National Audit of Percutaneous Coronary Interventions (NAPCI) monitors with a view to drive up an improvement in the quality of care and patient outcomes that receive percutaneous coronary intervention (PCI). - Structure of the provision of PCI services across the UK (for example the number of PCI centres, number of PCIs per centre and population, number of operators). - Clinical care and treatment provided by each hospital, measured against national aggregated data and agreed national standards (for example indication for treatment, use of stents, arterial access routes). - Process of care (for example delays in receiving treatments such as primary PCI). - Outcome for patients such as complications, adverse cardiac events and death. The datasets contain information on the percutaneous coronary interventions for ACS and stable angina: - Call to balloon with degree of improvement - Door to balloon with a degree of improvement - Use of drug eluting stents and use of radial artery access - Number of PCI per centre - Key fields data completeness The datasets are effectively tables underpinning the graphical presentations of the results in the annual public report and selection of slides from the BCIS website. Patient outcomes – in this case PCI related complication rates - are published by the operator and at the hospital level on the BCIS and MyNHS websites. These data do not include any data about individual patients nor do they contain any patient identifiable data.

Data accompanying the manuscript "Long-term unsupervised recalibration of cursor BCIs", consisting of closed-loop cursor control datasets collected with participant T5. The dataset includes historical sessions as well as new online tests of recalibration methods collected specifically for this manuscript. It also contains results of parameter optimizations and cursor control simulations. Personal use data collected with participant T11 (Figure 6) is not included, as it may contain PHI. The README.md file describes each of the four data formats included. This data is meant to be used with the accompanying github repository: guyhwilson/nonstationarities: Unsupervised recalibration project. Original abstract from the manuscript: Intracortical brain-computer interfaces (iBCIs) require frequent recalibration to maintain robust performance due to changes in neural activity that accumulate over time. Compensating for this nonstationarity would enable consistently high performance without the need for supervised recalibration periods, where users cannot engage in personal use of their device. Here we introduce a hidden Markov model (HMM) to infer what targets users are moving toward during iBCI use. We then retrain the system using these inferred targets, enabling unsupervised adaptation to changing neural activity. Our approach outperforms distribution alignment methods in large-scale, closed-loop simulations over two months, and in closed-loop with a human iBCI user over one month. Leveraging an offline dataset spanning five years of iBCI recordings, we further show how recently proposed data distribution-matching approaches to recalibration fail over long time scales. Only target-inference methods appear capable of enabling long-term unsupervised recalibration, while distribution-matching methods appear to accumulate compounding error over time. Finally, we show offline that our approach also performs well on freeform datasets of a person using a home computer with an iBCI. Our results demonstrate how task structure can be used to bootstrap a noisy decoder into a highly-performant one, thereby overcoming one of the major barriers to clinically translating BCIs.

Bcis Bahrain International Cargo Services W L L Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Brain-Computer Interface Insurance Market Outlook

According to our latest research, the global Brain-Computer Interface (BCI) Insurance market size reached USD 1.17 billion in 2024, driven by the rapid integration of BCI technologies across multiple industries and rising concerns over data security and user safety. The market is expected to grow at a CAGR of 16.8% during the forecast period of 2025 to 2033, reaching a projected value of USD 4.13 billion by 2033. This robust growth is primarily fueled by the increasing adoption of BCI in healthcare, gaming, and smart home applications, as well as the evolving risk landscape that demands specialized insurance products tailored for BCI-enabled devices and platforms.

One of the primary growth factors of the Brain-Computer Interface Insurance market is the exponential advancement and deployment of BCI technologies in the healthcare sector. Hospitals and healthcare providers are increasingly utilizing BCIs for neurorehabilitation, assistive communication, and advanced prosthetics, which raises complex liability and product risk issues. The sensitive nature of neural data and the critical role of BCIs in patient care necessitate comprehensive insurance coverage, including health, product, and cyber insurance. Insurers are developing specialized policies to address unique risks such as device malfunction, data breaches, and liability arising from unintended outcomes, thereby driving demand for tailored insurance solutions. The growing regulatory scrutiny on medical devices and patient safety further amplifies the need for robust insurance frameworks, making healthcare a dominant application area in this market.

Another significant driver is the proliferation of BCI applications in gaming, entertainment, and smart home environments. As consumer-grade BCIs become more accessible, users are exposed to new forms of cyber threats, privacy breaches, and accidental misuse. The integration of BCIs with virtual reality, augmented reality, and IoT devices creates a complex ecosystem where data flows between multiple endpoints, increasing the attack surface for cybercriminals. This scenario propels the demand for cyber insurance and liability coverage, as both individuals and enterprises seek to mitigate financial losses from hacking, data theft, and unauthorized access. The insurance industry is responding with innovative products that cover not only the devices but also the digital assets and personal data associated with BCI usage, thus supporting market expansion.

