Table of contents

This special issue of Journal of Neural Engineering is a result of the Fourth International Brain–Computer Interface Meeting, which was held at the Asilomar Conference Center in Monterey, California, USA from 31 May to 4 June, 2010. The meeting was sponsored by the National Institutes of Health, The National Science Foundation and the Department of Defense, and was organized by the Wadsworth Center of the New York State Department of Health. It attracted over 260 participants from 17 countries—including many graduate students and postdoctoral fellows—and featured 19 workshops, platform presentations from 26 research groups, 170 posters, multiple brain–computer interface (BCI) demonstrations, and a keynote address by W Zev Rymer of the Rehabilitation Institute of Chicago. The number of participants and the diversity of the topics covered greatly exceeded those of the previous meeting in 2005, and testified to the continuing rapid expansion and growing sophistication of this exciting and still relatively new research field.

BCI research focuses primarily on using brain signals to replace or restore the motor functions that people have lost due to amyotrophic lateral sclerosis (ALS), a brainstem stroke, or some other devastating neuromuscular disorder. In the last few years, attention has also turned towards using BCIs to improve rehabilitation after a stroke, and beyond that to enhancing or supplementing the capabilities of even those without disabilities. These diverse interests were represented in the wide range of topics covered in the workshops. While some workshops addressed broad traditional topics, such as signal acquisition, feature extraction and translation, and software development, many addressed topics that were entirely new or focused sharply on areas that have become important only recently. These included workshops on optimizing P300-based BCIs; improving the mutual adaptations of the BCI and the user; BCIs that can control neuroprostheses, robotic arms, and other complex devices; moving BCIs from the laboratory to the home; BCIs that can induce neural plasticity and restore function; BCIs that use metabolic brain signals; novel BCI designs; non-medical BCI applications; ethical issues in BCI research; and contentious issues in BCI research.

Dr Rymer's keynote address, 'BCI: a long range view from within a rehabilitation hospital', applied lessons from the field of rehabilitation robotics to the current state and future prospects of BCI technology. He noted that the present enthusiasm for BCI research and development reflects a desire to help people and to make basic research serve clinical needs, the excitement of the challenges for engineers and scientists, and (perhaps overly optimistic) anticipation of future benefits. He emphasized the importance of focusing research on major clinical problems that affect large numbers of people and can be addressed by BCI technology. In this regard, he identified the common and devastating motor problems produced by hemispheric stroke and limb loss as realistic and important targets for BCI research. He also cautioned against overly aggressive invasive BCI studies that might produce adverse events that could sharply curtail support and enthusiasm for further work.

The keynote address, and presentations and discussions throughout the meeting, brought out five major issues that will help determine whether BCIs realize the exciting future that many envision for them. The first issue is the identification and characterization of those brain signals that are best able to encode user intent and the development of improved methods for recording these signals. Both noninvasive and invasive BCIs need sensors and associated hardware that are robust, convenient, cosmetically acceptable and function reliably and safely for long periods with minimal ongoing technical support. The second issue is the need for BCI software that optimizes the ongoing adaptive interactions between the BCI and the user to achieve the reliability lacking in current BCIs. Marked improvement in reliability is essential if BCIs are to move from the laboratory into widespread practical use for significant purposes in real life. The third issue is the development of BCI applications that serve the most important unmet needs of specific populations of potential users. BCI applications are needed that can extend the current spectrum of assistive technology or augment rehabilitation to move beyond what is possible using conventional methods. The fourth issue is the critical requirement for well-designed studies that validate the ability of BCIs to serve the needs and improve the lives of people with severe disabilities. Without such translational studies, BCIs will never realize the promise projected from laboratory results. Finally, the future of BCI technology depends on the realization of clinically and economically viable models for implementing and supporting its widespread dissemination.

Effective attention to these five issues requires cooperation among researchers from many diverse disciplines: neuroscientists, engineers, psychologists, applied mathematicians, computer scientists, clinicians and rehabilitation specialists. The translational research essential for the validation of BCI technology is particularly dependent on such multidisciplinary collaborations. The need for multidisciplinary interactions has been the primary impetus for the four international BCI meetings to date. In the service of this continuing need, a discussion session at the meeting resulted in the formation of a multidisciplinary steering committee to organize future meetings at three-year intervals. Chaired by Jane E Huggins of the University of Michigan, this committee is planning the next meeting for 2013.

