Future Brain-computer Interface Research Articles

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.

BNCI Horizon 2020: towards a roadmap for the BCI community

The brain-computer interface (BCI) field has grown dramatically over the past few years, but there are still no coordinated efforts to ensure efficient communication and collaboration among key stakeholders. The European Commission (EC) has recently renewed their efforts to establish such a coordination effort by funding a coordination and support action for the BCI community called ‘BNCI Horizon 2020’ after the ‘Future BNCI’ project. Major goals of this new project include developing a roadmap for the next decade and beyond, encouraging discussion and collaboration within the BCI community, fostering communication with the general public, and the foundation of an international BCI Society. We present a short overview of current and past EU-funded BCI projects and provide evidence of a growing research and industrial community. Efficient communication also entails the establishment of clear terminology, which is a major goal of BNCI Horizon 2020. To this end, we give a brief overview of current BCI-related terms and definitions. A major networking activity in the project was the BNCI Horizon 2020 Retreat in Hallstatt, Austria. Over 60 experts participated in this event to discuss the future of the BCI field in a series of plenary talks, targeted discussions, and parallel focus sessions. A follow-up event was the EU BCI Day at the 6th International Brain-Computer Interface Conference in Graz, Austria. This networking event included plenary talks by eight companies and representatives from all seven ongoing EU research projects, poster presentations, demos, and discussions. Another goal of BNCI Horizon 2020 is the foundation of an official BCI Society. In this article, we summarize the current status of this process. Finally, we present visions for future BCI applications developed within BNCI Horizon 2020 using input from external BCI experts as well. We identify common themes and conclude with six exemplary use cases.

Development of electroencephalographic pattern classifiers for real and imaginary thumb and index finger movements of one hand.

This study aimed to find effective approaches to electroencephalographic (EEG) signal analysis and resolve problems of real and imaginary finger movement pattern recognition and categorization for one hand. Eight right-handed subjects (mean age 32.8 [SD=3.3] years) participated in the study, and activity from sensorimotor zones (central and contralateral to the movements/imagery) was recorded for EEG data analysis. In our study, we explored the decoding accuracy of EEG signals using real and imagined finger (thumb/index of one hand) movements using artificial neural network (ANN) and support vector machine (SVM) algorithms for future brain-computer interface (BCI) applications. The decoding accuracy of the SVM based on a Gaussian radial basis function linearly increased with each trial accumulation (mean: 45%, max: 62% with 20 trial summarizations), and the decoding accuracy of the ANN was higher when single-trial discrimination was applied (mean: 38%, max: 42%). The chosen approaches of EEG signal discrimination demonstrated differential sensitivity to data accumulation. Additionally, the time responses varied across subjects and inside sessions but did not influence the discrimination accuracy of the algorithms. This work supports the feasibility of the approach, which is presumed suitable for one-hand finger movement (real and imaginary) decoding. These results could be applied in the elaboration of multiclass BCI systems.

Electrocorticogram encoding of upper extremity movement duration.

Electrocorticogram (ECoG) is a promising long-term signal acquisition platform for brain-computer interface (BCI) systems such as upper extremity prostheses. Several studies have demonstrated decoding of arm and finger trajectories from ECoG high-gamma band (80-160 Hz) signals. In this study, we systematically vary the velocity of three elementary movement types (pincer grasp, elbow and shoulder flexion/extension) to test whether the high-gamma band encodes for the entirety of the movements, or merely the movement onset. To this end, linear regression models were created for the durations and amplitudes of high-gamma power bursts and velocity deflections. One subject with 8×8 high-density ECoG grid (4 mm center-to-center electrode spacing) participated in the experiment. The results of the regression models indicated that the power burst durations varied directly with the movement durations (e.g. R(2)=0.71 and slope=1.0 s/s for elbow). The persistence of power bursts for the duration of the movement suggests that the primary motor cortex (M1) is likely active for the entire duration of a movement, instead of providing a marker for the movement onset. On the other hand, the amplitudes were less co-varied. Furthermore, the electrodes of maximum R(2) conformed to somatotopic arrangement of the brain. Also, electrodes responsible for flexion and extension movements could be resolved on the high-density grid. In summary, these findings suggest that M1 may be directly responsible for activating the individual muscle motor units, and future BCI may be able to utilize them for better control of prostheses.

Evaluating a four-class motor-imagery-based optical brain-computer interface.

