The Brain-Computer Interface (BCI) was envisioned as an assistive technology option for people with severe movement impairments. The traditional synchronous event-related potential (ERP) BCI design uses a fixed communication speed and is vulnerable to variations in attention. Recent ERP BCI designs have added asynchronous features, including abstention and dynamic stopping, but it remains a open question of how to evaluate asynchronous BCI performance. In this work, we build on the BCI-Utility metric to create the first evaluation metric with special consideration of the asynchronous features of self-paced BCIs. This metric considers accuracy as all of the following three - probability of a correct selection when a selection was intended, probability of making a selection when a selection was intended, and probability of an abstention when an abstention was intended. Further, it considers the average time required for a selection when using dynamic stopping and the proportion of intended selections versus abstentions. We establish the validity of the derived metric via extensive simulations, and illustrate and discuss its practical usage on real-world BCI data. We describe the relative contribution of different inputs with plots of BCI-Utility curves under different parameter settings. Generally, the BCI-Utility metric increases as any of the accuracy values increase and decreases as the expected time for an intended selection increases. Furthermore, in many situations, we find shortening the expected time of an intended selection is the most effective way to improve the BCI-Utility, which necessitates the advancement of asynchronous BCI systems capable of accurate abstention and dynamic stopping.
Brain-Computer Interfaces , Electroencephalography , Humans , Event-Related Potentials, P300 , Evoked Potentials , Movement
Performance estimation is a necessary step in the development and validation of Brain-Computer Interface (BCI) systems. Unfortunately, even modern BCI systems are slow, making collecting sufficient data for validation a time-consuming task for end users and experimenters alike. Yet without sufficient data, the random variation in performance can lead to false inferences about how well a BCI is working for a particular user. For example, P300 spellers commonly operate around 1-5 characters per minute. To estimate accuracy with a 5% resolution requires 20 characters (4-20 min). Despite this time investment, the confidence bounds for accuracy from 20 characters can be as much as ±23% depending on observed accuracy. A previously published method, Classifier-Based Latency Estimation (CBLE), was shown to be highly correlated with BCI accuracy. This work presents a protocol for using CBLE to predict a user's P300 speller accuracy from relatively few characters (~3-8) of typing data. The resulting confidence bounds are tighter than those produced by traditional methods. The method can thus be used to estimate BCI performance more quickly and/or more accurately.
Brain-Computer Interfaces , Mental Processes
Objective: This study examined the effect of individualized electroencephalogram (EEG) electrode location selection for non-invasive P300-design brain-computer interfaces (BCIs) in people with varying severity of cerebral palsy (CP). Approach: A forward selection algorithm was used to select the best performing 8 electrodes (of an available 32) to construct an individualized electrode subset for each participant. BCI accuracy of the individualized subset was compared to accuracy of a widely used default subset. Main Results: Electrode selection significantly improved BCI calibration accuracy for the group with severe CP. Significant group effect was not found for the group of typically developing controls and the group with mild CP. However, several individuals with mild CP showed improved performance. Using the individualized electrode subsets, there was no significant difference in accuracy between calibration and evaluation data in the mild CP group, but there was a reduction in accuracy from calibration to evaluation in controls. Significance: The findings suggested that electrode selection can accommodate developmental neurological impairments in people with severe CP, while the default electrode locations are sufficient for many people with milder impairments from CP and typically developing individuals.
Brain-computer interfaces (BCIs) have been successfully used by adults, but little information is available on BCI use by children, especially children with severe multiple impairments who may need technology to facilitate communication. Here we discuss the challenges of using non-invasive BCI with children, especially children who do not have another established method of communication with unfamiliar partners. Strategies to manage these challenges require consideration of multiple factors related to accessibility, cognition, and participation. These factors include decisions regarding where (home, clinic, or lab) participation will take place, the number of sessions involved, and the degree of participation necessary for success. A strategic approach to addressing the unique challenges inherent in BCI use by children with disabilities will increase the potential for successful BCI calibration and adoption of BCI as a valuable access method for children with the most significant impairments in movement and communication.
