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

Bring mental activity into action! An enhanced online co-adaptive brain-computer interface training protocol

Non-stationarity and inherent variability of the noninvasive electroencephalogram (EEG) makes robust recognition of spontaneous EEG patterns challenging. Reliable modulation of EEG patterns that a BCI can robustly detect is a skill that users must learn. In this paper, we present a novel online co-adaptive BCI training paradigm. The system autonomously screens users for their ability to modulate EEG patterns in a predictive way and adapts its model parameters online. Results of a supporting study in seven first-time BCI users with disability are very encouraging. Three of 7 users achieved online accuracy > 70% for 2-class BCI control after 24 minutes of training. Online performance in 6 of 7 users was significantly higher than chance level. Online control was based on one single bipolar EEG channel. Beta band activity carried most discriminant information. Our fully automatic co-adaptive online approach allows to evaluate whether user benefit from current BCI technology within a reasonable timescale.

Commanding a Brain-Controlled Wheelchair Using Steady-State Somatosensory Evoked Potentials

In this work, we propose a novel brain-controlled wheelchair, one of the major applications of brain-machine interfaces (BMIs), that allows an individual with mobility impairments to perform daily living activities independently. Specifically, we propose to use a steady-state somatosensory evoked potential (SSSEP) paradigm, which elicits brain responses to tactile stimulation of specific frequencies, for a user's intention to control a wheelchair. In our system, a user had three possible commands by concentrating on one of three vibration stimuli, which were attached to the left-hand, right-hand, and right-foot, to selectively control the wheelchair. The three stimuli were associated with three wheelchair commands: turn-left, turn-right, and move-forward. From a machine learning perspective, we also devise a novel feature representation by combining spatial and spectral characteristics of brain signals. In order to validate the effectiveness of the proposed SSSEP-based system, we considered two different tasks: 1) a simple obstacle-avoidance task within a limited time and; 2) a driving task along the predefined trajectory of about 40 m length, where there were a narrow pathway, a door, and obstacles. In both experiments, we recruited 12 subjects and compared the average time of motor imagery (MI) and SSSEP-based controls to complete the task. With the SSSEP-based control, all subjects successfully completed the task without making any collision while four subjects failed it with MI-based control. It is also noteworthy that in terms of the average time to complete the task, the SSSEP-based control outperformed the MI-based control. In the other more challenging task, all subjects successfully reached the target location.

Control of an Ambulatory Exoskeleton with a Brain-Machine Interface for Spinal Cord Injury Gait Rehabilitation

The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain-machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton-without any weight or balance support-for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and patients, respectively) would have suffered from unexpected activations (i.e., false positives) without the proposed control strategy. All the patients showed low exertion and fatigue levels during the performance of the experiments. This paper constitutes a proof-of-concept study to validate the feasibility of a BMI to control an ambulatory exoskeleton by patients with incomplete paraplegia (i.e., patients with good prognosis for gait rehabilitation).

Driving a Semiautonomous Mobile Robotic Car Controlled by an SSVEP-Based BCI

Brain-computer interfaces represent a range of acknowledged technologies that translate brain activity into computer commands. The aim of our research is to develop and evaluate a BCI control application for certain assistive technologies that can be used for remote telepresence or remote driving. The communication channel to the target device is based on the steady-state visual evoked potentials. In order to test the control application, a mobile robotic car (MRC) was introduced and a four-class BCI graphical user interface (with live video feedback and stimulation boxes on the same screen) for piloting the MRC was designed. For the purpose of evaluating a potential real-life scenario for such assistive technology, we present a study where 61 subjects steered the MRC through a predetermined route. All 61 subjects were able to control the MRC and finish the experiment (mean time 207.08 s, SD 50.25) with a mean (SD) accuracy and ITR of 93.03% (5.73) and 14.07 bits/min (4.44), respectively. The results show that our proposed SSVEP-based BCI control application is suitable for mobile robots with a shared-control approach. We also did not observe any negative influence of the simultaneous live video feedback and SSVEP stimulation on the performance of the BCI system.

Biomimetic rehabilitation engineering: the importance of somatosensory feedback for brain-machine interfaces

OBJECTIVE

Brain-machine interfaces (BMIs) re-establish communication channels between the nervous system and an external device. The use of BMI technology has generated significant developments in rehabilitative medicine, promising new ways to restore lost sensory-motor functions. However and despite high-caliber basic research, only a few prototypes have successfully left the laboratory and are currently home-deployed.

APPROACH

The failure of this laboratory-to-user transfer likely relates to the absence of BMI solutions for providing naturalistic feedback about the consequences of the BMI's actions. To overcome this limitation, nowadays cutting-edge BMI advances are guided by the principle of biomimicry; i.e. the artificial reproduction of normal neural mechanisms.

MAIN RESULTS

Here, we focus on the importance of somatosensory feedback in BMIs devoted to reproducing movements with the goal of serving as a reference framework for future research on innovative rehabilitation procedures. First, we address the correspondence between users' needs and BMI solutions. Then, we describe the main features of invasive and non-invasive BMIs, including their degree of biomimicry and respective advantages and drawbacks. Furthermore, we explore the prevalent approaches for providing quasi-natural sensory feedback in BMI settings. Finally, we cover special situations that can promote biomimicry and we present the future directions in basic research and clinical applications.

SIGNIFICANCE

The continued incorporation of biomimetic features into the design of BMIs will surely serve to further ameliorate the realism of BMIs, as well as tremendously improve their actuation, acceptance, and use.