The evolution of regulatory and legal frameworks is also a major catalyst for the growth of the Brain-Computer Interface Insurance market. Governments and international bodies are working to establish clear guidelines for the ethical use, data protection, and interoperability of BCIs. These regulations often mandate insurance coverage for manufacturers, service providers, and end-users to ensure accountability and risk mitigation. The emergence of standards for device safety, user consent, and data privacy has prompted insurers to design policies that comply with regulatory requirements, fostering trust among stakeholders. As compliance becomes a prerequisite for market entry, insurance adoption is expected to accelerate, particularly among enterprises and healthcare providers seeking to avoid legal complications and reputational damage.

Regionally, North America leads the Brain-Computer Interface Insurance market, accounting for the largest share due to its advanced healthcare infrastructure, high adoption of emerging technologies, and strong presence of BCI manufacturers. Europe follows closely, driven by stringent data protection laws and a proactive approach to digital health. The Asia Pacific region is witnessing the fastest growth, supported by rising investments in healthcare innovation and expanding consumer electronics markets. Latin America and the Middle East & Africa are gradually catching up, as awareness of BCI applications and associated risks increases. Each region presents unique challenges and opportunities, shaped by local regulations, technological maturity, and insurance penetration rates.

Goal

The dataset contains data from 22 healthy subjects controlling a brain-computer interface (BCI) system based on event related potentials (ERP). The exterpiment was aimed to collect data during control and non-control tasks to improve asynchronous mangament of the system. For more information, see the original study [1].

Code

Check the code section to find the dataset exploration script. Additionally, in the official repository you will find the original code of EEG-Inception, a novel CNN for EEG classification tasks that was presented in [2]. Official EEG-Inception repository: https://github.com/esantamariavazquez/EEG-Inception

Experimental setup

Twenty-two healthy subjects (age: 24.7±4.3 years; 15 males) participated in the experiments. All participants had normal or corrected-to-normal vision. The experimental protocol was approved by the local ethics committee and all participants gave their informed consent.

Participants were sat on a comfortable chair in front of 2 screens keeping a distance of 50 cm, as displayed in Fig. 1a. The screen on the right showed the BCI application, whereas the screen on the left displayed a web browser. Accordingly, the experiment comprised 2 different procedures: the control task and the non-control task. In the control task, participants were asked to make selections with an ERP-based speller using the row-column paradigm (RCP). In this paradigm, commands are displayed in a matrix, whose rows and columns are highlighted sequentially in random order. When each row and column is highlighted once, the algorithm completes a sequence. Thus, participants had to stare at the desired command, which was indicated by the supervisor. Of note, participants were instructed to mentally count the stimuli on the target to maintain the concentration. For this task, we used the 6x6 matrix displayed in Fig. 1a, with an inter-stimulus interval of 100 ms and a stimulus duration of 75 ms. The target commands were selected randomly. In the non-control task, participants had to use the web browser at their will to read a document or watch a video while ignoring the stimuli on the right screen, simulating the real use of the system for assistive applications.

The experiment flow is described in Fig. 1b. The experiment comprised 2 sessions of 10 runs (i.e., 5 control and 5 non-control), which had 6 trials of 15 sequences each. Noteworthy, the tasks were intercalated in order to avoid excessive fatigue. Therefore, the database was composed by 60 control trials and 60 non-control trials for each subject.

Fig. 1. (a) Schematic representation of the subject and both screens. The screen on the left displayed the browser that was used during the non-control task, whereas the speller was showed on the right screen. Although the paradigm was active during both tasks, subjects only had to attend to the stimuli during the control task. (b) Overview of the experiment, which comprised 2 sessions of 10 runs, 6 trials of 15 sequences each. Both tasks were intercalated to avoid excessive fatigue of the subject.

Dataset content

Signals were recorded using a g.USBampg (g.tec medical engineering, Austria) with a sample frequency of 256 Hz and using 8 active electrodes in positions Fz, Cz, Pz, P3, P4, PO7, PO8, Oz according to the international 10-10 system. The ground and reference were placed at FPz and the earlobe, respectively. The raw EEG was preprocessed to increase the SNR of the target signals. First, the signal is filtered between 0.5 and 45 Hz with a finite impulse response filter and resampled to 128 Hz, keeping the most discriminative information for control state and ERP classification. Then, common average reference (CAR) is used to remove noisy artifacts. Afterward, we extracted the epochs of signal for each stimulus from 0 to 1000 ms after the onset. Additionally, z-score normalization was applied taking a baseline window of 250 ms before the stimulus onset. At the end of this process, each observation had 128 samples x 8 channels. Therefore, each trial had 180 observations (12 rows and columns x 15 sequences).