This work investigates the potential of a four-class motor-imagery-based brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS). Four motor imagery tasks (right hand, left hand, right foot, and left foot tapping) were executed while motor cortex activity was recorded via fNIRS. Preliminary results from three participants suggest that this could be a viable BCI interface, with two subjects achieving 50% accuracy. fNIRS is a noninvasive, safe, portable, and affordable optical brain imaging technique used to monitor cortical hemodynamic changes. Because of its portability and ease of use, fNIRS is amenable to deployment in more natural settings. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) BCIs have already been used with up to four motor-imagery-based commands. While fNIRS-based BCIs are relatively new, success with EEG and fMRI systems, as well as signal characteristics similar to fMRI and complementary to EEG, suggest that fNIRS could serve to build or augment future BCIs.

Brain–computer interfaces and dualism: a problem of brain, mind, and body

The brain---computer interface (BCI) has made remarkable progress in the bridging the divide between the brain and the external environment to assist persons with severe disabilities caused by brain impairments. There is also continuing philosophical interest in BCIs which emerges from thoughtful reflection on computers, machines, and artificial intelligence. This article seeks to apply BCI perspectives to examine, challenge, and work towards a possible resolution to a persistent problem in the mind---body relationship, namely dualism. The original humanitarian goals of BCIs and the technological inventiveness result in BCIs being surprisingly useful. We begin from the neurologically impaired person, the problems encountered, and some pioneering responses from computers and machines. Secondly, the interface of mind and brain is explored via two points of clarification: direct and indirect BCIs, and the nature of thoughts. Thirdly, dualism is beset by mind---body interaction difficulties and is further questioned by the phenomena of intentions, interactions, and technology. Fourthly, animal minds and robots are explored in BCI settings again with relevance for dualism. After a brief look at other BCIs, we conclude by outlining a future BCI philosophy of brain and mind, which might appear ominous and could be possible.

Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future

The Fifth International Brain-Computer Interface (BCI) Meeting met on 3–7 June 2013 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 19 workshops covering topics in brain-computer interface and brain-machine interface research. Topics included translation of BCIs into clinical use, standardization and certification, types of brain activity to use for BCI, recording methods, the effects of plasticity, special interest topics in BCIs applications, and future BCI directions. BCI research is well established and transitioning to practical use to benefit people with physical impairments. At the same time, new applications are being explored, both for people with physical impairments and beyond. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues for future research and development.

Preserved Foot Motor Cortex in Patients With Complete Spinal Cord Injury

Background. Since the brain is intact, persons with a spinal cord injury (SCI) might benefit from a brain–computer interface (BCI) to improve mobility by making use of functional near-infrared spectroscopy (fNIRS). Objective. We aimed to use fNIRS to detect contralateral primary motor cortex activity during attempted foot movements in participants with complete SCI. Methods. A 6-channel fNIRS, including 2 reference channels, measured relative concentration changes of oxy- (HbO) and deoxy-hemoglobin (HbR) in the contralateral motor cortex for the right foot. Seven subjects, studied within 18 months after injury, performed 12 trials of attempted right foot and real hand movements. Results. T tests revealed significant HbO and HbR responses of the left motor cortex for attempted foot movements, but not for right hand movements. A 2-way repeated-measures analysis of variance revealed a larger decrease in HbR for attempted foot movements compared to hand movements. Individual results show major interindividual differences in (number of) channels activated and the sensitive chromophore (HbR or HbO). Conclusions. On group level, activity in the motor cortex of the foot can be measured with fNIRS in patients with complete SCI during attempted foot movements and might in principle be used in future BCI studies and applications.

Brain–computer interfaces using capacitive measurement of visual or auditory steady-state responses

Objective. Brain–computer interface (BCI) technologies have been intensely studied to provide alternative communication tools entirely independent of neuromuscular activities. Current BCI technologies use electroencephalogram (EEG) acquisition methods that require unpleasant gel injections, impractical preparations and clean-up procedures. The next generation of BCI technologies requires practical, user-friendly, nonintrusive EEG platforms in order to facilitate the application of laboratory work in real-world settings. Approach. A capacitive electrode that does not require an electrolytic gel or direct electrode–scalp contact is a potential alternative to the conventional wet electrode in future BCI systems. We have proposed a new capacitive EEG electrode that contains a conductive polymer-sensing surface, which enhances electrode performance. This paper presents results from five subjects who exhibited visual or auditory steady-state responses according to BCI using these new capacitive electrodes. The steady-state visual evoked potential (SSVEP) spelling system and the auditory steady-state response (ASSR) binary decision system were employed. Main results. Offline tests demonstrated BCI performance high enough to be used in a BCI system (accuracy: 95.2%, ITR: 19.91 bpm for SSVEP BCI (6 s), accuracy: 82.6%, ITR: 1.48 bpm for ASSR BCI (14 s)) with the analysis time being slightly longer than that when wet electrodes were employed with the same BCI system (accuracy: 91.2%, ITR: 25.79 bpm for SSVEP BCI (4 s), accuracy: 81.3%, ITR: 1.57 bpm for ASSR BCI (12 s)). Subjects performed online BCI under the SSVEP paradigm in copy spelling mode and under the ASSR paradigm in selective attention mode with a mean information transfer rate (ITR) of 17.78 ± 2.08 and 0.7 ± 0.24 bpm, respectively. Significance. The results of these experiments demonstrate the feasibility of using our capacitive EEG electrode in BCI systems. This capacitive electrode may become a flexible and non-intrusive tool fit for various applications in the next generation of BCI technologies.

