Background Stroke often leads to motor disabilities, and effective rehabilitation is crucial for restoring limb function. Gamified rehabilitation programs have emerged to increase stroke survivor motivation through engaging environments and rewards. However, it is uncertain whether stroke survivors respond to video game environments and challenges in the same way as healthy individuals do, given the potential impact of stroke on mood and intrinsic motivation. Methods EEG data were collected from stroke survivors and healthy controls under multiple conditions. Initially, a cognitively activated state was identified via an N-back task. The participants subsequently engaged with an upper limb rehabilitation game featuring an immersive 3D environment. The brain activity patterns during this engaging game were then compared to those during a version of the same task without a 3D, gamified environment. Finally, responses to a more challenging iteration of the engaging game were assessed. Results While healthy controls showed brain activity indicative of effort and attention in N-back and subsequent games, stroke survivors displayed similar brain activation patterns only when playing the engaging version of the game. Notably, increasing the game difficulty increased attentional states in controls but not in stroke survivors. Conclusions Our findings suggest that an engagement mental state, as indexed by EEG markers, is more difficult to achieve in stroke survivors during repetitive tasks than in healthy controls. However, engaging in video game environments can significantly increase stroke survivors' motivation and participation, provided that the difficulty level is appropriately adjusted. We believe this provides evidence that video game-based rehabilitation is a promising approach to promote stroke survivor engagement through enhanced motivation.

Chronic pain presents a tremendous personal, societal, and financial burden. Treatment options are often limited to managing symptoms as opposed to treating the causes, and the consequences of pharmacological treatments can be immensely harmful as evidenced by the on-going opioid epidemic. Virtual Reality interventions and digital therapeutics provide a new tool for therapists but are currently limited to pain-distraction or digitizing traditional approaches that are similarly focused on pain management. However, recent research suggests that potentially neurorestorative, non-pharmacological treatments are conceivable using personalized, embodied, multimodal feedback. In particular, i) providing visual feedback of the affected body part, ii) increasing its embodiment using multisensory stimulation, and iii) timing stimulation to the systole of the ECG cycle have been linked with analgesic effects in induced acute and chronic pain. Based on these findings and methods, we here propose a stand-alone solution that aims to provide neurorestorative feedback for individuals suffering from chronic pain by visualizing interoceptive signals and mapping them onto the patients affected body part using Augmented Reality, tapping into the three aforementioned principles. Our initial findings indicate the feasibility, acceptance, and stimulus adherence of the device and intervention, whose efficacy will be evaluated in an upcoming randomized controlled clinical trial.

The susceptibility of electroencephalography (EEG) signal to artifacts is considered a major obstacle preventing the deployment of relevant non-invasive neurotechnology. In spite of a large body of literature dedicated to the identification, rejection and removal of artifactual components in EEG, the study of the impact that different artifacts may have on the EEG signal properties has been mostly qualitative and focused on the source (e.g. muscle activity, electromagnetic interference) rather than the function generating them. This work takes advantage of a unique dataset where EEG of 12 participants elicited during the execution of 9 common human activities (e.g., speaking, blinking, etc.) is co-registered with electromyography (EMG), electrooculography (EOG), accelerometer and gyroscope sensors, and baselined to “resting” (artifact-free) intervals to allow an exact, quantified assessment of the impact of artifacts. We examine several metrics capturing different facets of the influence of artifacts on EEG and measure the extent to which a state-of-the-art artifact removal method is able to eliminate them. In addition to an in-depth, quantified profiling of functional EEG artifacts, our work provides valuable information for precisely tuning the hyper-parameters of artifact rejection and removal algorithms and for designing realistic brain-computer interface (BCI) applications.

This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters through expectation-maximization (EM) improves over the regular unsupervised case and can approach the performances of supervised learning, despite the absence of any explicit ground-truth data labeling. By direct application of the missing information principle (MIP), the algorithms' performances are proven to range between the conventional supervised and unsupervised MLE extremities proportionally to the information content of the contextual assistance provided. The acquired benefits regard higher estimation precision, smaller standard errors, faster convergence rates, and improved classification accuracy or regression fitness shown in various scenarios while also highlighting important properties and differences among the outlined situations. Applicability is showcased with three real-world unsupervised classification scenarios employing Gaussian mixture models. Importantly, we exemplify the natural extension of this methodology to any type of generative model by deriving an equivalent context-aware algorithm for variational autoencoders (VAs), thus broadening the spectrum of applicability to unsupervised deep learning with artificial neural networks. The latter is contrasted with a neural-symbolic algorithm exploiting side information.

Brain-computer interfaces (BCIs) and virtual reality (VR) are two technologic advances that are changing our way of interacting with the world. BCIs can be used to influence and can serve as a control mechanism in navigation tasks, communication, or other assistive functions. VR can create ad hoc interactive scenarios that involve all our senses, stimulate the brain in a multisensory fashion, and increase the motivation and fun with game-like environments. VR and motion tracking enable natural human-computer interaction at cognitive and physical levels. This includes both brain and body in the design of meaningful VR experiences; these cases in which participants feel naturally present could help augment the benefits of BCIs for assistive and neurorehabilitation applications for the relearning of motor and cognitive skills. VR technology is now available at the consumer level thanks to the proliferation of affordable head-mounted displays (HMDs). Merging both technologies into simplified, practical devices may help democratize these technologies.

Closed-loop or adaptive deep brain stimulation (DBS) for Parkinson’s Disease (PD) has shown comparable clinical improvements to continuous stimulation, yet with less stimulation times and side effects. In this form of control, stimulation is driven by pathological beta oscillations recorded from the subthalamic nucleus, which have been shown to correlate with PD motor symptoms. An important consideration is that beta activity is itself modulated during volitional movements, yet it is unknown the impact that these volitional modulations may have on the efficacy of closed-loop systems. Here, three PD patients performed a functional reaching task during closed-loop stimulation while we measured their motor behavior. Our results show that closed-loop stimulation can alter motor performance at distinct movement intervals. Of particular relevance, closed-loop DBS compromised behavior during the returning period by increasing the amount of submovements executed, and in turn delayed movement termination. Following these findings, we hypothesize that the use of machine learning decoding different movement intervals to fully switch off the stimulator may be beneficial, and present here an exemplary approach decoding the initiation of the movement returning interval above chance level. These findings highlight the importance of evaluating these systems during functional tasks, and the need of extracting more robust biomarkers encoding ongoing symptoms or tasks execution intervals.* DBS : deep brain stimulation LFP : local field potentials PD : Parkinson’s disease STN : subthalamic nucleus LMM : linear mixed model UPDRS : Unified Parkinson’s Disease rating scale CLDBS : closed-loop deep brain stimulation AUC : area under the receiver-operating characteristic curve

Hand grasping is a sophisticated motor task that has received much attention by the neuroscientific community, which demonstrated how grasping activates a network involving parietal, pre-motor and motor cortices using fMRI, ECoG, LFPs and spiking activity. Yet, there is a need for a more precise spatio-temporal analysis as it is still unclear how these brain activations over large cortical areas evolve at the sub-second level. In this study, we recorded ten human participants (1 female) performing visually-guided, self-paced reaching and grasping with precision or power grips. Following the results, we demonstrate the existence of neural correlates of grasping from broadband EEG in self-paced conditions and show how neural correlates of precision and power grasps differentially evolve as grasps unfold. 100 ms before the grasp is secured, bilateral parietal regions showed increasingly differential patterns. Afterwards, sustained differences between both grasps occurred over the bilateral motor and parietal regions, and medial pre-frontal cortex. Furthermore, these differences were sufficiently discriminable to allow single-trial decoding with 70% decoding performance. Functional connectivity revealed differences at the network level between grasps in fronto-parietal networks, in terms of upper-alpha cortical oscillatory power with a strong involvement of ipsilateral hemisphere. Our results supported the existence of fronto-parietal recurrent feedback loops, with stronger interactions for precision grips due to the finer motor control required for this grasping type.

