Centrally controlled heart rate changes during mental practice in immersive virtual environment: a case study with a tetraplegic.

A tetraplegic patient was able to induce midcentral localized beta oscillations in the electroencephalogram (EEG) after extensive mental practice of foot motor imagery. This beta oscillation was used to simulate a wheel chair movement in a virtual environment (VE). The analysis of electrocardiogram (ECG) data revealed that the induced beta oscillations were accompanied by a characteristic heart rate (HR) change in form of a preparatory HR acceleration followed by a short-lasting deceleration in the order of 10-20 bpm (beats-per-minute). This provides evidence that mental practice of motor performance is accompanied not only by activation of cortical structures but also by central commands into the cardiovascular system with its nuclei in the brain stem.

Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke.

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.

A fully automated correction method of EOG artifacts in EEG recordings.

OBJECTIVE: A fully automated method for reducing EOG artifacts is presented and validated. METHODS: The correction method is based on regression analysis and was applied to 18 recordings with 22 channels and approx. 6 min each. Two independent experts scored the original and corrected EEG in a blinded evaluation. RESULTS: The expert scorers identified in 5.9% of the raw data some EOG artifacts; 4.7% were corrected. After applying the EOG correction, the expert scorers identified in another 1.9% of the data some EOG artifacts, which were not recognized in the uncorrected data. CONCLUSIONS: The advantage of a fully automated reduction of EOG artifacts justifies the small additional effort of the proposed method and is a viable option for reducing EOG artifacts. The method has been implemented for offline and online analysis and is available through BioSig, an open source software library for biomedical signal processing. SIGNIFICANCE: Visual identification and rejection of EOG-contaminated EEG segments can miss many EOG artifacts, and is therefore not sufficient for removing EOG artifacts. The proposed method was able to reduce EOG artifacts by 80%.

Seperability of four-class motor imagery data using independent components analysis.

This paper compares different ICA preprocessing algorithms on cross-validated training data as well as on unseen test data. The EEG data were recorded from 22 electrodes placed over the whole scalp during motor imagery tasks consisting of four different classes, namely the imagination of right hand, left hand, foot and tongue movements. Two sessions on different days were recorded for eight subjects. Three different independent components analysis (ICA) algorithms (Infomax, FastICA and SOBI) were studied and compared to common spatial patterns (CSP), Laplacian derivations and standard bipolar derivations, which are other well-known preprocessing methods. Among the ICA algorithms, the best performance was achieved by Infomax when using all 22 components as well as for the selected 6 components. However, the performance of Laplacian derivations was comparable with Infomax for both cross-validated and unseen data. The overall best four-class classification accuracies (between 33% and 84%) were obtained with CSP. For the cross-validated training data, CSP performed slightly better than Infomax, whereas for unseen test data, CSP yielded significantly better classification results than Infomax in one of the sessions.

15 years of BCI research at Graz University of Technology: current projects.

Over the last 15 years, the Graz Brain-Computer Interface (BCI) has been developed and all components such as feature extraction and classification, mode of operation, mental strategy, and type of feedback have been investigated. Recent projects deal with the development of asynchronous BCIs, the presentation of feedback and applications for communication and control.

Cardiac responses induced during thought-based control of a virtual environment.

Cardiac responses induced by motor imagery were investigated in 3 subjects in a series of experiments with a synchronous (cue-based) Brain-Computer Interface (BCI). The cue specified right hand vs. leg/foot motor imagery. After a number of BCI training sessions reaching a classification accuracy of at least 80%, the BCI experiments were carried out in an immersive virtual environment (VE), commonly referred as a "CAVE". In this VE, the subjects were able to move along a virtual street by motor imagery alone. The thought-based control of VE resulted in an acceleration of the heart rate in 2 subjects and a heart rate deceleration in the other subject. In control experiments in front of a PC, all 3 subjects displayed a significant heart rate deceleration of the order of about 3-5%. This heart rate decrease during motor imagery in a normal environment is similar to that observed during preparation for a voluntary movement. The heart rate acceleration in the VE is interpreted as effect of an increased mental effort to walk as far as possible in VE.

Context-aware adaptive spelling in motor imagery BCI.