Detecting the Intention to Move Upper Limbs from Electroencephalographic Brain Signals

Early decoding of motor states directly from the brain activity is essential to develop brain-machine interfaces (BMI) for natural motor control of neuroprosthetic devices. Hence, this study aimed to investigate the detection of movement information before the actual movement occurs. This information piece could be useful to provide early control signals to drive BMI-based rehabilitation and motor assisted devices, thus providing a natural and active rehabilitation therapy. In this work, electroencephalographic (EEG) brain signals from six healthy right-handed participants were recorded during self-initiated reaching movements of the upper limbs. The analysis of these EEG traces showed that significant event-related desynchronization is present before and during the execution of the movements, predominantly in the motor-related α and β frequency bands and in electrodes placed above the motor cortex. This oscillatory brain activity was used to continuously detect the intention to move the limbs, that is, to identify the motor phase prior to the actual execution of the reaching movement. The results showed, first, significant classification between relax and movement intention and, second, significant detection of movement intention prior to the onset of the executed movement. On the basis of these results, detection of movement intention could be used in BMI settings to reduce the gap between mental motor processes and the actual movement performed by an assisted or rehabilitation robotic device.

Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task

We propose a framework for building electrophysiological predictors of single-trial motor performance variations, exemplified for SVIPT, a sequential isometric force control task suitable for hand motor rehabilitation after stroke. Electroencephalogram (EEG) data of 20 subjects with mean age of 53 years was recorded prior to and during 400 trials of SVIPT. They were executed within a single session with the non-dominant left hand, while receiving continuous visual feedback of the produced force trajectories. The behavioral data showed strong trial-by-trial performance variations for five clinically relevant metrics, which accounted for reaction time as well as for the smoothness and precision of the produced force trajectory. 18 out of 20 tested subjects remained after preprocessing and entered offline analysis. Source Power Comodulation (SPoC) was applied on EEG data of a short time interval prior to the start of each SVIPT trial. For 11 subjects, SPoC revealed robust oscillatory EEG subspace components, whose bandpower activity are predictive for the performance of the upcoming trial. Since SPoC may overfit to non-informative subspaces, we propose to apply three selection criteria accounting for the meaningfulness of the features. Across all subjects, the obtained components were spread along the frequency spectrum and showed a variety of spatial activity patterns. Those containing the highest level of predictive information resided in and close to the alpha band. Their spatial patterns resemble topologies reported for visual attention processes as well as those of imagined or executed hand motor tasks. In summary, we identified subject-specific single predictors that explain up to 36% of the performance fluctuations and may serve for enhancing neuroergonomics of motor rehabilitation scenarios.

Event-related modulation of steady-state visual evoked potentials for eyes-closed brain computer interface

Brain computer interfaces (BCIs), also be referred to be as brain machine interfaces, transform modulations of electroencephalogram (EEG) into user's intents to communicate with others without voice and physical movement. BCIs have been studied and developed as one of the important means for communication-aid between disabled with severe motor disabilities such as amyotrophic lateral sclerosis and muscular dystrophy patients and their caregivers. State-of-art BCIs have achieved the outstanding performance in information transfer rate and classification accuracy. However, most of conventional BCIs are still unavailable for patients with impaired oculomotor control due to requirement of visual modality. The present study aimed at developing a novel 2-class BCI which was independent of oculomotor control including eye-opening using event-related modulation of steady state visual evoked potential (SSVEP) associated with mental tasks under eyes-closed condition. Eleven healthy subjects aged 21-24 years old were recruited and directed to perform each of two mental tasks under an eyes-closed condition; mental focus on flicker stimuli and image recall of their favorite animals, respectively. The magnitudes of SSVEP in the posterior regions of almost all the subjects were seen to be modulated by performing the mental tasks under the conditions of the flickering frequency of 10 Hz and stimulus intensity of 3-5 lx, which was used to express a user's binary intent, namely, performing one of the mental tasks or not (rest). The classification performance on the mental focus, 80 %, was larger than that on the image recall, 75 %, in average across all the subjects. Shortening of the data length used for classification would improve the information transfer rate of the proposed BCI.

Asynchronous detection of kinesthetic attention during mobilization of lower limbs using EEG measurements

OBJECTIVE

Attention is known to modulate the plasticity of the motor cortex, and plasticity is crucial for recovery in motor rehabilitation. This study addresses the possibility of using an EEG-based brain-computer interface (BCI) to detect kinesthetic attention to movement.

APPROACH

A novel experiment emulating physical rehabilitation was designed to study kinesthetic attention. The protocol involved continuous mobilization of lower limbs during which participants reported levels of attention to movement-from focused kinesthetic attention to mind wandering. For this protocol an asynchronous BCI detector of kinesthetic attention and deliberate mind wandering was designed.

MAIN RESULTS

EEG analysis showed significant differences in theta, alpha, and beta bands, related to the attentional state. These changes were further pinpointed to bands relative to the frequency of the individual alpha peak. The accuracy of the designed BCI ranged between 60.8% and 68.4% (significantly above chance level), depending on the used analysis window length, i.e. acceptable detection delay.

SIGNIFICANCE

This study shows it is possible to use self-reporting to study attention-related changes in EEG during continuous mobilization. Such a protocol is used to develop an asynchronous BCI detector of kinesthetic attention, with potential applications to motor rehabilitation.

The Evolution of Neuroprosthetic Interfaces

The ideal neuroprosthetic interface permits high-quality neural recording and stimulation of the nervous system while reliably providing clinical benefits over chronic periods. Although current technologies have made notable strides in this direction, significant improvements must be made to better achieve these design goals and satisfy clinical needs. This article provides an overview of the state of neuroprosthetic interfaces, starting with the design and placement of these interfaces before exploring the stimulation and recording platforms yielded from contemporary research. Finally, we outline emerging research trends in an effort to explore the potential next generation of neuroprosthetic interfaces.