The 28 primary research articles and workshop-based reviews that comprise this special issue reflect the substance and range of the meeting, and illustrate the current state of the field. The articles are loosely organized into three groups: signal acquisition; feature extraction and translation; and applications. The large number of application studies indicates the critical importance of this area for the ultimate significance of BCI research and development.

A brain–computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the development of certification, dissemination and reimbursement procedures.

This paper reviews several critical issues facing signal processing for brain–computer interfaces (BCIs) and suggests several recent approaches that should be further examined. The topics were selected based on discussions held during the 4th International BCI Meeting at a workshop organized to review and evaluate the current state of, and issues relevant to, feature extraction and translation of field potentials for BCIs. The topics presented in this paper include the relationship between electroencephalography and electrocorticography, novel features for performance prediction, time-embedded signal representations, phase information, signal non-stationarity, and unsupervised adaptation.

This paper summarizes the presentations and discussions at a workshop held during the Fourth International BCI Meeting charged with reviewing and evaluating the current state, limitations and future development of P300-based brain–computer interface (P300-BCI) systems. We reviewed such issues as potential users, recording methods, stimulus presentation paradigms, feature extraction and classification algorithms, and applications. A summary of the discussions and the panel's recommendations for each of these aspects are presented.

Analyzing neural signals and providing feedback in realtime is one of the core characteristics of a brain–computer interface (BCI). As this feature may be employed to induce neural plasticity, utilizing BCI technology for therapeutic purposes is increasingly gaining popularity in the BCI community. In this paper, we discuss the state-of-the-art of research on this topic, address the principles of and challenges in inducing neural plasticity by means of a BCI, and delineate the problems of study design and outcome evaluation arising in this context. We conclude with a list of open questions and recommendations for future research in this field.

Cognitive monitoring is an approach utilizing realtime brain signal decoding (RBSD) for gaining information on the ongoing cognitive user state. In recent decades this approach has brought valuable insight into the cognition of an interacting human. Automated RBSD can be used to set up a brain–computer interface (BCI) providing a novel input modality for technical systems solely based on brain activity. In BCIs the user usually sends voluntary and directed commands to control the connected computer system or to communicate through it. In this paper we propose an extension of this approach by fusing BCI technology with cognitive monitoring, providing valuable information about the users' intentions, situational interpretations and emotional states to the technical system. We call this approach passive BCI. In the following we give an overview of studies which utilize passive BCI, as well as other novel types of applications resulting from BCI technology. We especially focus on applications for healthy users, and the specific requirements and demands of this user group. Since the presented approach of combining cognitive monitoring with BCI technology is very similar to the concept of BCIs itself we propose a unifying categorization of BCI-based applications, including the novel approach of passive BCI.

A brain–computer interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. Most research investigating BCI in humans has used scalp-recorded electroencephalography or intracranial electrocorticography. The use of brain signals obtained directly from stereotactic depth electrodes to control a BCI has not previously been explored. In this study, event-related potentials (ERPs) recorded from bilateral stereotactic depth electrodes implanted in and adjacent to the hippocampus were used to control a P300 Speller paradigm. The ERPs were preprocessed and used to train a linear classifier to subsequently predict the intended target letters. The classifier was able to predict the intended target character at or near 100% accuracy using fewer than 15 stimulation sequences in the two subjects tested. Our results demonstrate that ERPs from hippocampal and hippocampal adjacent depth electrodes can be used to reliably control the P300 Speller BCI paradigm.

For the development of minimally invasive brain–computer interfaces (BCIs), it is important to accurately localize the area of implantation. Using fMRI, we investigated which brain areas are involved in motor imagery. Twelve healthy subjects performed a motor execution and imagery task during separate fMRI and EEG measurements. fMRI results showed that during imagery, premotor and parietal areas were most robustly activated in individual subjects, but surprisingly, no activation was found in the primary motor cortex. EEG results showed that spectral power decreases in contralateral sensorimotor rhythms (8–24 Hz) during both movement and imagery. To further verify the involvement of the motor imagery areas found with fMRI, one epilepsy patient performed the same task during both fMRI and ECoG recordings. Significant ECoG low (8–24 Hz) and high (65–95 Hz) frequency power changes were observed selectively on premotor cortex and these co-localized with fMRI. During a subsequent BCI task, excellent performance (91%) was obtained based on ECoG power changes from the localized premotor area. These results indicate that other areas than the primary motor area may be more reliably activated during motor imagery. Specifically, the premotor cortex may be a better area to implant an invasive BCI.

In this paper, we present a new, low-cost dry electrode for EEG that is made of flexible metal-coated polymer bristles. We examine various standard EEG paradigms, such as capturing occipital alpha rhythms, testing for event-related potentials in an auditory oddball paradigm and performing a sensory motor rhythm-based event-related (de-) synchronization paradigm to validate the performance of the novel electrodes in terms of signal quality. Our findings suggest that the dry electrodes that we developed result in high-quality EEG recordings and are thus suitable for a wide range of EEG studies and BCI applications. Furthermore, due to the flexibility of the novel electrodes, greater comfort is achieved in some subjects, this being essential for long-term use.

All brain–computer interface (BCI) groups that have published results of studies involving a large number of users performing BCI control based on the voluntary modulation of sensorimotor rhythms (SMR) report that BCI control could not be achieved by a non-negligible number of subjects (estimated 20% to 25%). This failure of the BCI system to read the intention of the user is one of the greatest problems and challenges in BCI research. There are two main causes for this problem in SMR-based BCI systems: either no idle SMR is observed over motor areas of the user, or this idle rhythm is not modulated during motor imagery, resulting in a classification performance lower than 70% (criterion level) that renders the control of a BCI application (like a speller) difficult or impossible. Previously, we introduced the concept of machine learning based co-adaptive calibration, which provided substantially improved performance for a variety of users. Here, we use a similar approach and investigate to what extent co-adaptive learning enables significant BCI control for completely novice users, as well as for those who could not achieve control with a conventional SMR-based BCI.

Brain–computer interface (BCI) systems rely on the direct measurement of brain signals, such as event-related desynchronization (ERD), steady state visual evoked potentials (SSVEPs), P300s, or slow cortical potentials. Unfortunately, none of these BCI approaches work for all users. This study compares two conventional BCI approaches (ERD and SSVEP) within subjects, and also evaluates a novel hybrid BCI based on a combination of these signals. We recorded EEG data from 12 subjects across three conditions. In the first condition, subjects imagined moving both hands or both feet (ERD). In the second condition, subjects focused on one of the two oscillating visual stimuli (SSVEP). In the third condition, subjects simultaneously performed both tasks. We used logarithmic band power features at sites and frequencies consistent with ERD and SSVEP activity, and subjects received real-time feedback based on their performance. Subjects also completed brief questionnaires. All subjects could simultaneously perform the movement and visual task in the hybrid condition even though most subjects had little or no training. All subjects showed both SSVEP and ERD activity during the hybrid task, consistent with the activity in both single tasks. Subjects generally considered the hybrid condition moderately more difficult, but all of them were able to complete the hybrid task. Results support the hypothesis that subjects who do not have strong ERD activity might be more effective with an SSVEP BCI, and suggest that SSVEP BCIs work for more subjects. A simultaneous hybrid BCI is feasible, although the current hybrid approach, which involves combining ERD and SSVEP in a two-choice task to improve accuracy, is not significantly better than a comparable SSVEP BCI. Switching to an SSVEP BCI could increase reliability in subjects who have trouble producing the EEG activity necessary to use an ERD BCI. Subjects who are proficient in both BCI approaches might be able to combine these approaches in different ways and for different goals.

Hybrid brain–computer interfaces (BCIs) are representing a recent approach to develop practical BCIs. In such a system disabled users are able to use all their remaining functionalities as control possibilities in parallel with the BCI. Sometimes these people have residual activity of their muscles. Therefore, in the presented hybrid BCI framework we want to explore the parallel usage of electroencephalographic (EEG) and electromyographic (EMG) activity, whereby the control abilities of both channels are fused. Results showed that the participants could achieve a good control of their hybrid BCI independently of their level of muscular fatigue. Thereby the multimodal fusion approach of muscular and brain activity yielded better and more stable performance compared to the single conditions. Even in the case of an increasing muscular fatigue a good control (moderate and graceful degradation of the performance compared to the non-fatigued case) and a smooth handover could be achieved. Therefore, such systems allow the users a very reliable hybrid BCI control although they are getting more and more exhausted or fatigued during the day.

Laplacian filters are widely used in neuroscience. In the context of brain–computer interfacing, they might be preferred to data-driven approaches such as common spatial patterns (CSP) in a variety of scenarios such as, e.g., when no or few user data are available or a calibration session with a multi-channel recording is not possible, which is the case in various applications. In this paper we propose the use of an ensemble of local CSP patches (CSPP) which can be considered as a compromise between Laplacian filters and CSP. Our CSPP only needs a very small number of trials to be optimized and significantly outperforms Laplacian filters in all settings studied. Additionally, CSPP also outperforms multi-channel CSP and a regularized version of CSP even when only very few calibration data are available, acting as a CSP regularizer without the need of additional hyperparameters and at a very low cost: 2–5 min of data recording, i.e. ten times less than CSP.

Conditional random fields (CRFs) are demonstrated to be a discriminative model able to exploit the temporal properties of EEG data obtained during synchronous three-class motor-imagery-based brain–computer interface experiments. The advantages of CRFs over the hidden Markov model (HMM) are both theoretical and practical. Theoretically, CRFs focus on modeling latent variables (labels) rather than both observation and latent variables. Furthermore, CRFs' loss function is convex, guaranteeing convergence to the global optimum. Practically, CRFs are much less prone to singularity problems. This property allows for the use of both time- and frequency-based features, such as band power. The HMM, on the other hand, requires temporal features such as autoregressive coefficients. A CRF-based classifier is tested on 13 subjects. Significant improvement is found when applying CRFs over HMM- and LDA-based classifiers.

The extraction of distinctly separable features from electroencephalogram (EEG) is one of the main challenges in designing a brain–computer interface (BCI). Existing feature extraction techniques for a BCI are mostly developed based on traditional signal processing techniques assuming that the signal is Gaussian and has linear characteristics. But the motor imagery (MI)-related EEG signals are highly non-Gaussian, non-stationary and have nonlinear dynamic characteristics. This paper proposes an advanced, robust but simple feature extraction technique for a MI-related BCI. The technique uses one of the higher order statistics methods, the bispectrum, and extracts the features of nonlinear interactions over several frequency components in MI-related EEG signals. Along with a linear discriminant analysis classifier, the proposed technique has been used to design an MI-based BCI. Three performance measures, classification accuracy, mutual information and Cohen's kappa have been evaluated and compared with a BCI using a contemporary power spectral density-based feature extraction technique. It is observed that the proposed technique extracts nearly recording-session-independent distinct features resulting in significantly much higher and consistent MI task detection accuracy and Cohen's kappa. It is therefore concluded that the bispectrum-based feature extraction is a promising technique for detecting different brain states.

Recently, electroencephalogram-based brain–computer interfaces (BCIs) have attracted much attention in the fields of neural engineering and rehabilitation due to their noninvasiveness. However, the low communication speed of current BCI systems greatly limits their practical application. In this paper, we present a high-speed BCI based on code modulation of visual evoked potentials (c-VEP). Thirty-two target stimuli were modulated by a time-shifted binary pseudorandom sequence. A multichannel identification method based on canonical correlation analysis (CCA) was used for target identification. The online system achieved an average information transfer rate (ITR) of 108 ± 12 bits min⁻¹ on five subjects with a maximum ITR of 123 bits min⁻¹ for a single subject.

Arm end-point position, end-point velocity, and the intended final location or 'goal' of a reach have all been decoded from cortical signals for use in brain–machine interface (BMI) applications. These different aspects of arm movement can be decoded from the brain and used directly to control the position, velocity, or movement goal of a device. However, these decoded parameters can also be remapped to control different aspects of movement, such as using the decoded position of the hand to control the velocity of a device. People easily learn to use the position of a joystick to control the velocity of an object in a videogame. Similarly, in BMI systems, the position, velocity, or goal of a movement could be decoded from the brain and remapped to control some other aspect of device movement. This study evaluates how easily people make transformations between position, velocity, and reach goal in BMI systems. It also evaluates how different amounts of decoding error impact on device control with and without these transformations. Results suggest some remapping options can significantly improve BMI control. This study provides guidance on what remapping options to use when various amounts of decoding error are present.

Volitional control of cortical activity is relevant for optimizing control signals for neuroprosthetic devices. We explored the control of firing rates of single cortical cells in two Macaca nemestrina monkeys by providing visual feedback of neural activity and rewarding changes in cell rates. During 'brain-control' sessions, the monkeys modulated the activity of each of 246 cells to acquire targets requiring high or low discharge rates. Cell control improved more than two-fold from the beginning of practice to peak performance. Cell activity was modulated substantially more during brain control than during wrist movements. When recording stability permitted, the monkeys practiced controlling activity of the same cells across multiple days. The performance improved substantially for 27 of 36 cells when practicing brain control across days. The monkeys maintained discharge rates within each target for 1 s, but could maintain rates for up to 3 s for some cells, and performed the brain-control task equally well using cells recorded from the pre-central cortex compared to cells in the post-central cortex, and independently of any directional tuning. These findings demonstrate that arbitrary single cortical neurons, regardless of the strength of directional tuning, are capable of controlling cursor movements in a one-dimensional brain–machine interface. It is possible that direct conversion of activity from single cortical cells to control signals for neuroprosthetic devices may be a useful complementary strategy to population decoding of the intended movement direction.

Moving a brain–computer interface (BCI) system from a laboratory demonstration to real-life applications still poses severe challenges to the BCI community. This study aims to integrate a mobile and wireless electroencephalogram (EEG) system and a signal-processing platform based on a cell phone into a truly wearable and wireless online BCI. Its practicality and implications in a routine BCI are demonstrated through the realization and testing of a steady-state visual evoked potential (SSVEP)-based BCI. This study implemented and tested online signal processing methods in both time and frequency domains for detecting SSVEPs. The results of this study showed that the performance of the proposed cell-phone-based platform was comparable, in terms of the information transfer rate, with other BCI systems using bulky commercial EEG systems and personal computers. To the best of our knowledge, this study is the first to demonstrate a truly portable, cost-effective and miniature cell-phone-based platform for online BCIs.

In this study, we examined the effects of a short mindfulness meditation induction (MMI) on the performance of a P300-based brain–computer interface (BCI) task. We expected that MMI would harness present-moment attentional resources, resulting in two positive consequences for P300-based BCI use. Specifically, we believed that MMI would facilitate increases in task accuracy and promote the production of robust P300 amplitudes. Sixteen-channel electroencephalographic data were recorded from 18 subjects using a row/column speller task paradigm. Nine subjects participated in a 6 min MMI and an additional nine subjects served as a control group. Subjects were presented with a 6 × 6 matrix of alphanumeric characters on a computer monitor. Stimuli were flashed at a stimulus onset asynchrony (SOA) of 125 ms. Calibration data were collected on 21 items without providing feedback. These data were used to derive a stepwise linear discriminate analysis classifier that was applied to an additional 14 items to evaluate accuracy. Offline performance analyses revealed that MMI subjects were significantly more accurate than control subjects. Likewise, MMI subjects produced significantly larger P300 amplitudes than control subjects at Cz and PO7. The discussion focuses on the potential attentional benefits of MMI for P300-based BCI performance.

The main purpose of electroencephalography (EEG)-based brain–computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how sensorimotor rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naïve participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle's cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22–29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.

Methods of statistical machine learning have recently proven to be very useful in contemporary brain–computer interface (BCI) research based on the discrimination of electroencephalogram (EEG) patterns. Because of this, many research groups develop new algorithms for both feature extraction and classification. However, until now, no large-scale comparison of these algorithms has been accomplished due to the fact that little EEG data is publicly available. Therefore, we at Team PhyPA recorded 32-channel EEGs, electromyograms and electrooculograms of 36 participants during a simple finger movement task. The data are published on our website www.phypa.org and are freely available for downloading. We encourage BCI researchers to test their algorithms on these data and share their results. This work also presents exemplary benchmarking procedures of common feature extraction methods for slow cortical potentials and event-related desynchronization as well as for classification algorithms based on these features.

The Farwell and Donchin matrix speller is well known as one of the highest performing brain–computer interfaces (BCIs) currently available. However, its use of visual stimulation limits its applicability to users with normal eyesight. Alternative BCI spelling systems which rely on non-visual stimulation, e.g. auditory or tactile, tend to perform much more poorly and/or can be very difficult to use. In this paper we present a novel extension of the matrix speller, based on flipping the letter matrix, which allows us to use the same interface for visual, auditory or simultaneous visual and auditory stimuli. In this way we aim to allow users to utilize the best available input modality for their situation, that is use visual + auditory for best performance and move smoothly to purely auditory when necessary, e.g. when disease causes the user's eyesight to deteriorate. Our results on seven healthy subjects demonstrate the effectiveness of this approach, with our modified visual + auditory stimulation slightly out-performing the classic matrix speller. The purely auditory system performance was lower than for visual stimulation, but comparable to other auditory BCI systems.

Eleven channels of EEG were recorded from a subject performing four mental tasks. A time-embedded representation of the untransformed EEG samples was constructed. Classification of the time-embedded samples was performed by linear and quadratic discriminant analysis and by an artificial neural network. A classifier's output for consecutive samples is combined to increase reliability. A new performance measure is defined as the number of correct selections that would be made by a brain–computer interface (BCI) user of the system, accounting for the need for an incorrect selection to be followed by a correct one to 'delete' the previous selection. A best result of 0.32 correct selections s⁻¹ (about 3 s per BCI decision) was obtained with a neural network using a time-embedding dimension of 50.