OBJECTIVE: This work presents a first motor imagery-based, adaptive brain-computer interface (BCI) speller, which is able to exploit application-derived context for improved, simultaneous classifier adaptation and spelling. Online spelling experiments with ten able-bodied users evaluate the ability of our scheme, first, to alleviate non-stationarity of brain signals for restoring the subject's performances, second, to guide naive users into BCI control avoiding initial offline BCI calibration and, third, to outperform regular unsupervised adaptation. APPROACH: Our co-adaptive framework combines the BrainTree speller with smooth-batch linear discriminant analysis adaptation. The latter enjoys contextual assistance through BrainTree's language model to improve online expectation-maximization maximum-likelihood estimation. MAIN RESULTS: Our results verify the possibility to restore single-sample classification and BCI command accuracy, as well as spelling speed for expert users. Most importantly, context-aware adaptation performs significantly better than its unsupervised equivalent and similar to the supervised one. Although no significant differences are found with respect to the state-of-the-art PMean approach, the proposed algorithm is shown to be advantageous for 30% of the users. SIGNIFICANCE: We demonstrate the possibility to circumvent supervised BCI recalibration, saving time without compromising the adaptation quality. On the other hand, we show that this type of classifier adaptation is not as efficient for BCI training purposes.

The effect of multimodal and enriched feedback on SMR-BCI performance.

OBJECTIVE: This study investigated the effect of multimodal (visual and auditory) continuous feedback with information about the uncertainty of the input signal on motor imagery based BCI performance. A liquid floating through a visualization of a funnel (funnel feedback) provided enriched visual or enriched multimodal feedback. METHODS: In a between subject design 30 healthy SMR-BCI naive participants were provided with either conventional bar feedback (CB), or visual funnel feedback (UF), or multimodal (visual and auditory) funnel feedback (MF). Subjects were required to imagine left and right hand movement and were trained to control the SMR based BCI for five sessions on separate days. RESULTS: Feedback accuracy varied largely between participants. The MF feedback lead to a significantly better performance in session 1 as compared to the CB feedback and could significantly enhance motivation and minimize frustration in BCI use across the five training sessions. CONCLUSION: The present study demonstrates that the BCI funnel feedback allows participants to modulate sensorimotor EEG rhythms. Participants were able to control the BCI with the funnel feedback with better performance during the initial session and less frustration compared to the CB feedback. SIGNIFICANCE: The multimodal funnel feedback provides an alternative to the conventional cursorbar feedback for training subjects to modulate their sensorimotor rhythms.

Exploring virtual environments with an EEG-based BCI through motor imagery.

In this paper, we describe the possibility of navigating in a virtual environment using the output signal of an EEG-based Brain-Computer Interface (BCI). The graphical capabilities of virtual reality (VR) should help to create new BCI-paradigms and improve feedback presentation. The objective of this combination is to enhance the subject's learning process of gaining control of the BCI. In this study, the participant had to imagine left or right hand movements while exploring a virtual conference room. By imaging a left hand movement the subject turned virtually to the left inside the room and with right hand imagery to the right. In fact, three trained subjects reached 80% to 100% BCI classification accuracy in the course of the experimental sessions. All subjects were able to achieve a rotation in the VR to the left or right by approximately 45 degrees during one trial.

Child maltreatment surveillance: enumeration, monitoring, evaluation and insight.

Application of epidemiologic surveillance to child abuse and neglect (CAN) presents specific challenges related to varying definitions and incident reporting. Definitions of abuse and neglect differ within and across countries, obscuring estimates of the true magnitude of the problem. Definitions also vary depending on the nature of the child protection system. Countries may lack legal or social systems with specific responsibility for responding to and recording reports of CAN, particularly countries where populations are remote or in flux (e.g. due to conflict). Underreporting of CAN results in underestimates of prevalence. Violence by caregivers toward children is often known only to the perpetrator, and depending on the developmental capacity of the child, the victim. Further, CAN cases may be reported to a wide variety of sentinels (e.g. educators, clergy, physicians, law enforcement, child welfare), or may not be reported to any official source at all. Social stigma and unintended consequences of reporting, as well as cultural and political barriers, also impact reporting both within communities and globally.