Autonomous Parameter Adjustment for SSVEP-Based BCIs with a Novel BCI Wizard

Brain-Computer Interfaces (BCIs) transfer human brain activities into computer commands and enable a communication channel without requiring movement. Among other BCI approaches, steady-state visual evoked potential (SSVEP)-based BCIs have the potential to become accurate, assistive technologies for persons with severe disabilities. Those systems require customization of different kinds of parameters (e.g., stimulation frequencies). Calibration usually requires selecting predefined parameters by experienced/trained personnel, though in real-life scenarios an interface allowing people with no experience in programming to set up the BCI would be desirable. Another occurring problem regarding BCI performance is BCI illiteracy (also called BCI deficiency). Many articles reported that BCI control could not be achieved by a non-negligible number of users. In order to bypass those problems we developed a SSVEP-BCI wizard, a system that automatically determines user-dependent key-parameters to customize SSVEP-based BCI systems. This wizard was tested and evaluated with 61 healthy subjects. All subjects were asked to spell the phrase "RHINE WAAL UNIVERSITY" with a spelling application after key parameters were determined by the wizard. Results show that all subjects were able to control the spelling application. A mean (SD) accuracy of 97.14 (3.73)% was reached (all subjects reached an accuracy above 85% and 25 subjects even reached 100% accuracy).

Investigation of the effect of EEG-BCI on the simultaneous execution of flight simulation and attentional tasks

Brain-computer interfaces (BCIs) are widely used for clinical applications and exploited to design robotic and interactive systems for healthy people. We provide evidence to control a sensorimotor electroencephalographic (EEG) BCI system while piloting a flight simulator and attending a double attentional task simultaneously. Ten healthy subjects were trained to learn how to manage a flight simulator, use the BCI system, and answer to the attentional tasks independently. Afterward, the EEG activity was collected during a first flight where subjects were required to concurrently use the BCI, and a second flight where they were required to simultaneously use the BCI and answer to the attentional tasks. Results showed that the concurrent use of the BCI system during the flight simulation does not affect the flight performances. However, BCI performances decrease from the 83 to 63 % while attending additional alertness and vigilance tasks. This work shows that it is possible to successfully control a BCI system during the execution of multiple tasks such as piloting a flight simulator with an extra cognitive load induced by attentional tasks. Such framework aims to foster the knowledge on BCI systems embedded into vehicles and robotic devices to allow the simultaneous execution of secondary tasks.

Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy-EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants' BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants' performance with a mean error of less than 3 points. This study determined how users' profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user.

Multiresolution analysis over graphs for a motor imagery based online BCI game

Multiresolution analysis (MRA) over graph representation of EEG data has proved to be a promising method for offline brain-computer interfacing (BCI) data analysis. For the first time we aim to prove the feasibility of the graph lifting transform in an online BCI system. Instead of developing a pointer device or a wheel-chair controller as test bed for human-machine interaction, we have designed and developed an engaging game which can be controlled by means of imaginary limb movements. Some modifications to the existing MRA analysis over graphs for BCI have also been proposed, such as the use of common spatial patterns for feature extraction at the different levels of decomposition, and sequential floating forward search as a best basis selection technique. In the online game experiment we obtained for three classes an average classification rate of 63.0% for fourteen naive subjects. The application of a best basis selection method helps significantly decrease the computing resources needed. The present study allows us to further understand and assess the benefits of the use of tailored wavelet analysis for processing motor imagery data and contributes to the further development of BCI for gaming purposes.

A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem

Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user’s intent for specific keyboard strikes or mouse button presses. The BCI’s data analytics of a subject’s MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse.

Single-Trial Detection With Magnetoencephalography During a Dual-Rapid Serial Visual Presentation Task

GOAL

The detection of brain responses corresponding to the presentation of a particular class of images is a challenge in brain-machine interface. Current systems based on the detection of brain responses during rapid serial visual presentation (RSVP) tasks possess advantages for both healthy and disabled people, as they are gaze independent and can offer a high throughput.

METHODS

We propose a novel paradigm based on a dual-RSVP task that assumes a low target probability. Two streams of images are presented simultaneously on the screen, the second stream is identical to the first one, but delayed in time. Participants were asked to detect images containing a person. They follow the first stream until they see a target image, then change their attention to the second stream until the target image reappears, finally they change their attention back to the first stream.

RESULTS

The performance of single-trial detection was evaluated on both streams and their combination of the decisions with signal recorded with magnetoencephalography (MEG) during the dual-RSVP task. We compare classification performance across different sets of channels (magnetometers, gradiometers) with a BLDA classifier with inputs obtained after spatial filtering.

CONCLUSION

The results suggest that single-trial detection can be obtained with an area under the ROC curve superior to 0.95, and that an almost perfect accuracy can be obtained with some subjects thanks to the combination of the decisions from two trials, without doubling the duration of the experiment.

SIGNIFICANCE

The present results show that a reliable accuracy can be obtained with the MEG for target detection during a dual-RSVP task.

Comparison of eye tracking, electrooculography and an auditory brain-computer interface for binary communication: a case study with a participant in the locked-in state

BACKGROUND

In this study, we evaluated electrooculography (EOG), an eye tracker and an auditory brain-computer interface (BCI) as access methods to augmentative and alternative communication (AAC). The participant of the study has been in the locked-in state (LIS) for 6 years due to amyotrophic lateral sclerosis. He was able to communicate with slow residual eye movements, but had no means of partner independent communication. We discuss the usability of all tested access methods and the prospects of using BCIs as an assistive technology.