A brain-computer interface (BCI) is a system that translates brain activity into commands to operate technology. A common design for an electroencephalogram (EEG) BCI relies on the classification of the P300 event-related potential (ERP), which is a response elicited by the rare occurrence of target stimuli among common non-target stimuli. Few existing ERP classifiers directly explore the underlying mechanism of the neural activity. To this end, we perform a novel Bayesian analysis of the probability distribution of multi-channel real EEG signals under the P300 ERP-BCI design. We aim to identify relevant spatial temporal differences of the neural activity, which provides statistical evidence of P300 ERP responses and helps design individually efficient and accurate BCIs. As one key finding of our single participant analysis, there is a 90% posterior probability that the target ERPs of the channels around visual cortex reach their negative peaks around 200 milliseconds post-stimulus. Our analysis identifies five important channels (PO7, PO8, Oz, P4, Cz) for the BCI speller leading to a 100% prediction accuracy. From the analyses of nine other participants, we consistently select the identified five channels, and the selection frequencies are robust to small variations of bandpass filters and kernel hyper-parameters.
Objective: To examine measurement agreement between a vocabulary test that is administered in the standardized manner and a version that is administered with a brain-computer interface (BCI). Method: The sample was comprised of 21 participants, ages 9-27, mean age 16.7 (5.4) years, 61.9% male, including 10 with congenital spastic cerebral palsy (CP), and 11 comparison peers. Participants completed both standard and BCI-facilitated alternate versions of the Peabody Picture Vocabulary Test - 4 (PPVT™-4). The BCI-facilitated PPVT-4 uses items identical to the unmodified PPVT-4, but each quadrant forced-choice item is presented on a computer screen for use with the BCI. Results: Measurement agreement between instruments was excellent, including an intra-class correlation coefficient of 0.98, and Bland-Altman plots and tests indicating adequate limits of agreement and no systematic test version bias. The mean standard score difference between test versions was 2.0 points (SD 6.3). Conclusion: These results demonstrate that BCI-facilitated quadrant forced-choice vocabulary testing has the potential to measure aspects of language without requiring any overt physical or communicative response. Thus, it may be possible to identify the language capabilities and needs of many individuals who have not had access to standardized clinical and research instruments.
The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9th, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering topics in BCI (also called brain-machine interface) research. As in the past, workshops covered the breadth of topics in BCI. Some workshops provided detailed examinations of specific methods, hardware, or processes. Others focused on specific BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and increase the ease of comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were both the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, presents an overview of the discussion topics, and describes the conclusion, challenges, or initiatives that resulted from the interactions and discussion at the workshop.
Advancements in novel neurotechnologies, such as brain computer interfaces (BCI) and neuromodulatory devices such as deep brain stimulators (DBS), will have profound implications for society and human rights. While these technologies are improving the diagnosis and treatment of mental and neurological diseases, they can also alter individual agency and estrange those using neurotechnologies from their sense of self, challenging basic notions of what it means to be human. As an international coalition of interdisciplinary scholars and practitioners, we examine these challenges and make recommendations to mitigate negative consequences that could arise from the unregulated development or application of novel neurotechnologies. We explore potential ethical challenges in four key areas: identity and agency, privacy, bias, and enhancement. To address them, we propose (1) democratic and inclusive summits to establish globally-coordinated ethical and societal guidelines for neurotechnology development and application, (2) new measures, including "Neurorights," for data privacy, security, and consent to empower neurotechnology users' control over their data, (3) new methods of identifying and preventing bias, and (4) the adoption of public guidelines for safe and equitable distribution of neurotechnological devices.
A Brain-Computer Interface (BCI) is a device that interprets brain activity to help people with disabilities communicate. The P300 ERP-based BCI speller displays a series of events on the screen and searches the elicited electroencephalogram (EEG) data for target P300 event-related potential (ERP) responses among a series of non-target events. The Checkerboard (CB) paradigm is a common stimulus presentation paradigm. Although a few studies have proposed data-driven methods for stimulus selection, they suffer from intractable decision rules, large computation complexity, or error propagation for participants who perform poorly under the static paradigm. In addition, none of the methods have been applied to the CB paradigm directly. In this work, we propose a sequence-based adaptive stimulus selection method using Thompson Sampling in the multi-bandit problem with multiple actions. During each sequence, the algorithm selects a random subset of stimuli with fixed size, aiming to identify all target stimuli and to improve the spelling speed by reducing the number of unnecessary non-target stimuli. We compute "clean" stimulus-specific rewards from raw classifier scores via the Bayes rule. We perform extensive simulation studies to compare our algorithm to the static CB paradigm. We show the robustness of our algorithm by considering the constraints of practical use. For scenarios where simulated data resemble the real data the most, the spelling efficiency of our algorithm increases by more than 70%, compared to the static CB paradigm.
Brain-Computer Interfaces (BCIs) have been used to restore communication and control to people with severe paralysis. However, non-invasive BCIs based on electroencephalogram (EEG) are particularly vulnerable to noise artifacts. These artifacts, including electro-oculogram (EOG), can be orders of magnitude larger than the signal to be detected. Many automated methods have been proposed to remove EOG and other artifacts from EEG recordings, most based on blind source separation. This work presents a performance comparison of ten different automated artifact removal methods. Unfortunately, all tested methods substantially and significantly reduced P3 Speller BCI performance, and all methods were more likely to reduce performance than increase it. The least harmful methods were titled SOBI, JADER, and EFICA, but even these methods caused an average of approximately ten percentage points drop in BCI accuracy. Possible mechanistic causes for this empirical performance deduction are proposed.
Much brain-computer interface (BCI) research is intended to benefit people with disabilities (PWD), but inclusion of these individuals as study participants remains relatively rare. When participants with disabilities are included, they are described with a range of clinical and non-clinical terms with varying degrees of specificity, often leading to difficulty in interpreting or replicating results. This study examined trends in inclusion and description of study participants with disabilities across six International BCI Meetings from 1999 to 2016. Abstracts from each Meeting were analyzed by two trained independent reviewers. Results suggested a decline in participation by PWD across Meetings until the 2016 Meeting. Increased diagnostic specificity was noted at the 2013 and 2016 Meetings. Fifty-eight percent of the abstracts identified PWD as being the target beneficiaries of BCI research, though only twenty-two percent included participants with disabilities, suggesting evidence of a persistent translational gap. Participants with disabilities were most commonly described as having physical and/or communication impairments compared to impairments in other areas. Implementing participatory action research principles and user-centered design strategies continues to be necessary within BCI research to bridge the translational gap and facilitate use of BCI systems within functional environments for PWD.
The Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics in BCI (also called brain-machine interface) research. Workshops covered foundational topics such as hardware development and signal analysis algorithms, new and imaginative topics such as BCI for virtual reality and multi-brain BCIs, and translational topics such as clinical applications and ethical assumptions of BCI development. BCI research is expanding in the diversity of applications and populations for whom those applications are being developed. BCI applications are moving toward clinical readiness as researchers struggle with the practical considerations to make sure that BCI translational efforts will be successful. This paper summarizes each workshop, providing an overview of the topic of discussion, references for additional information, and identifying future issues for research and development that resulted from the interactions and discussion at the workshop.
Event-related potentials (ERPs) are the brain response directly related to specific events or stimuli. The P300 ERP is a positive deflection nominally 300ms post-stimulus that is related to mental decision making processes and also used in P300-based speller systems. Single-trial estimation of P300 responses will help to understand the underlying cognitive process more precisely and also to improve the speed of speller brain-computer interfaces (BCIs). This paper aims to develop a single-trial estimation of the P300 amplitudes and latencies by using the least mean squares (LMS) adaptive filtering method. Results for real data from people with amyotrophic lateral sclerosis (ALS) have shown that the LMS filter can be effectively used to estimate P300 latencies.
Brain-Computer Interfaces , Event-Related Potentials, P300 , Electroencephalography , Least-Squares Analysis
The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.
Artificial Intelligence/ethics , Bioengineering/ethics , Brain-Computer Interfaces/ethics , Codes of Ethics , Guidelines as Topic , Neurosciences/ethics , Privacy , Alzheimer Disease/diagnosis , Animals , Artificial Intelligence/economics , Artificial Intelligence/trends , Bioengineering/economics , Bioengineering/trends , Biomedical Enhancement/ethics , Biomedical Enhancement/methods , Brain-Computer Interfaces/economics , Brain-Computer Interfaces/trends , Electroencephalography , Female , Humans , Individuality , Informed Consent/ethics , Informed Consent/legislation & jurisprudence , Male , Neural Networks, Computer , Neurosciences/economics , Neurosciences/trends , Parkinson Disease/diagnosis , Privacy/legislation & jurisprudence
Brain Computer Interfaces (BCIs) offer restoration of communication to those with the most severe movement impairments, but performance is not yet ideal. Previous work has demonstrated that latency jitter, the variation in timing of the brain responses, plays a critical role in determining BCI performance. In this study, we used Classifier-Based Latency Estimation (CBLE) and a wavelet transform to provide information about latency jitter to a second-level classifier. Three second-level classifiers were tested: least squares (LS), step-wise linear discriminant analysis (SWLDA), and support vector machine (SVM). Of these three, LS and SWLDA performed better than the original online classifier. The resulting combination demonstrated improved detection of brain responses for many participants, resulting in better BCI performance. Interestingly, the performance gain was greatest for those individuals for whom the BCI did not work well online, indicating that this method may be most suitable for improving performance of otherwise marginal participants.
Brain-computer interfaces (BCIs) are intended to provide independent communication for those with the most severe physical impairments. However, development and testing of BCIs is typically conducted with copy-spelling of provided text, which models only a small portion of a functional communication task. This study was designed to determine how BCI performance is affected by novel text generation. We used a within-subject single-session study design in which subjects used a BCI to perform copy-spelling of provided text and to generate self-composed text to describe a picture. Additional off-line analysis was performed to identify changes in the event-related potentials that the BCI detects and to examine the effects of training the BCI classifier on task-specific data. Accuracy was reduced during the picture description task; (t(8)=2.59 p=0.0321). Creating the classifier using self-generated text data significantly improved accuracy on these data; (t(7)=-2.68, p=0.0317), but did not bring performance up to the level achieved during copy-spelling. Thus, this study shows that the task for which the BCI is used makes a difference in BCI accuracy. Task-specific BCI classifiers are a first step to counteract this effect, but additional study is needed.
A formal definition of brain-computer interface (BCI) is as follows: a system that acquires brain signal activity and translates it into an output that can replace, restore, enhance, supplement, or improve the existing brain signal, which can, in turn, modify or change ongoing interactions between the brain and its internal or external environment. More simply, a BCI can be defined as a system that translates "brain signals into new kinds of outputs." After brain signal acquisition, the BCI evaluates the brain signal and extracts signal features that have proven useful for task performance. There are 2 broad categories of BCIs: implantable and noninvasive, distinguished by invasively and noninvasively acquired brain signals, respectively. For this supplement, we will focus on BCIs that use noninvasively acquired brain signals.
Brain-Computer Interfaces , Communication Aids for Disabled , Leisure Activities , Physical Therapy Modalities/instrumentation , Recovery of Function , Humans
OBJECTIVES: To identify perceptions among people with spinal cord injury (SCI) of the priorities for brain-computer interface (BCI) applications and design features along with the time investment and risk acceptable to obtain a BCI. DESIGN: Survey. SETTING: Research registry participants surveyed via telephone and BCI usage study participants surveyed in person before BCI use. PARTICIPANTS: Convenience sample of people with SCI (N=40), consisting of persons from the registry (n=30) and from the BCI study (n=10). Participants were classified as those with low function (n=24) and those with high function (n=16). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Descriptive statistics of functional independence, living situations and support structures, ratings of importance of different task and design features, and acceptable levels of performance, risk, and time investment. RESULTS: BCIs were of interest to 96% of the low-function group. Emergency communication was the top priority task (ranked in the top 2 by 43%). The most important design features were "functions the BCI provides" and "simplicity of BCI setup." Desired performance was 90% accuracy, with standby mode errors no more than once every 4 hours and speeds of more than 20 letters per minute. Dry electrodes were preferred over gel or implanted electrodes (P<.05). Median acceptable setup time was 10 to 20 minutes, satisfying 65% of participants. CONCLUSIONS: People with low functional independence resulting from SCI have a strong interest in BCIs. Advances in speed and setup time will be required for BCIs to meet the desired performance. Creating BCI functions appropriate to the needs of those with SCI will be of ultimate importance for BCI acceptance with this population.
Brain-Computer Interfaces , Spinal Cord Injuries/rehabilitation , Adult , Aged , Caregivers , Communication Aids for Disabled , Electroencephalography , Female , Humans , Male , Middle Aged , Physical Therapy Modalities , Socioeconomic Factors , Spinal Cord Injuries/psychology , Trauma Severity Indices , User-Computer Interface