TITRE: Commentaire - Surveillance de la maltraitance des enfants : recension, suivi, évaluation et aperçu. RÉSUMÉ: Exercer une surveillance épidémiologique sur la violence et la négligence envers les enfants présente des difficultés spécifiques liées à la variabilité des définitions et des modalités dans le rapport des cas. Les définitions de la violence et de la négligence diffèrent entre les pays et en leur sein, empêchant une estimation exacte de l'ampleur du problème. Les définitions varient également en fonction de la nature du système de protection des enfants. Certains pays ne disposent pas de système judiciaire ou social doté de responsabilités précises pour réagir aux cas de violence et de négligence envers les enfants et en consigner les rapports, ce qui est en particulier le cas des pays où certaines populations sont isolées ou en migration (p. ex. en raison de conflits). La sous-déclaration des cas de violence et de négligence envers les enfants entraîne des sous-estimations de la prévalence. Les sévices subis par les enfants ne sont souvent connus que de l'agresseur et, selon le niveau de développement de l'enfant, de sa victime. De plus, les cas de violence et de négligence envers les enfants sont susceptibles d'être signalés à divers intervenants (éducateurs, clergé, médecins, services de police, aide sociale à l'enfance) comme ils peuvent ne l'être à aucune source officielle. La stigmatisation sociale et les conséquences non prévisibles des signalements, tout comme les barrières culturelles et politiques, ont aussi des répercussions sur le signalement, tant au sein d'une collectivité qu'à l'échelle planétaire.

Sex differences in neonates' cuddliness.

The authors undertook the present study to determine whether under ecologically valid, low-stress conditions, female and male neonates could be differentiated on cuddliness. Sixteen female and 15 male neonates were videotaped interacting briefly with both a female and a male adult who were blind to the sex of the neonate. Raters coded degree of cuddliness and activity level. Results showed that raters could discriminate the sex of the neonate on the basis of degree of cuddliness. Discussion focuses on the importance of theoretical and methodological considerations in assessing sex differences in behavioral characteristics of neonates.

Modular organization of reaching and grasping movements investigated using EEG microstates.

How movements are generated and controlled by the central nervous system (CNS) is still not well understood. In this work, we tested the hypothesis of a modular organization of the brain activity during the execution of voluntary movements. In particular, we extracted meta-stable topographies as a measure for global brain state, so-called microstates, from electroencephalography (EEG) data during pure planar reaching movements as well as reaching and grasping of different objects, and we compared them with those extracted during resting-state. The results showed the emergence of specific EEG microstates related to movement execution. Our results provide evidence about the benefits of EEG microstate analysis for motor control studies and their importance to better understand brain reorganization in neurological pathologies.

Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller.

OBJECTIVE: While brain-computer interfaces (BCIs) for communication have reached considerable technical maturity, there is still a great need for state-of-the-art evaluation by the end-users outside laboratory environments. To achieve this primary objective, it is necessary to augment a BCI with a series of components that allow end-users to type text effectively. APPROACH: This work presents the clinical evaluation of a motor imagery (MI) BCI text-speller, called BrainTree, by six severely disabled end-users and ten able-bodied users. Additionally, we define a generic model of code-based BCI applications, which serves as an analytical tool for evaluation and design. MAIN RESULTS: We show that all users achieved remarkable usability and efficiency outcomes in spelling. Furthermore, our model-based analysis highlights the added value of human-computer interaction techniques and hybrid BCI error-handling mechanisms, and reveals the effects of BCI performances on usability and efficiency in code-based applications. SIGNIFICANCE: This study demonstrates the usability potential of code-based MI spellers, with BrainTree being the first to be evaluated by a substantial number of end-users, establishing them as a viable, competitive alternative to other popular BCI spellers. Another major outcome of our model-based analysis is the derivation of a 80% minimum command accuracy requirement for successful code-based application control, revising upwards previous estimates attempted in the literature.

An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation.

OBJECTIVE: In this work we present--for the first time--the online operation of an electroencephalogram (EEG) brain-computer interface (BCI) system based on covert visuospatial attention (CVSA), without relying on any evoked responses. Electrophysiological correlates of pure top-down CVSA have only recently been proposed as a control signal for BCI. Such systems are expected to share the ease of use of stimulus-driven BCIs (e.g. P300, steady state visually evoked potential) with the autonomy afforded by decoding voluntary modulations of ongoing activity (e.g. motor imagery). APPROACH: Eight healthy subjects participated in the study. EEG signals were acquired with an active 64-channel system. The classification method was based on a time-dependent approach tuned to capture the most discriminant spectral features of the temporal evolution of attentional processes. The system was used by all subjects over two days without retraining, to verify its robustness and reliability. MAIN RESULTS: We report a mean online accuracy across the group of 70.6 ± 1.5%, and 88.8 ± 5.8% for the best subject. Half of the participants produced stable features over the entire duration of the study. Additionally, we explain drops in performance in subjects showing stable features in terms of known electrophysiological correlates of fatigue, suggesting the prospect of online monitoring of mental states in BCI systems. SIGNIFICANCE: This work represents the first demonstration of the feasibility of an online EEG BCI based on CVSA. The results achieved suggest the CVSA BCI as a promising alternative to standard BCI modalities.