The dataset consists of an h5 file with the following variables:

• features [n_stimuli x n_samples x n_channels] -> EEG epochs of shape (128 x 8) after preprocessing and epoch extraction. Channel order: ['FZ', 'CZ', 'PZ', 'P3', 'P4', 'PO7', 'PO8', 'OZ'].
• erp_labels [n_stimuli x 1] -> ERP labels (i.e., 0 for non-target stimulus, 1 for target stimulus)
• codes [n_stimuli x 1] -> Code of the row or column that was highlighted
• trials [n_stimuli x 1] -> Array that relates each stimulus to its trial. Use example: features[trials==10] returns the EEG epochs of the 10th trial.
• sequences [n_stimuli x 1] -> Array that relates each stimulus to its number of sequence. Use example: features[...

Body condition is a measure of the health and fitness of an organism represented by available energy stores, typically fat. Direct measurements of fat are difficult to obtain non-invasively, thus body condition is usually estimated by calculating body condition indices (BCIs) using mass and length. The utility of BCIs is contingent on the relationship of BCIs and fat, thereby validation studies should be performed to select the best-performing BCI before application in ecological investigations. We evaluated 11 BCIs in 883 Argentine black and white tegus (Salvator merianae) removed from their non-native range in South Florida, United States. Because the length-mass relationship in tegus is allometric, a segmented linear regression model was fit to the relationship between mass and length to define size classes. We evaluated percent, residual, and scaled fat and determined percent fat was the best measure of fat because it was the least associated with snout-vent length (SVL). We evaluated performance of BCIs with the full dataset and within size classes and identified Fulton’s K as the best-performing BCI for our sampled population, explaining up to 19% of the variation in fat content. Overall, we found that BCIs: 1) maintained relatively weak relationships with measures of fat and 2) splitting data into size classes reduced the strength of the relationship (i.e., bias) between percent fat and SVL but did not improve the performance of BCIs. We postulate that the weak performance of BCIs in our dataset was likely due to the weak association of fat with SVL, the body plan and life-history traits of tegus, and potentially inadequate accounting of available energy resources. We caution against assuming that BCIs are strong indicators of body condition across species and suggest that validation studies be implemented, or that alternative or complementary measures of health or fitness should be considered. Methods Species description The Argentine black and white tegu is a large teiid lizard native to eastern and central South America that has become established in several areas of central and southern Florida, United States. Tegus can be classified as omnivores as well as generalist meso-predators which consume fruit, plant material, and animal prey (arthropods, gastropods, reptiles, birds, and small mammals), with evidence of diet changing based on seasonal abundance. Tegus are also known to scavenge upon carrion and regularly consume eggs of ground-nesting vertebrates such as alligators, turtles, and birds. This species is considered invasive in Florida with potential negative impacts on native fauna through egg predation and competition for burrowing sites. To combat this threat, management programs have been established in Florida to reduce the population of invasive tegus and halt their expansion into ecologically sensitive areas, such as nesting sites of threatened species such as American crocodile (Crocodylus acutus) and gopher tortoises (Gopherus polyphemus). Collection, euthanasia, and necropsy We received tegus collected through trapping and removal efforts performed by the Florida Fish and Wildlife Conservation Commission (FWC) and the University of Florida (UF) from 2012 through 2018. Once obtained, tegus were humanely euthanized using captive bolt or firearm immediately followed by pithing, and frozen until necropsy. Tegus were thawed prior to necropsy and examined for general health and condition by visually inspecting all internal organs and the body exterior for any abnormalities or deformities that may affect body mass, body length, or fat mass. We obtained measurements of snout-vent length (SVL) to the nearest 0.1 cm using a flexible measuring tape, and total body mass using a digital scale to the nearest g. Coelomic wet-fat mass was obtained by removing and weighing the discrete abdominal fat bodies to the nearest 0.0001 g. The average timespan a tegu was held between euthanasia and necropsy was approximately 263 ± 258 days (range 0–1,847 days). The research protocol was approved by the University of Florida Animal Research Committee and University of Florida Institutional Animal Care and Use Committee and protocol numbers are listed in the Acknowledgements. Statistical methods To reduce potential bias in our dataset, we excluded data from tegus with incomplete or unreliable necropsies due to decay, unknown sex, physical abnormalities (missing or abnormal limbs or tails (e.g., regenerated tail), and scoliosis), or if the time spent in captivity prior to euthanasia was more than 4 days. We also removed animals whose wet-fat mass was equal to 0, which could not be transformed with natural log. All data analyses were performed in R. Additional information on the following analyses using this data is available within the associated manuscript.