Decoding finger movements from ECoG signals using Empirical Mode Decomposition

ECoG promises exact localization of brain sources by providing high spatial resolution and good signal quality, thus makes it the premier choice for future BCI applications. Unfortunately decoding these signals is not as straightforward as one would expect. In this work we applied a time-frequency analysis based on Empirical Mode decomposition (EMD) and Adaptive Filtering (AF) to decode and estimate the finger movement using 10 minutes-long, multi-channel ECoG signals. The dataset was recorded from three epileptic patients at Harborview Hospital in Seattle, Washington for Brain Computer Interface (BCI) Competition IV. Our proposed method showed the average correlation of 0.55 between real and predicted movement across the subjects and across fingers.

Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces

Dry and noncontact electroencephalographic (EEG) electrodes, which do not require gel or even direct scalp coupling, have been considered as an enabler of practical, real-world, brain-computer interface (BCI) platforms. This study compares wet electrodes to dry and through hair, noncontact electrodes within a steady state visual evoked potential (SSVEP) BCI paradigm. The construction of a dry contact electrode, featuring fingered contact posts and active buffering circuitry is presented. Additionally, the development of a new, noncontact, capacitive electrode that utilizes a custom integrated, high-impedance analog front-end is introduced. Offline tests on 10 subjects characterize the signal quality from the different electrodes and demonstrate that acquisition of small amplitude, SSVEP signals is possible, even through hair using the new integrated noncontact sensor. Online BCI experiments demonstrate that the information transfer rate (ITR) with the dry electrodes is comparable to that of wet electrodes, completely without the need for gel or other conductive media. In addition, data from the noncontact electrode, operating on the top of hair, show a maximum ITR in excess of 19 bits/min at 100% accuracy (versus 29.2 bits/min for wet electrodes and 34.4 bits/min for dry electrodes), a level that has never been demonstrated before. The results of these experiments show that both dry and noncontact electrodes, with further development, may become a viable tool for both future mobile BCI and general EEG applications.

Rehabilitation of gait after stroke: a review towards a top-down approach.

This document provides a review of the techniques and therapies used in gait rehabilitation after stroke. It also examines the possible benefits of including assistive robotic devices and brain-computer interfaces in this field, according to a top-down approach, in which rehabilitation is driven by neural plasticity.The methods reviewed comprise classical gait rehabilitation techniques (neurophysiological and motor learning approaches), functional electrical stimulation (FES), robotic devices, and brain-computer interfaces (BCI).From the analysis of these approaches, we can draw the following conclusions. Regarding classical rehabilitation techniques, there is insufficient evidence to state that a particular approach is more effective in promoting gait recovery than other. Combination of different rehabilitation strategies seems to be more effective than over-ground gait training alone. Robotic devices need further research to show their suitability for walking training and their effects on over-ground gait. The use of FES combined with different walking retraining strategies has shown to result in improvements in hemiplegic gait. Reports on non-invasive BCIs for stroke recovery are limited to the rehabilitation of upper limbs; however, some works suggest that there might be a common mechanism which influences upper and lower limb recovery simultaneously, independently of the limb chosen for the rehabilitation therapy. Functional near infrared spectroscopy (fNIRS) enables researchers to detect signals from specific regions of the cortex during performance of motor activities for the development of future BCIs. Future research would make possible to analyze the impact of rehabilitation on brain plasticity, in order to adapt treatment resources to meet the needs of each patient and to optimize the recovery process.

Effects of P300-Based BCI Use on Reported Presence in a Virtual Environment

Brain–computer interfaces (BCIs) are becoming more and more popular as an input device for virtual worlds and computer games. Depending on their function, a major drawback is the mental workload associated with their use and there is significant effort and training required to effectively control them. In this paper, we present two studies assessing how mental workload of a P300-based BCI affects participants' reported sense of presence in a virtual environment (VE). In the first study, we employ a BCI exploiting the P300 event-related potential (ERP) that allows control of over 200 items in a virtual apartment. In the second study, the BCI is replaced by a gaze-based selection method coupled with wand navigation. In both studies, overall performance is measured and individual presence scores are assessed by means of a short questionnaire. The results suggest that there is no immediate benefit for visualizing events in the VE triggered by the BCI and that no learning about the layout of the virtual space takes place. In order to alleviate this, we propose that future P300-based BCIs in VR are set up so as require users to make some inference about the virtual space so that they become aware of it, which is likely to lead to higher reported presence.

Constant pressure fluid infusion into rat neocortex from implantable microfluidic devices

Implantable electrode arrays capable of recording and stimulating neural activity with high spatial and temporal resolution will provide a foundation for future brain computer interface technology. Currently, their clinical impact has been curtailed by a general lack of functional stability, which can be attributed to the acute and chronic reactive tissue responses to devices implanted in the brain. Control of the tissue environment surrounding implanted devices through local drug delivery could significantly alter both the acute and chronic reactive responses, and thus enhance device stability. Here, we characterize pressure-mediated release of test compounds into rat cortex using an implantable microfluidic platform. A fixed volume of fluorescent cell marker cocktail was delivered using constant pressure infusion at reservoir backpressures of 0, 5 and 10 psi. Affected tissue volumes were imaged and analyzed using epifluorescence and confocal microscropies and quantitative image analysis techniques. The addressable tissue volume for the 5 and 10 psi infusions, defined by fluorescent staining with Hoescht 33342 dye, was significantly larger than the tissue volume addressed by simple diffusion (0 psi) and the tissue volume exhibiting insertion-related cell damage (stained by propidium iodide). The results demonstrate the potential for using constant pressure infusion to address relevant tissue volumes with appropriate pharmacologies to alleviate reactive biological responses around inserted neuroprosthetic devices.

Predicting Reaching Targets from Human EEG

Extracting reach information from brain signals is of great interest to the fields of brain-computer interfaces (BCIs) and human motor control. To date, most work in this area has focused on invasive intracranial recordings; however, successful decoding of reach targets from noninvasive electroencephalogram (EEG) signals would be of great interest. In this article, we show that EEG signals contain sufficient information to decode target location during a reach (Experiment 1) and during the planning period before a reach (Experiment 2). We discuss the application of independent component analysis and dipole fitting for removing movement artifacts. With this technique we get similar classification accuracy for classifying EEG signals during a reach (Experiment 1) and during the planning period before a reach (Experiment 2). To the best of our knowledge, this is the first demonstration of decoding (planned) reach targets from EEG. These results lay the foundation for future EEG-based BCIs based on decoding of planned reaches.

Brain–computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation

Brain-computer interfaces (BCIs) are a rehabilitation tool for tetraplegic patients that aim to improve quality of life by augmenting communication, control of the environment, and self-care. The neurobiology of both rehabilitation and BCI control depends upon learning to modify the efficacy of spared neural ensembles that represent movement, sensation and cognition through progressive practice with feedback and reward. To serve patients, BCI systems must become safe, reliable, cosmetically acceptable, quickly mastered with minimal ongoing technical support, and highly accurate even in the face of mental distractions and the uncontrolled environment beyond a laboratory. BCI technologies may raise ethical concerns if their availability affects the decisions of patients who become locked-in with brain stem stroke or amyotrophic lateral sclerosis to be sustained with ventilator support. If BCI technology becomes flexible and affordable, volitional control of cortical signals could be employed for the rehabilitation of motor and cognitive impairments in hemiplegic or paraplegic patients by offering on-line feedback about cortical activity associated with mental practice, motor intention, and other neural recruitment strategies during progressive task-oriented practice. Clinical trials with measures of quality of life will be necessary to demonstrate the value of near-term and future BCI applications.

Online Control of a Brain-Computer Interface Using Phase Synchronization

Currently, almost all brain-computer interfaces (BCIs) ignore the relationship between phases of electroencephalographic signals detected from different recording sites (i.e., electrodes). The vast majority of BCI systems rely on feature vectors derived from e.g., bandpower or univariate adaptive autoregressive (AAR) parameters. However, ample evidence suggests that additional information is obtained by quantifying the relationship between signals of single electrodes, which might provide innovative features for future BCI systems. This paper investigates one method to extract the degree of phase synchronization between two electroencephalogram (EEG) signals by calculating the so-called phase locking value (PLV). In our offline study, several PLV-based features were acquired and the optimal feature set was selected for each subject individually by a feature selection algorithm. The online sessions with three trained subjects revealed that all subjects were able to control three mental states (motor imagery of left hand, right hand, and foot, respectively) with single-trial accuracies between 60% and 66.7% (33% would be expected by chance) throughout the whole session.