Watching object related movements modulates mirror-like activity in parietal brain regions.

OBJECTIVE: We studied the activation of cortical motor and parietal areas during the observation of object related grasping movements. By manipulating the type of an object (realistic versus abstract) and the type of grasping (correct versus incorrect), we addressed the question how observing such object related movements influences cortical rhythmicity, especially the mu-rhythm, in the context of an "extended" human mirror neuron system (MNS). METHODS: Multichannel electroencephalogram (EEG) was recorded during the observation of different object-related grasping actions in twenty healthy subjects. Different movies were presented, showing sequences of correct or incorrect hand grasping actions related to an abstract or realistic (daily life) object. RESULTS: Event-related de/synchronization (ERD/ERS) analyses revealed a larger ERD in the upper alpha (10-12 Hz), beta (16-20 Hz) and gamma (36-40 Hz) frequency bands over parietal brain regions depending on the type of grasping. The type of object only influenced ERD patterns in the gamma band range (36-40 Hz) at parietal sites suggesting a strong relation of gamma band activity and cortical object representation. Abstract and realistic objects produced lower beta band synchronization at central sites only, whereas depending on the type of grasping an ERS in the upper alpha band (10-12 Hz) was observed. CONCLUSION: Depending on the type of the grasped object and the type of grasping stronger parietal cortical activation occurred during movement observation. SIGNIFICANCE: Discussing the results in terms of an "extended" human mirror neuron system (MNS), it could be concluded that beside sensorimotor areas a stronger involvement of parietal brain regions was found depending on the type of object and grasping movement observed.

Time-dependent approach for single trial classification of covert visuospatial attention.

Recently, several studies have started to explore covert visuospatial attention as a control signal for brain-computer interfaces (BCIs). Covert visuospatial attention represents the ability to change the focus of attention from one point in the space without overt eye movements. Nevertheless, the full potential and possible applications of this paradigm remain relatively unexplored. Voluntary covert visuospatial attention might allow a more natural and intuitive interaction with real environments as neither stimulation nor gazing is required. In order to identify brain correlates of covert visuospatial attention, classical approaches usually rely on the whole α-band over long time intervals. In this work, we propose a more detailed analysis in the frequency and time domains to enhance classification performance. In particular, we investigate the contribution of α sub-bands and the role of time intervals in carrying information about visual attention. Previous neurophysiological studies have already highlighted the role of temporal dynamics in attention mechanisms. However, these important aspects are not yet exploited in BCI. In this work, we studied different methods that explicitly cope with the natural brain dynamics during visuospatial attention tasks in order to enhance BCI robustness and classification performances. Results with ten healthy subjects demonstrate that our approach identifies spectro-temporal patterns that outperform the state-of-the-art classification method. On average, our time-dependent classification reaches 0.74 ± 0.03 of the area under the ROC (receiver operating characteristic) curve (AUC) value with an increase of 12.3% with respect to standard methods (0.65 ± 0.4). In addition, the proposed approach allows faster classification (<1 instead of 3 s), without compromising performances. Finally, our analysis highlights the fact that discriminant patterns are not stable for the whole trial period but are changing over short time intervals. These results support the hypothesis that visual attention information is actually indexed by subject-specific α sub-bands and is time dependent.

Toward smarter BCIs: extending BCIs through hybridization and intelligent control.

This paper summarizes two novel ways to extend brain-computer interface (BCI) systems. One way involves hybrid BCIs. A hybrid BCI is a system that combines a BCI with another device to help people send information. Different types of hybrid BCIs are discussed, along with challenges and issues. BCIs are also being extended through intelligent systems. Software that allows high-level control, incorporates context and the environment and/or uses virtual reality can substantially improve BCI systems. Throughout the paper, we critically address the real benefits of these improvements relative to existing technology and practices. We also present new challenges that are likely to emerge as these novel BCI directions become more widespread.

Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges.

In recent years, new research has brought the field of electroencephalogram (EEG)-based brain-computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely, "Communication and Control", "Motor Substitution", "Entertainment", and "Motor Recovery". We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users' mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices.