instructions), but also reveal that perfect feedback is insufficient to guarantee high performance. This approach provides a tool for assessing and predicting NFT variability, interpret empirical data, and potentially develop personalized training protocols.

Attention is a fundamental mechanism enabling the brain to overcome its limited capacity for parallel processing. In non-human primates, invasive electrophysiology has shown that attentional selection operates rhythmically, primarily within the alpha (∼8–12 Hz) and theta (∼4–5 Hz) bands. Whether such finely resolved control signals can be captured non-invasively in humans, and how they adapt to changing task demands, remains unclear. Using high-precision magnetoencephalography (MEG) combined with machine learning, we decoded the spatial locus of covert attention in humans performing three variants of a spatial cueing task that manipulated cue validity as well invalid trial switching rules. Spatial attention could be decoded from whole-brain MEG activity at both static and time-resolved scales, with accuracies significantly above chance (N = 30). Decoding performance decreased as cue validity was reduced, indicating that task structure shapes attentional engagement. Analysis of decoding trajectories revealed rhythmic fluctuations at ∼8–12 Hz across all tasks, demonstrating alpha-band sampling of attention. Pre-target attention became increasingly focused on the cued side, especially in the 100% Valid condition, consistent with proactive orienting. Furthermore, individual and task-specific differences in decoding strength correlated with task-variations in behavioral performance, linking the accuracy of neural attention codes to both discrimination accuracy and reaction time. These findings demonstrate that MEG can non-invasively capture dynamic, task-dependent fluctuations in spatial attention that parallel those observed in non-human primates. They reveal that attentional demands reshape the neural code for attention, modulate rhythmic sampling, and influence behavioral efficiency. This work bridges invasive primate and non-invasive human research and establishes MEG-based decoding of attention as a promising tool for mechanistic and clinical applications, including neurofeedback and attention-related interventions. ### Competing Interest Statement The authors have declared no competing interest. Agence Nationale de la Recherche, ANR-11-LABX-0042, ANR-11-IDEX-0007 European Research Council, #716862

Background: Exercise duration at maximum oxygen uptake ((V) over dotO(2)max) appears to be influenced not only by metabolic factors but also by the interplay between brain dynamics and ventilatory regulation. This study examined how cortical activity, assessed via electroencephalography (EEG), relates to performance and acute fatigue regulation during a constant-load cycling test. We hypothesized that oscillatory activity in the theta, alpha, and beta bands would be associated with ventilatory coordination and endurance capacity. Methods: Thirty trained participants performed a cycling test to exhaustion at 90% maximal aerobic power. EEG and gas exchange were continuously recorded; ratings of perceived exertion were assessed immediately after exhaustion. Results: Beta power was negatively correlated with time spent at (V) over dotO(2)max (r = -0.542, p = 0.002). Theta and Alpha power alone showed no direct associations with endurance, but EEG-metabolic ratios revealed significant correlations. Specifically, the time to reach (V) over dotO(2)max correlated with Alpha/(V) over dotO(2) (p < 0.001), Alpha/(V) over dotCO(2) (p < 0.001), and Beta/(V) over dotCO(2) (p = 0.002). The time spent at (V) over dotO(2)max correlated with Theta/(V) over dotO(2) (p = 0.002) and Theta/(V) over dotCO(2) (p < 0.001). The time-to-exhaustion was correlated with Theta/(V) over dotCO(2) (p < 0.001) and Alpha/(V) over dotCO(2) (p < 0.001). Conclusions: These findings indicate that cortical oscillations were associated with different aspects of acute fatigue regulation. Beta activity was associated with fatigue-related neural strain, whereas Theta and Alpha bands, when normalized to metabolic load, were consistent with a role in ventilatory coordination and motor control. EEG-metabolic ratios may provide exploratory indicators of brain-metabolism interplay during high-intensity exercise and could help guide future brain-body interactions in endurance performance.