METHODS

Within four days, we tested whether EOG, eye tracking and a BCI would allow the participant in LIS to make simple selections. We optimized the parameters in an iterative procedure for all systems.

RESULTS

The participant was able to gain control over all three systems. Nonetheless, due to the level of proficiency previously achieved with his low-tech AAC method, he did not consider using any of the tested systems as an additional communication channel. However, he would consider using the BCI once control over his eye muscles would no longer be possible. He rated the ease of use of the BCI as the highest among the tested systems, because no precise eye movements were required; but also as the most tiring, due to the high level of attention needed to operate the BCI.

CONCLUSIONS

In this case study, the partner based communication was possible due to the good care provided and the proficiency achieved by the interlocutors. To ease the transition from a low-tech AAC method to a BCI once control over all muscles is lost, it must be simple to operate. For persons, who rely on AAC and are affected by a progressive neuromuscular disease, we argue that a complementary approach, combining BCIs and standard assistive technology, can prove valuable to achieve partner independent communication and ease the transition to a purely BCI based approach. Finally, we provide further evidence for the importance of a user-centered approach in the design of new assistive devices.

Shared-control brain-computer interface for a two dimensional reaching task using EEG error-related potentials

One of the main problems of EEG-based brain computer interfaces (BCIs) is their low information rate, thus for complex tasks the user needs large amounts of time to solve the task. In an attempt to reduce this time and improve the application robustness, recent works have explored shared-control strategies where the device does not only execute the decoded commands, but it is also involved in executing the task. This work proposes a shared-control BCI using error potentials for a 2D reaching task with discrete actions and states. The proposed system has several interesting properties: the system is scalable without increasing the complexity of the user's mental task; the interaction is natural for the user, as the mental task is to monitor the device performance to promote its task learning (in this context the reaching task); and the system has the potential to be combined with additional brain signals to recover or learn from interaction errors. Online control experiments were performed with four subjects, showing that it was possible to reach a goal location from any starting point within a 5×5 grid in around 23 actions (about 19 seconds of EEG signal), both with fixed goals and goals freely chosen by the users.

Brain-machine interfaces for assistive smart homes: A feasibility study with wearable near-infrared spectroscopy

Smart houses for elderly or physically challenged people need a method to understand residents' intentions during their daily-living behaviors. To explore a new possibility, we here developed a novel brain-machine interface (BMI) system integrated with an experimental smart house, based on a prototype of a wearable near-infrared spectroscopy (NIRS) device, and verified the system in a specific task of controlling of the house's equipments with BMI. We recorded NIRS signals of three participants during typical daily-living actions (DLAs), and classified them by linear support vector machine. In our off-line analysis, four DLAs were classified at about 70% mean accuracy, significantly above the chance level of 25%, in every participant. In an online demonstration in the real smart house, one participant successfully controlled three target appliances by BMI at 81.3% accuracy. Thus we successfully demonstrated the feasibility of using NIRS-BMI in real smart houses, which will possibly enhance new assistive smart-home technologies.

Goal-directed or aimless? EEG differences during the preparation of a reach-and-touch task

The natural control of neuroprostheses is currently a challenge in both rehabilitation engineering and brain-computer interfaces (BCIs) research. One of the recurrent problems is to know exactly when to activate such devices. For the execution of the most common activities of daily living, these devices only need to be active when in the presence of a goal. Therefore, we believe that the distinction between the planning of goal-directed and aimless movements, using non-invasive recordings, can be useful for the implementation of a simple and effective activation method for these devices. We investigated whether those differences are detectable during a reach-and-touch task, using electroencephalography (EEG). Event-related potentials and oscillatory activity changes were studied. Our results show that there are statistically significant differences between both types of movement. Combining this information with movement decoding would allow a natural control strategy for BCIs, exclusively relying on the cognitive processes behind movement preparation and execution.

Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials

This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials. The proposed method exploits task constraints to simultaneously calibrate the decoder and control the device, by using a robust likelihood function and an ad-hoc planner to cope with the large uncertainty resulting from the unknown task and decoder. The method has been evaluated in closed-loop online experiments with 8 users using a previously proposed BCI protocol for reaching tasks over a grid. The results show that it is possible to have a usable BCI control from the beginning of the experiment without any prior calibration. Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach.

Offline decoding of upper limb muscle synergies from EEG slow cortical potentials

Muscle synergies are thought to be the building blocks used by the central nervous system to control the underdetermined problem of muscles activation. Decoding these synergies from EEG could provide useful tools for BCI-controlled orthotic devices. In this paper, we assess the possibility of decoding muscle synergies from EEG slow cortical potentials in two healthy subjects and two stroke patients performing a center-out reaching task. We were able to successfully decode the extracted muscle synergies in both healthy subject and one patient.

Visual Feedback Dominates the Sense of Agency for Brain-Machine Actions

Recent advances in neuroscience and engineering have led to the development of technologies that permit the control of external devices through real-time decoding of brain activity (brain-machine interfaces; BMI). Though the feeling of controlling bodily movements (sense of agency; SOA) has been well studied and a number of well-defined sensorimotor and cognitive mechanisms have been put forth, very little is known about the SOA for BMI-actions. Using an on-line BMI, and verifying that our subjects achieved a reasonable level of control, we sought to describe the SOA for BMI-mediated actions. Our results demonstrate that discrepancies between decoded neural activity and its resultant real-time sensory feedback are associated with a decrease in the SOA, similar to SOA mechanisms proposed for bodily actions. However, if the feedback discrepancy serves to correct a poorly controlled BMI-action, then the SOA can be high and can increase with increasing discrepancy, demonstrating the dominance of visual feedback on the SOA. Taken together, our results suggest that bodily and BMI-actions rely on common mechanisms of sensorimotor integration for agency judgments, but that visual feedback dominates the SOA in the absence of overt bodily movements or proprioceptive feedback, however erroneous the visual feedback may be.