Since the introduction of the P300 brain–computer interface (BCI) speller by Farwell and Donchin in 1988, the speed and accuracy of the system has been significantly improved. Larger electrode montages and various signal processing techniques are responsible for most of the improvement in performance. New presentation paradigms have also led to improvements in bit rate and accuracy (e.g. Townsend et al (2010 Clin. Neurophysiol.121 1109–20)). In particular, the checkerboard paradigm for online P300 BCI-based spelling performs well, has started to document what makes for a successful paradigm, and is a good platform for further experimentation. The current paper further examines the checkerboard paradigm by suppressing items which surround the target from flashing during calibration (i.e. the suppression condition). In the online feedback mode the standard checkerboard paradigm is used with a stepwise linear discriminant classifier derived from the suppression condition and one classifier derived from the standard checkerboard condition, counter-balanced. The results of this research demonstrate that using suppression during calibration produces significantly more character selections/min ((6.46) time between selections included) than the standard checkerboard condition (5.55), and significantly fewer target flashes are needed per selection in the SUP condition (5.28) as compared to the RCP condition (6.17). Moreover, accuracy in the SUP and RCP conditions remained equivalent (∼90%). Mean theoretical bit rate was 53.62 bits/min in the suppression condition and 46.36 bits/min in the standard checkerboard condition (ns). Waveform morphology also showed significant differences in amplitude and latency.

Brain–computer interface (BCI) systems allow people with severe motor disabilities to communicate and interact with the external world. The P300 potential is one of the most used control signals for EEG-based BCIs. Classic P300-based BCIs work in a synchronous mode; the synchronous control assumes that the user is constantly attending to the stimulation, and the number of stimulation sequences is fixed a priori. This issue is an obstacle for the use of these systems in everyday life; users will be engaged in a continuous control state, their distractions will cause misclassification and the speed of selection will not take into account users' current psychophysical condition. An efficient BCI system should be able to understand the user's intentions from the ongoing EEG instead. Also, it has to refrain from making a selection when the user is engaged in a different activity and it should increase or decrease its speed of selection depending on the current user's state. We addressed these issues by introducing an asynchronous BCI and tested its capabilities for effective environmental monitoring, involving 11 volunteers in three recording sessions. Results show that this BCI system can increase the bit rate during control periods while the system is proved to be very efficient in avoiding false negatives when the users are engaged in other tasks.

The steady state visual evoked protocol has recently become a popular paradigm in brain–computer interface (BCI) applications. Typically (regardless of function) these applications offer the user a binary selection of targets that perform correspondingly discrete actions. Such discrete control systems are appropriate for applications that are inherently isolated in nature, such as selecting numbers from a keypad to be dialled or letters from an alphabet to be spelled. However motivation exists for users to employ proportional control methods in intrinsically analogue tasks such as the movement of a mouse pointer. This paper introduces an online BCI in which control of a mouse pointer is directly proportional to a user's intent. Performance is measured over a series of pointer movement tasks and compared to the traditional discrete output approach. Analogue control allowed subjects to move the pointer faster to the cued target location compared to discrete output but suffers more undesired movements overall. Best performance is achieved when combining the threshold to movement of traditional discrete techniques with the range of movement offered by proportional control.

The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking activity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were successfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical microelectrode array can provide repeatable, accurate point-and-click control of a computer interface to an individual with tetraplegia 1000 days after implantation of this sensor.

Advancing the brain–computer interface (BCI) towards practical applications in technology-based assistive solutions for people with disabilities requires coping with problems of accessibility and usability to increase user acceptance and satisfaction. The main objective of this study was to introduce a usability-oriented approach in the assessment of BCI technology development by focusing on evaluation of the user's subjective workload and satisfaction. The secondary aim was to compare two applications for a P300-based BCI. Eight healthy subjects were asked to use an assistive technology solution which integrates the P300-based BCI with commercially available software under two conditions—visual stimuli needed to evoke the P300 response were either overlaid onto the application's graphical user interface or presented on a separate screen. The two conditions were compared for effectiveness (level of performance), efficiency (subjective workload measured by means of NASA-TXL) and satisfaction of the user. Although no significant difference in usability could be detected between the two conditions, the methodology proved to be an effective tool to highlight weaknesses in the technical solution.