The end-stage of amyotrophic lateral sclerosis (ALS) is commonly regarded as a complete Locked-In Syndrome (cLIS). Shifting the perspective from cLIS (assumed consciousness) to Cognitive Motor Dissociation (potentially demonstrable consciousness), we aimed to assess the preservation of covert awareness (internally preserved but externally inaccessible) using a multimodal battery. We evaluate two end-stage ALS patients using neurophysiological testing, passive and active auditory oddball paradigms, an auditory Brain-Computer Interface (BCI), functional activation-task imaging, long-term EEG, brain morphology, and resting-state metabolism to characterize underlying brain function. Patient 1 initially follows simple commands but fails twice at BCI control. At follow-up, command following is no longer observed and his oddball cognitive responses disappear. Patient 2, at a single evaluation, is unable to follow commands or control the BCI. Both patients exhibit altered wakefulness, brain atrophy, and a global cortico–subcortical hypometabolism pattern consistent with a disorder of consciousness, regarded as an extreme manifestation of ALS-associated fronto-temporal dementia. Although it is not possible to firmly prove the absence of awareness, each independent measure concurred with suggesting that a “degenerative disorder of consciousness” rather than a cLIS may constitute the final stage of ALS. This condition appears pathophysiologically distinct from typical tetraplegia and anarthria, in which behavioural communication and BCI use persist to enhance quality of life. Identifying the neuroimaging signatures of this condition represents a substantial milestone in understanding end-stage ALS. Large-scale longitudinal investigations are warranted to determine the prevalence of this profile among patients whose communication appears impossible. Some patients with amyotrophic lateral sclerosis become progressively paralyzed to the extent that they can no longer communicate. This raises a critical question: does consciousness remain despite a complete loss of interaction? To address this issue, we analysed brain activity in two patients at the late stage of this disease. Our findings indicate that no reliable signs of awareness could be detected, even when employing advanced methods to assess brain function, including approaches designed to enable communication without movement. Two hypotheses may account for this outcome: either prolonged paralysis progressively leads to the disappearance of consciousness, or the degeneration of brain function itself directly underlies its loss. However, our conclusions ultimately depend on the reliability of consciousness assessment tools, even when used in combination. Gobert et al. investigate whether consciousness persists in end-stage Amyotrophic Lateral Sclerosis, traditionally assumed as being fully aware but unable to communicate. Multimodal assessments, including Brain-Computer Interface, suggest some individuals may show degenerative disorder of consciousness rather than a complete Locked-In Syndrome.

Background: Exercise duration at maximum oxygen uptake (V̇˙O2max) appears to be influenced not only by metabolic factors but also by the interplay between brain dynamics and ventilatory regulation. This study examined how cortical activity, assessed via electroencephalography (EEG), relates to performance and acute fatigue regulation during a constant-load cycling test. We hypothesized that oscillatory activity in the theta, alpha, and beta bands would be associated with ventilatory coordination and endurance capacity. Methods: Thirty trained participants performed a cycling test to exhaustion at 90% maximal aerobic power. EEG and gas exchange were continuously recorded; ratings of perceived exertion were assessed immediately after exhaustion. Results: Beta power was negatively correlated with time spent at V̇˙O2max (r = −0.542, p = 0.002). Theta and Alpha power alone showed no direct associations with endurance, but EEG–metabolic ratios revealed significant correlations. Specifically, the time to reach V̇˙O2max correlated with Alpha/V̇˙O2 (p < 0.001), Alpha/V̇˙CO2 (p < 0.001), and Beta/V̇˙CO2 (p = 0.002). The time spent at V̇˙O2max correlated with Theta/V̇˙O2 (p = 0.002) and Theta/V̇˙CO2 (p < 0.001). The time-to-exhaustion was correlated with Theta/V̇˙CO2 (p < 0.001) and Alpha/V̇˙CO2 (p < 0.001). Conclusions: These findings indicate that cortical oscillations were associated with different aspects of acute fatigue regulation. Beta activity was associated with fatigue-related neural strain, whereas Theta and Alpha bands, when normalized to metabolic load, were consistent with a role in ventilatory coordination and motor control. EEG–metabolic ratios may provide exploratory indicators of brain–metabolism interplay during high-intensity exercise and could help guide future brain-body interactions in endurance performance.

Abstract Neural dynamics at the laminar level are critical for cortical computation. However, in humans, non-invasive methods to probe such dynamics have been limited to coarse distinctions between deep and superficial layers. Here, we present a multilayer magnetoencephalography source reconstruction framework and evaluate the conditions under which depth-resolved laminar inference may be feasible. Using simulations, we systematically assess the limits of magnetoencephalography depth resolution, showing that laminar discrimination depends on sufficiently high signal-to-noise ratio, precise co-registration, and accurate specification of cortical column orientation. We demonstrate that regional variations in cortical anatomy influence reconstruction fidelity, with lead-field separability emerging as a key determinant. We then apply this framework to empirical data from three independent datasets and find laminar activation patterns that align with canonical feedforward and feedback motifs in visual and sensorimotor circuits, supporting the plausibility of laminar inference under favorable conditions and offering opportunities to bridge invasive electrophysiology and human neuroimaging.

Mismatch negativity (MMN) has been frequently used to assess auditory processing and change detection in autism spectrum disorder (ASD), but findings have been fairly inconsistent. To address this issue, we conducted a systematic review and meta-analysis of MMN amplitude (76 effect sizes) and latency (62 effect sizes) in ASD to identify factors contributing to this heterogeneity and to interpret findings within the predictive coding framework. While residual heterogeneity remained, significant effects of the interaction between age group and design type (unifeature vs. multifeature, i.e., one or several types of deviants) and deviant type were found for MMN amplitude. In multifeature designs, autistic children and adolescents exhibited reduced MMN amplitudes compared to neurotypical peers (g = 0.25, p = 0.01), whereas autistic adults showed increased MMN amplitudes (g = -0.26, p = 0.02). In addition, autistic individuals had significantly smaller MMN amplitudes than neurotypical individuals in paradigms using phoneme deviants (g = 0.41, p < 0.001). Across designs, no significant MMN latency differences were observed between neurotypical and autistic individuals. These results are discussed within the predictive coding framework, as MMN responses are thought to reflect prediction errors, aligning with theories suggesting heightened prediction errors in autistic adults. Future studies with larger samples and improved data reporting are needed to further clarify the developmental trajectory and variability of MMN responses in ASD. Additionally, computational modeling approaches can help characterize learning dynamics and disentangle predictive coding accounts among autistic individuals.

Objective: Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience.Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in 'left vs. right hand' motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements.Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.

V̇O2max is recognized as a key measure in exercise physiology and sports medicine. However, only 20–50% of maximal incremental exercise tests (IET) result in a plateau of V̇O2 (V̇O2pl). To our knowledge, no study has yet examined the possible difference in brain activity during an IET, in V̇O2pl and non-plateau athletes with the same V̇O2max and age. This study aimed to shed light on the central governor hypothesis, namely that the inability to reach a V̇O2pl may be dictated by the brain rather than by a peripheral physical limit. This hypothesis can now be explored using electroencephalography (EEG) during IET, measuring concomitant power in specific frequency bands. Forty-two athletes were divided into two groups: those who practiced endurance sports and those who did not, and were asked to perform an IET. EEG signals and gas exchange were recorded. A V̇O2pl was observed in twenty-two subjects (52%). EEG power increased in all subjects during IET, except in the alpha band, which showed variability, but not significantly (64% increase, 34% decrease, p = 0.07). No differences were found between endurance athletes and non-endurance athletes, except for V̇O2max (60.10 ± 6.16 vs. 51.77 ± 6.41, p < 0.001). However, the baseline-corrected ratio of EEG power to V̇O2 was found to decrease in all subjects during IET, in the alpha, beta and theta bands. In conclusion, the presence or absence of a V̇O2pl is not related to the type of EEG response during an IET. Nevertheless, the decline in brain and V̇O2 powers/ratios in all frequency bands suggests that aerobic power may be constrained by brain mobilization.

The end-stage of amyotrophic lateral sclerosis [ALS] is presumed to be a complete Locked-In Syndrome [cLIS], assuming an internally preserved consciousness that would not be accessible anymore from the outside. However, whether consciousness persists at this stage of ALS remains to be demonstrated. Shifting the perspective from cLIS (presupposed consciousness) to Cognitive Motor Dissociation (to-be-demonstrated consciousness), we attempted to demonstrate consciousness and communication with two cLIS-ALS patients using a multimodal awareness assessment battery. It involved complete neurophysiological assessments, passive and active auditory oddball paradigm (Subject-Own-Name/P300), an auditory-based Brain-Computer-Interface [BCI] and activation-task imaging using functional MRI or [15O]H2O PET. Wakefulness (long-term EEG), brain morphology (CT or MRI scans) and resting brain metabolism ([18F]fluoro-deoxy-glucose PET) were used to describe the underlying cLIS brain function. While Patient 1 could initially follow simple commands, he failed twice to control the BCI. At follow-up, he showed no more evidence of command following and his oddball (Own Name - P300) cognitive responses has disappeared. At his unique evaluation, Patient 2 was neither able to follow simple commands nor to control the BCI. Both patients had altered wakefulness, brain atrophy, and a global cortico-sub-cortical hypometabolism pattern compatible with a disorder of consciousness, regarded as an extreme form of an ALS-associated fronto-temporal dementia. While it is not possible to firmly demonstrate the absence of awareness, each independent measure concurred with suggesting that a degenerative disorder of consciousness rather than a cLIS might be the final stage of ALS. In future cass, this dramatic cognitive decline should be anticipated before communication disappears to enable precise advance directives regarding end-of-life issues in case complete, and neurophysiologically confirmed, unresponsiveness occurs. Altogether, the neuroimaging features distinguishing the mechanisms in this rare condition is a significant milestone to understand end-stage ALS. The present clinical study calls for further exploration of this terminal stage to determine the prevalence of this profile in whom communication seems hopeless. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement AO was funded by the Fondation pour la Recherche Medicale (FRM, ING20121226307). PS, JM, EM were funded by one grant from the Fondation pour la Recherche Medicale (FRM, FDM201906008524). JM, EM and PS were funded by ANR-17-CE40-0005, MindMadeClear & ANR-20-CE17-0023, ANR HiFi. PS was funded by Perce-Neige Fondation. The teams of the Lyon Neurocience Research Center are funded by the Labex cortex. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of Hospices Civils de LYON CSE-HCL - IRB 00013204; Pr Cyrille Confavreux gave ethical approval for this work (approval N. 24-310). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript.

The cognitive mechanisms underlying subjects' self-regulation in Brain-Computer Interface (BCI) and neurofeedback (NF) training remain poorly understood. Yet, a mechanistic computational model of each individual learning trajectory is required to improve the reliability of BCI applications. The few existing attempts mostly rely on model-free (reinforcement learning) approaches. Hence, they cannot capture the strategy developed by each subject and neither finely predict their learning curve. In this study, we propose an alternative, model-based approach rooted in cognitive skill learning within the Active Inference framework. We show how BCI training may be framed as an inference problem under high uncertainties. We illustrate the proposed approach on a previously published synthetic Motor Imagery ERD laterality training. We show how simple changes in model parameters allow us to qualitatively match experimental results and account for various subject. In the near future, this approach may provide a powerful computational to model individual skill learning and thus optimize and finely characterize BCI training.

Our understanding of motor-related, macroscale brain processes has been significantly shaped by the description of the event-related desynchronization (ERD) and synchronization (ERS) phenomena in the mu and beta frequency bands prior to, during, and following movement. The demonstration of reproducible, spatially- and band-limited signal power changes has, consequently, attracted the interest of non-invasive brain-computer interface (BCI) research for a long time. BCIs often rely on motor imagery (MI) experimental paradigms that are expected to generate brain signal modulations analogous to movement-related ERD and ERS. However, a number of recent neuroscience studies has questioned the nature of these phenomena. Beta band activity has been shown to occur, on a single-trial level, in short, transient, and heterogeneous events termed bursts rather than sustained oscillations. In a previous study, we established that an analysis of hand MI binary classification tasks based on beta bursts can be superior to beta power in terms of classification score. In this article, we elaborate on this idea, proposing a signal processing algorithm that is comparable to- and compatible with state-of-the-art techniques. Our pipeline filters brain recordings by convolving them with kernels extracted from beta bursts and then applies spatial filtering before classification. This data-driven filtering allowed for a simple and efficient analysis of signals from multiple sensors, thus being suitable for online applications. By adopting a time-resolved decoding approach, we explored MI dynamics and showed the specificity of the new classification features. In accordance with previous results, beta bursts improved classification performance compared to beta band power, while often increasing information transfer rate compared to state-of-the-art approaches.

This paper presents a completely automatic artifact rejection method for electroencephalographic data based on Riemannian geometry. The proposed method is tested on two P300-based brain-computer interface databases and is shown to allow superior accuracy and information transfer rate as compared to two state-of-the-art methods.

Background The locked-in syndrome (LIS), due to a lesion in the pons, impedes communication. This situation can also be met after some severe brain injury or in advanced Amyotrophic Lateral Sclerosis (ALS). In the most severe condition, the persons cannot communicate at all because of a complete oculomotor paralysis (Complete LIS or CLIS). This even prevents the detection of consciousness. Some studies suggest that auditory brain–computer interface (BCI) could restore a communication through a « yes–no» code. Methods We developed an auditory EEG-based interface which makes use of voluntary modulations of attention, to restore a yes–no communication code in non-responding persons. This binary BCI uses repeated speech sounds (alternating “yes” on the right ear and “no” on the left ear) corresponding to either frequent (short) or rare (long) stimuli. Users are instructed to pay attention to the relevant stimuli only. We tested this BCI with 18 healthy subjects, and 7 people with severe motor disability (3 “classical” persons with locked-in syndrome and 4 persons with ALS). Results We report online BCI performance and offline event-related potential analysis. On average in healthy subjects, online BCI accuracy reached 86% based on 50 questions. Only one out of 18 subjects could not perform above chance level. Ten subjects had an accuracy above 90%. However, most patients could not produce online performance above chance level, except for two people with ALS who obtained 100% accuracy. We report individual event-related potentials and their modulation by attention. In addition to the classical P3b, we observed a signature of sustained attention on responses to frequent sounds, but in healthy subjects and patients with good BCI control only. Conclusions Auditory BCI can be very well controlled by healthy subjects, but it is not a guarantee that it can be readily used by the target population of persons in LIS or CLIS. A conclusion that is supported by a few previous findings in BCI and should now trigger research to assess the reasons of such a gap in order to propose new and efficient solutions. Clinical trial registrations : No. NCT02567201 (2015) and NCT03233282 (2013).

Objective: Early functional evaluation and prognosis of patients with disorders of consciousness is a major challenge that clinical assessments alone cannot solve. Objective measures of brain activity could help resolve this uncertainty. We used electroencephalogram at bedside to detect voluntary attention with a paradigm previously validated in healthy subjects. Methods: Using auditory-oddball sequences, our approach rests on detecting known attentional modulations of Event Related Potentials that reflect compliance with verbal instructions. Sixty-eight unresponsive patients were tested in their first year after coma onset (37 coma and 31 first year post-coma patients). Their evolution 6 months after the test was considered. Results: Fourteen of the 68 patients, showed a positive response. Nine were in a coma and 5 in a minimally conscious state (MCS). Except for one who died early, all responders evolved to exit-MCS within 6 months (93%), while 35 (65%) among non-responders only. Conclusions: Among those patients for whom the outcome is highly uncertain, 21% responded positively to this simple but cognitively demanding test. Strikingly, some coma patients were among responders. Significance: The proposed paradigm revealed cognitive-motor dissociation in some coma patients. This ability to sustain attention on demand predicted awakening within 6 months and represents an immediately useful information for relatives and caregivers. (c) 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.