Effects of user mental state on EEG-BCI performance

Changes in psychological state have been proposed as a cause of variation in brain-computer interface performance, but little formal analysis has been conducted to support this hypothesis. In this study, we investigated the effects of three mental states—fatigue, frustration, and attention—on BCI performance. Twelve able-bodied participants were trained to use a two-class EEG-BCI based on the performance of user-specific mental tasks. Following training, participants completed three testing sessions, during which they used the BCI to play a simple maze navigation game while periodically reporting their perceived levels of fatigue, frustration, and attention. Statistical analysis indicated that there is a significant relationship between frustration and BCI performance while the relationship between fatigue and BCI performance approached significance. BCI performance was 7% lower than average when self-reported fatigue was low and 7% higher than average when self-reported frustration was moderate. A multivariate analysis of mental state revealed the presence of contiguous regions in mental state space where BCI performance was more accurate than average, suggesting the importance of moderate fatigue for achieving effortless focus on BCI control, frustration as a potential motivating factor, and attention as a compensatory mechanism to increasing frustration. Finally, a visual analysis showed the sensitivity of underlying class distributions to changes in mental state. Collectively, these results indicate that mental state is closely related to BCI performance, encouraging future development of psychologically adaptive BCIs.

Assessing movement factors in upper limb kinematics decoding from EEG signals

The past decades have seen the rapid development of upper limb kinematics decoding techniques by performing intracortical recordings of brain signals. However, the use of non-invasive approaches to perform similar decoding procedures is still in its early stages. Recent studies show that there is a correlation between electroencephalographic (EEG) signals and hand-reaching kinematic parameters. From these studies, it could be concluded that the accuracy of upper limb kinematics decoding depends, at least partially, on the characteristics of the performed movement. In this paper, we have studied upper limb movements with different speeds and trajectories in a controlled environment to analyze the influence of movement variability in the decoding performance. To that end, low frequency components of the EEG signals have been decoded with linear models to obtain the position of the volunteer’s hand during performed trajectories grasping the end effector of a planar manipulandum. The results confirm that it is possible to obtain kinematic information from low frequency EEG signals and show that decoding performance is significantly influenced by movement variability and tracking accuracy as continuous and slower movements improve the accuracy of the decoder. This is a key factor that should be taken into account in future experimental designs.

Individually adapted imagery improves brain-computer interface performance in end-users with disability

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.

Brain-computer interface users speak up: the Virtual Users' Forum at the 2013 International Brain-Computer Interface Meeting

More than 300 researchers gathered at the 2013 International Brain-Computer Interface (BCI) Meeting to discuss current practice and future goals for BCI research and development. The authors organized the Virtual Users' Forum at the meeting to provide the BCI community with feedback from users. We report on the Virtual Users' Forum, including initial results from ongoing research being conducted by 2 BCI groups. Online surveys and in-person interviews were used to solicit feedback from people with disabilities who are expert and novice BCI users. For the Virtual Users' Forum, their responses were organized into 4 major themes: current (non-BCI) communication methods, experiences with BCI research, challenges of current BCIs, and future BCI developments. Two authors with severe disabilities gave presentations during the Virtual Users' Forum, and their comments are integrated with the other results. While participants' hopes for BCIs of the future remain high, their comments about available systems mirror those made by consumers about conventional assistive technology. They reflect concerns about reliability (eg, typing accuracy/speed), utility (eg, applications and the desire for real-time interactions), ease of use (eg, portability and system setup), and support (eg, technical support and caregiver training). People with disabilities, as target users of BCI systems, can provide valuable feedback and input on the development of BCI as an assistive technology. To this end, participatory action research should be considered as a valuable methodology for future BCI research.

Electrode replacement does not affect classification accuracy in dual-session use of a passive brain-computer interface for assessing cognitive workload

The passive brain-computer interface (pBCI) framework has been shown to be a very promising construct for assessing cognitive and affective state in both individuals and teams. There is a growing body of work that focuses on solving the challenges of transitioning pBCI systems from the research laboratory environment to practical, everyday use. An interesting issue is what impact methodological variability may have on the ability to reliably identify (neuro)physiological patterns that are useful for state assessment. This work aimed at quantifying the effects of methodological variability in a pBCI design for detecting changes in cognitive workload. Specific focus was directed toward the effects of replacing electrodes over dual sessions (thus inducing changes in placement, electromechanical properties, and/or impedance between the electrode and skin surface) on the accuracy of several machine learning approaches in a binary classification problem. In investigating these methodological variables, it was determined that the removal and replacement of the electrode suite between sessions does not impact the accuracy of a number of learning approaches when trained on one session and tested on a second. This finding was confirmed by comparing to a control group for which the electrode suite was not replaced between sessions. This result suggests that sensors (both neurological and peripheral) may be removed and replaced over the course of many interactions with a pBCI system without affecting its performance. Future work on multi-session and multi-day pBCI system use should seek to replicate this (lack of) effect between sessions in other tasks, temporal time courses, and data analytic approaches while also focusing on non-stationarity and variable classification performance due to intrinsic factors.

Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives

Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract specific features of brain activity and translate them into control signals that drive an output. Recently, a category of BCIs that are built on the rhythmic activity recorded over the sensorimotor cortex, i.e., the sensorimotor rhythm (SMR), has attracted considerable attention among the BCIs that use noninvasive neural recordings, e.g., electroencephalography (EEG), and have demonstrated the capability of multidimensional prosthesis control. This paper reviews the current state and future perspectives of SMR-based BCI and its clinical applications, in particular focusing on the EEG SMR. The characteristic features of SMR from the human brain are described and their underlying neural sources are discussed. The functional components of SMR-based BCI, together with its current clinical applications, are reviewed. Finally, limitations of SMR-BCIs and future outlooks are also discussed.

Control strategies for active lower extremity prosthetics and orthotics: a review

: Technological advancements have led to the development of numerous wearable robotic devices for the physical assistance and restoration of human locomotion. While many challenges remain with respect to the mechanical design of such devices, it is at least equally challenging and important to develop strategies to control them in concert with the intentions of the user.This work reviews the state-of-the-art techniques for controlling portable active lower limb prosthetic and orthotic (P/O) devices in the context of locomotive activities of daily living (ADL), and considers how these can be interfaced with the user's sensory-motor control system. This review underscores the practical challenges and opportunities associated with P/O control, which can be used to accelerate future developments in this field. Furthermore, this work provides a classification scheme for the comparison of the various control strategies.As a novel contribution, a general framework for the control of portable gait-assistance devices is proposed. This framework accounts for the physical and informatic interactions between the controller, the user, the environment, and the mechanical device itself. Such a treatment of P/Os--not as independent devices, but as actors within an ecosystem--is suggested to be necessary to structure the next generation of intelligent and multifunctional controllers.Each element of the proposed framework is discussed with respect to the role that it plays in the assistance of locomotion, along with how its states can be sensed as inputs to the controller. The reviewed controllers are shown to fit within different levels of a hierarchical scheme, which loosely resembles the structure and functionality of the nominal human central nervous system (CNS). Active and passive safety mechanisms are considered to be central aspects underlying all of P/O design and control, and are shown to be critical for regulatory approval of such devices for real-world use.The works discussed herein provide evidence that, while we are getting ever closer, significant challenges still exist for the development of controllers for portable powered P/O devices that can seamlessly integrate with the user's neuromusculoskeletal system and are practical for use in locomotive ADL.

Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG)

BACKGROUND

Brain-machine interfaces (BMIs) allow direct translation of electric, magnetic or metabolic brain signals into control commands of external devices such as robots, prostheses or exoskeletons. However, non-stationarity of brain signals and susceptibility to biological or environmental artifacts impede reliable control and safety of BMIs, particularly in daily life environments. Here we introduce and tested a novel hybrid brain-neural computer interaction (BNCI) system fusing electroencephalography (EEG) and electrooculography (EOG) to enhance reliability and safety of continuous hand exoskeleton-driven grasping motions.

FINDINGS

12 healthy volunteers (8 male, mean age 28.1 ± 3.63y) used EEG (condition #1) and hybrid EEG/EOG (condition #2) signals to control a hand exoskeleton. Motor imagery-related brain activity was translated into exoskeleton-driven hand closing motions. Unintended motions could be interrupted by eye movement-related EOG signals. In order to evaluate BNCI control and safety, participants were instructed to follow a visual cue indicating either to move or not to move the hand exoskeleton in a random order. Movements exceeding 25% of a full grasping motion when the device was not supposed to be moved were defined as safety violation. While participants reached comparable control under both conditions, safety was frequently violated under condition #1 (EEG), but not under condition #2 (EEG/EOG).

CONCLUSION

EEG/EOG biosignal fusion can substantially enhance safety of assistive BNCI systems improving their applicability in daily life environments.

Novel hold-release functionality in a P300 brain-computer interface

Assistive technology control interface theory describes interface activation and interface deactivation as distinct properties of any control interface. Separating control of activation and deactivation allows precise timing of the duration of the activation. Objective. We propose a novel P300 brain-computer interface (BCI) functionality with separate control of the initial activation and the deactivation (hold-release) of a selection. Approach. Using two different layouts and off-line analysis, we tested the accuracy with which subjects could (1) hold their selection and (2) quickly change between selections. Main results. Mean accuracy across all subjects for the hold-release algorithm was 85% with one hold-release classification and 100% with two hold-release classifications. Using a layout designed to lower perceptual errors, accuracy increased to a mean of 90% and the time subjects could hold a selection was 40% longer than with the standard layout. Hold-release functionality provides improved response time (6-16 times faster) over the initial P300 BCI selection by allowing the BCI to make hold-release decisions from very few flashes instead of after multiple sequences of flashes. Significance. For the BCI user, hold-release functionality allows for faster, more continuous control with a P300 BCI, creating new options for BCI applications.

Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia

Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.

Control of Leg Movements Driven by EMG Activity of Shoulder Muscles

During human walking, there exists a functional neural coupling between arms and legs, and between cervical and lumbosacral pattern generators. Here, we present a novel approach for associating the electromyographic (EMG) activity from upper limb muscles with leg kinematics. Our methodology takes advantage of the high involvement of shoulder muscles in most locomotor-related movements and of the natural co-ordination between arms and legs. Nine healthy subjects were asked to walk at different constant and variable speeds (3–5 km/h), while EMG activity of shoulder (deltoid) muscles and the kinematics of walking were recorded. To ensure a high level of EMG activity in deltoid, the subjects performed slightly larger arm swinging than they usually do. The temporal structure of the burst-like EMG activity was used to predict the spatiotemporal kinematic pattern of the forthcoming step. A comparison of actual and predicted stride leg kinematics showed a high degree of correspondence (r > 0.9). This algorithm has been also implemented in pilot experiments for controlling avatar walking in a virtual reality setup and an exoskeleton during over-ground stepping. The proposed approach may have important implications for the design of human–machine interfaces and neuroprosthetic technologies such as those of assistive lower limb exoskeletons.

Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment

Individuals with severe motor impairment can use event-related desynchronization (ERD) based BCIs as assistive technology. Auto-calibrating and adaptive ERD-based BCIs that users control with motor imagery tasks (“SMR-AdBCI”) have proven effective for healthy users. We aim to find an improved configuration of such an adaptive ERD-based BCI for individuals with severe motor impairment as a result of spinal cord injury (SCI) or stroke. We hypothesized that an adaptive ERD-based BCI, that automatically selects a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (“Auto-AdBCI”) could allow for higher control performance than a conventional SMR-AdBCI. To answer this question we performed offline analyses on two sessions (21 data sets total) of cue-guided, five-class electroencephalography (EEG) data recorded from individuals with SCI or stroke. On data from the twelve individuals in Session 1, we first identified three bipolar derivations for the SMR-AdBCI. In a similar way, we determined three bipolar derivations and four mental tasks for the Auto-AdBCI. We then simulated both, the SMR-AdBCI and the Auto-AdBCI configuration on the unseen data from the nine participants in Session 2 and compared the results. On the unseen data of Session 2 from individuals with SCI or stroke, we found that automatically selecting a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (Auto-AdBCI) significantly (p < 0.01) improved classification performance compared to an adaptive ERD-based BCI that only used motor imagery tasks (SMR-AdBCI; average accuracy of 75.7 vs. 66.3%).

Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose

The paper proposes a novel approach toward EEG-driven position control of a robot arm by utilizing motor imagery, P300 and error-related potentials (ErRP) to align the robot arm with desired target position. In the proposed scheme, the users generate motor imagery signals to control the motion of the robot arm. The P300 waveforms are detected when the user intends to stop the motion of the robot on reaching the goal position. The error potentials are employed as feedback response by the user. On detection of error the control system performs the necessary corrections on the robot arm. Here, an AdaBoost-Support Vector Machine (SVM) classifier is used to decode the 4-class motor imagery and an SVM is used to decode the presence of P300 and ErRP waveforms. The average steady-state error, peak overshoot and settling time obtained for our proposed approach is 0.045, 2.8% and 44 s, respectively, and the average rate of reaching the target is 95%. The results obtained for the proposed control scheme make it suitable for designs of prosthetics in rehabilitative applications.

Correction: cecotti, h. And rivet, B. Subject combination and electrode selection in cooperative brain-computer interface based on event related potentials. Brain sci. 2014, 4, 335-355

The authors wish to make the following correction to this paper (Cecotti, H.; Rivet, B. Subject Combination and Electrode Selection in Cooperative Brain-Computer Interface Based on Event Related Potentials. Brain Sci. 2014, 4, 335-355). Dut to an error the reference number in the original published paper were not shown. The former main text should be replaced as below.

Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution

Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.

Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR)

Modulation of sensorimotor rhythms (SMR) was suggested as a control signal for brain-computer interfaces (BCI). Yet, there is a population of users estimated between 10 to 50% not able to achieve reliable control and only about 20% of users achieve high (80–100%) performance. Predicting performance prior to BCI use would facilitate selection of the most feasible system for an individual, thus constitute a practical benefit for the user, and increase our knowledge about the correlates of BCI control. In a recent study, we predicted SMR-BCI performance from psychological variables that were assessed prior to the BCI sessions and BCI control was supported with machine-learning techniques. We described two significant psychological predictors, namely the visuo-motor coordination ability and the ability to concentrate on the task. The purpose of the current study was to replicate these results thereby validating these predictors within a neurofeedback based SMR-BCI that involved no machine learning.Thirty-three healthy BCI novices participated in a calibration session and three further neurofeedback training sessions. Two variables were related with mean SMR-BCI performance: (1) a measure for the accuracy of fine motor skills, i.e., a trade for a person’s visuo-motor control ability; and (2) subject’s “attentional impulsivity”. In a linear regression they accounted for almost 20% in variance of SMR-BCI performance, but predictor (1) failed significance. Nevertheless, on the basis of our prior regression model for sensorimotor control ability we could predict current SMR-BCI performance with an average prediction error of M = 12.07%. In more than 50% of the participants, the prediction error was smaller than 10%. Hence, psychological variables played a moderate role in predicting SMR-BCI performance in a neurofeedback approach that involved no machine learning. Future studies are needed to further consolidate (or reject) the present predictors.

Evaluation of healthy EEG responses for spelling through listener-assisted scanning

We investigated whether listener-assisted scanning, an alternative communication method for persons with severe motor and visual impairments but preserved cognitive skills, could be used for spelling with EEG. To that end spoken letters were presented sequentially, and the participants made selections by performing motor execution/imagery or a cognitive task. The motor task was a brisk dorsiflexion of both feet, and the cognitive task was related to working memory and perception of human voice. The motor imagery task yielded the most promising results with respect to letter selection accuracy, albeit with a large variation in individual performance. The cognitive task yielded significant ( p = 0.05) albeit moderate results. Closer inspection of grand average ERPs for the cognitive task revealed task-related modulation of a late negative component, which is novel in the auditory BCI literature. Guidelines for further development are presented.