Brain-computer interfaces (BCI) are used in stroke rehabilitation to translate brain signals into intended movements of the paralyzed limb. However, the efficacy and mechanisms of BCI-based therapies remain unclear. Here we show that BCI coupled to functional electrical stimulation (FES) elicits significant, clinically relevant, and lasting motor recovery in chronic stroke survivors more effectively than sham FES. Such recovery is associated to quantitative signatures of functional neuroplasticity. BCI patients exhibit a significant functional recovery after the intervention, which remains 6–12 months after the end of therapy. Electroencephalography analysis pinpoints significant differences in favor of the BCI group, mainly consisting in an increase in functional connectivity between motor areas in the affected hemisphere. This increase is significantly correlated with functional improvement. Results illustrate how a BCI–FES therapy can drive significant functional recovery and purposeful plasticity thanks to contingent activation of body natural efferent and afferent pathways.

Excessive beta oscillatory activity in the subthalamic nucleus (STN) is linked to Parkinson's Disease (PD) motor symptoms. However, previous works have been inconsistent regarding the functional role of beta activity in untreated Parkinsonian states, questioning such role. We hypothesized that this inconsistency is due to the influence of electrophysiological broadband activity -a neurophysiological indicator of synaptic excitation/inhibition ratio- that could confound measurements of beta activity in STN recordings. Here we propose a data-driven, automatic and individualized mathematical model that disentangles beta activity and 1/f broadband activity in the STN power spectrum, and investigate the link between these individual components and motor symptoms in thirteen Parkinsonian patients. We show, using both modeled and actual data, how beta oscillatory activity significantly correlates with motor symptoms (bradykinesia and rigidity) only when broadband activity is not considered in the biomarker estimations, providing solid evidence that oscillatory beta activity does correlate with motor symptoms in untreated PD states as well as the significant impact of broadband activity. These findings emphasize the importance of data-driven models and the identification of better biomarkers for characterizing symptom severity and closed-loop applications.

Background Excessive beta oscillatory activity in the subthalamic nucleus (STN) is linked to Parkinson’s disease and associated motor symptoms. However, the relationship between beta activity and motor symptoms has been inconsistent, which may influence the efficacy of closed-loop deep brain stimulation. Hypothesis We hypothesized that this variability is due to the degree of neural noise in STN recordings. Recent evidence has shown that neural noise is influenced by multiple factors, such as development, aging and disease, and could confound measures of beta activity. In this work, we propose a model that disentangles beta oscillatory activity and neural noise in the STN power spectrum. Methods We investigated the impact of neural noise on estimations of beta activity and motor symptoms from data recorded bilaterally from the subthalamic nuclei of thirteen Parkinsonian patients. Results Results showed that the relationship between beta oscillatory amplitude and motor symptoms (bradykinesia and rigidity) significantly improved when neural noise was removed from the estimation of beta activity. Conclusion These findings emphasize the importance of modeling neural components independently for understanding physiological processes associated with Parkinson’s disease, and identifying better biomarkers for characterizing symptom severity. Subsequently, we predict that our findings can have a direct application for closed-loop deep brain stimulation on Parkinson’s Disease.

In this paper, we present and analyze an event distribution system for brain-computer interfaces. Events are commonly used to mark and describe incidents during an experiment and are therefore critical for later data analysis or immediate real-time processing. The presented approach, called Tools for brain-computer interaction interface D (TiD), delivers messages in XML format via a buslike system using transmission control protocol connections or shared memory. A dedicated server dispatches TiD messages to distributed or local clients. The TiD message is designed to be flexible and contains time stamps for event synchronization, whereas events describe incidents, which occur during an experiment. TiD was tested extensively toward stability and latency. The effect of an occurring event jitter was analyzed and benchmarked on a reference implementation under different conditions as gigabit and 100-Mb Ethernet or Wi-Fi with a different number of event receivers. A 3-dB signal attenuation, which occurs when averaging jitter influenced trials aligned by events, is starting to become visible at around 1-2 kHz in the case of a gigabit connection. Mean event distribution times across operating systems are ranging from 0.3 to 0.5ms for a gigabit network connection for 106 events. Results for other environmental conditions are available in this paper. References already using TiD for event distribution are provided showing the applicability of TiD for event delivery with distributed or local clients.

During the last years, several studies have suggested that Brain-Computer Interface (BCI) can play a critical role in the field of motor rehabilitation. In this case report, we aim to investigate the feasibility of a covert visuospatial attention (CVSA) driven BCI in three patients with left spatial neglect (SN). We hypothesize that such a BCI is able to detect attention task-specific brain patterns in SN patients and can induce significant changes in their abnormal cortical activity (α-power modulation, feature recruitment, and connectivity). The three patients were asked to control online a CVSA BCI by focusing their attention at different spatial locations, including their neglected (left) space. As primary outcome, results show a significant improvement of the reaction time in the neglected space between calibration and online modalities (p < 0.01) for the two out of three patients that had the slowest initial behavioral response. Such an evolution of reaction time negatively correlates (p < 0.05) with an increment of the Individual α-Power computed in the pre-cue interval. Furthermore, all patients exhibited a significant reduction of the inter-hemispheric imbalance (p < 0.05) over time in the parieto-occipital regions. Finally, analysis on the inter-hemispheric functional connectivity suggests an increment across modalities for regions in the affected (right) hemisphere and decrement for those in the healthy. Although preliminary, this feasibility study suggests a possible role of BCI in the therapeutic treatment of lateralized, attention-based visuospatial deficits.

Introduction: Performance variation is one of the main challenges that BCIs are confronted with, when being used over extended periods of time. Several methods have been proposed to find correlates of performance variation for sensorimotor rhythms EEG-based BCIs [1]. However, they typically focus on assessing performance variations within the same day or session, and do not use this information online. This issue is even more critical for end users, as they usually achieve a limited level of performance [2]. Previously, we proposed that some issues resulting from performance variation could be overcome by providing adaptive assistance based on the users’ needs. Therefore, we suggested a method for providing online adaptive assistance (i.e., modulating the timeout to deliver the intended command) based on an estimation of the user’s performance (i.e., the command delivery time, CDT, for a single trial) [3]. We previously reported results on able-bodied subjects (N=9). Here, we test the approach with an end-user with locked-in syndrome.

One of the challenges of using brain-computer interfaces (BCIs) over extended periods of time is the variation of the users' performance from one experimental day to another. The goal of the current study is to propose a performance estimator for an electroencephalography-based motor imagery BCI by assessing the reliability of a command (i.e., predicting a 'short' or 'long' command delivery time, CDT). Using a short time window (<;1.5 s, shorter than the delivery time) of the mental task execution and a linear discriminant analysis classifier, we could reliably differentiate between short and long CDT (Area under the sensitivity-specificity curve, AUC . 0.8) for 9 healthy subjects. Moreover, we assessed the feasibility of providing online adaptive assistance using the performance estimator in a BCI game by comparing two conditions: (i) allowing a 'fixed timeout' to deliver each command or (ii) providing 'adaptive assistance' by giving more time if the performance estimator detects a long CDT. The results revealed that providing adaptive assistance increases the ratio of correct commands significantly cantly (p <;0.01). Moreover, the task load index (measured via the NASA TLX questionnaire) shows a significantly higher user acceptance in case of providing adaptive assistance (p <;0.01). Furthermore, the results obtained in this study were used to simulate a robotic navigation scenario, which showed how adaptive assistance improved performance.