Exploration of the neural correlates of cerebral palsy for sensorimotor BCI control

Cerebral palsy (CP) includes a broad range of disorders, which can result in impairment of posture and movement control. Brain-computer interfaces (BCIs) have been proposed as assistive devices for individuals with CP. Better understanding of the neural processing underlying motor control in affected individuals could lead to more targeted BCI rehabilitation and treatment options. We have explored well-known neural correlates of movement, including event-related desynchronization (ERD), phase synchrony, and a recently-introduced measure of phase dynamics, in participants with CP and healthy control participants. Although present, significantly less ERD and phase locking were found in the group with CP. Additionally, inter-group differences in phase dynamics were also significant. Taken together these findings suggest that users with CP exhibit lower levels of motor cortex activation during motor imagery, as reflected in lower levels of ongoing mu suppression and less functional connectivity. These differences indicate that development of BCIs for individuals with CP may pose additional challenges beyond those faced in providing BCIs to healthy individuals.

Audio-visual feedback improves the BCI performance in the navigational control of a humanoid robot

Advancement in brain computer interfaces (BCI) technology allows people to actively interact in the world through surrogates. Controlling real humanoid robots using BCI as intuitively as we control our body represents a challenge for current research in robotics and neuroscience. In order to successfully interact with the environment the brain integrates multiple sensory cues to form a coherent representation of the world. Cognitive neuroscience studies demonstrate that multisensory integration may imply a gain with respect to a single modality and ultimately improve the overall sensorimotor performance. For example, reactivity to simultaneous visual and auditory stimuli may be higher than to the sum of the same stimuli delivered in isolation or in temporal sequence. Yet, knowledge about whether audio-visual integration may improve the control of a surrogate is meager. To explore this issue, we provided human footstep sounds as audio feedback to BCI users while controlling a humanoid robot. Participants were asked to steer their robot surrogate and perform a pick-and-place task through BCI-SSVEPs. We found that audio-visual synchrony between footsteps sound and actual humanoid's walk reduces the time required for steering the robot. Thus, auditory feedback congruent with the humanoid actions may improve motor decisions of the BCI's user and help in the feeling of control over it. Our results shed light on the possibility to increase robot's control through the combination of multisensory feedback to a BCI user.

Applications of Brain-Machine Interface Systems in Stroke Recovery and Rehabilitation

Stroke is a leading cause of disability, significantly impacting the quality of life (QOL) in survivors, and rehabilitation remains the mainstay of treatment in these patients. Recent engineering and technological advances such as brain-machine interfaces (BMI) and robotic rehabilitative devices are promising to enhance stroke neu-rorehabilitation, to accelerate functional recovery and improve QOL. This review discusses the recent applications of BMI and robotic-assisted rehabilitation in stroke patients. We present the framework for integrated BMI and robotic-assisted therapies, and discuss their potential therapeutic, assistive and diagnostic functions in stroke rehabilitation. Finally, we conclude with an outlook on the potential challenges and future directions of these neurotechnologies, and their impact on clinical rehabilitation.

Fusion of electroencephalographic dynamics and musical contents for estimating emotional responses in music listening

Electroencephalography (EEG)-based emotion classification during music listening has gained increasing attention nowadays due to its promise of potential applications such as musical affective brain-computer interface (ABCI), neuromarketing, music therapy, and implicit multimedia tagging and triggering. However, music is an ecologically valid and complex stimulus that conveys certain emotions to listeners through compositions of musical elements. Using solely EEG signals to distinguish emotions remained challenging. This study aimed to assess the applicability of a multimodal approach by leveraging the EEG dynamics and acoustic characteristics of musical contents for the classification of emotional valence and arousal. To this end, this study adopted machine-learning methods to systematically elucidate the roles of the EEG and music modalities in the emotion modeling. The empirical results suggested that when whole-head EEG signals were available, the inclusion of musical contents did not improve the classification performance. The obtained performance of 74~76% using solely EEG modality was statistically comparable to that using the multimodality approach. However, if EEG dynamics were only available from a small set of electrodes (likely the case in real-life applications), the music modality would play a complementary role and augment the EEG results from around 61-67% in valence classification and from around 58-67% in arousal classification. The musical timber appeared to replace less-discriminative EEG features and led to improvements in both valence and arousal classification, whereas musical loudness was contributed specifically to the arousal classification. The present study not only provided principles for constructing an EEG-based multimodal approach, but also revealed the fundamental insights into the interplay of the brain activity and musical contents in emotion modeling.

Subject combination and electrode selection in cooperative brain-computer interface based on event related potentials

New paradigms are required in Brain-Computer Interface (BCI) systems for the needs and expectations of healthy people. To solve this issue, we explore the emerging field of cooperative BCIs, which involves several users in a single BCI system. Contrary to classical BCIs that are dependent on the unique subject's will, cooperative BCIs are used for problem solving tasks where several people shall be engaged by sharing a common goal. Similarly as combining trials over time improves performance, combining trials across subjects can significantly improve performance compared with when only a single user is involved. Yet, cooperative BCIs may only be used in particular settings, and new paradigms must be proposed to efficiently use this approach. The possible benefits of using several subjects are addressed, and compared with current single-subject BCI paradigms. To show the advantages of a cooperative BCI, we evaluate the performance of combining decisions across subjects with data from an event-related potentials (ERP) based experiment where each subject observed the same sequence of visual stimuli. Furthermore, we show that it is possible to achieve a mean AUC superior to 0.95 with 10 subjects and 3 electrodes on each subject, or with 4 subjects and 6 electrodes on each subject. Several emerging challenges and possible applications are proposed to highlight how cooperative BCIs could be efficiently used with current technologies and leverage BCI applications.