IntroductionMovement-based brain-computer interfaces (BCIs) utilize brain activity generated during executed or attempted movement to provide control over applications. By relying on natural movement processes, these BCIs offer a more intuitive control compared to other BCI systems. However, non-invasive movement-based BCIs utilizing electroencephalographic (EEG) signals usually require large amounts of training data to achieve suitable accuracy in the detection of movement intent. Additionally, patients with movement impairments require cue-based paradigms to indicate the start of a movement-related task. Such paradigms tend to introduce long delays between trials, thereby extending training times. To address this, we propose a novel experimental paradigm that enables the collection of 300 cued movement trials in 18 min.MethodsBy obtaining measurements from ten participants, we demonstrate that the data produced by this paradigm exhibits characteristics similar to those observed during self-paced movement.Results and discussionWe also show that classifiers trained on this data can be used to accurately detect executed movements with an average true positive rate of 31.8% at a maximum rate of 1.0 false positives per minute.

BackgroundBrain-computer interfaces (BCIs) represent a ground-breaking advancement in neuroscience, facilitating direct communication between the brain and external devices. This technology has the potential to significantly improve the lives of individuals with neurological disorders by providing innovative solutions for rehabilitation, communication and personal autonomy. However, despite the rapid progress in BCI technology and social media discussions around Neuralink, public perceptions and ethical considerations concerning BCIs—particularly within community settings in the UK—have not been thoroughly investigated.ObjectiveThe primary aim of this study was to investigate public knowledge, attitudes and perceptions regarding BCIs including ethical considerations. The study also explored whether demographic factors were related to beliefs about BCIs increasing inequalities, support for strict regulations, and perceptions of appropriate fields for BCI design, testing and utilization in healthcare.MethodsThis cross-sectional study was conducted between 1 December 2023 and 8 March 2024. The survey included 29 structured questions covering demographics, awareness of BCIs, ethical considerations and willingness to use BCIs for various applications. The survey was distributed via the Imperial College Qualtrics platform. Participants were recruited primarily through Prolific Academic’s panel and personal networks. Data analysis involved summarizing responses using frequencies and percentages, with chi-squared tests to compare groups. All data were securely stored and pseudo-anonymized to ensure confidentiality.ResultsOf the 950 invited respondents, 846 participated and 806 completed the survey. The demographic profile was diverse, with most respondents aged 36–45 years (26%) balanced in gender (52% female), and predominantly identifying as White (86%). Most respondents (98%) had never used BCIs, and 65% were unaware of them prior to the survey. Preferences for BCI types varied by condition. Ethical concerns were prevalent, particularly regarding implantation risks (98%) and costs (92%). Significant associations were observed between demographic variables and perceptions of BCIs regarding inequalities, regulation and their application in healthcare. Conclusion: Despite strong interest in BCIs, particularly for medical applications, ethical concerns, safety and privacy issues remain significant highlighting the need for clear regulatory frameworks and ethical guidelines, as well as educational initiatives to improve public understanding and trust. Promoting public discourse and involving stakeholders including potential users, ethicists and technologists in the design process through co-design principles can help align technological development with public concerns whilst also helping developers to proactively address ethical dilemmas.

BCIS forecast data showing 15% construction cost increases over five years, broken down by labour costs (16% increase), materials (13% increase), Building Safety Act compliance costs, and programme extension impacts on SME residential developers

Used in conjunction with the formulae price adjustment method of adjusting building and civil and specialist engineering contracts.

These statistics are now produced by BCIS and can be found on their website. BIS no longer has any involvement with the production of these statistics.

Source agency: Business, Innovation and Skills

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Price Adjustment Formulae for Construction Contracts

Construction Output Price Indices (OPIs) from January 2014 to September 2025, UK. Summary

Advancements in machine learning in combination with fundamental research in cognitive neuroscience have put forth application areas for brain-computer interfaces (BCIs) that go beyond communication and control. The ability to decode covert mental states and intentions from the electroencephalogram (EEG) in real-time – hence, to study the "brain at work" – establishes the basis for multifaceted applications of non-control BCIs. In this thesis, the use of such BCIs is demonstrated with two independent studies which both have different research directions and serve different purposes. While the first study follows what has been the traditional path of BCI research, namely the development of an application for people, the second study strikes a new path by engaging in the hitherto unsought approach to use a closed-loop BCI as a research tool for cognitive neuroscience. The first study aims for the classification of operator workload as it is expected in many real-life workplace environments. Brain-signal based workload predictors, based on modulations of the power of theta and alpha oscillations in the EEG associated with workload changes, were explored. The predictors differed with respect to the level of label information required for training, including an entirely unsupervised approach. This was made possible by employing stateof- the-art EEG spatial filtering methods from machine learning. Mean classification accuracies above 90% were achieved with the supervised predictors and 82% with the unsupervised approach. The findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable. The second study investigates the role of the readiness potential (RP), a slow cortical potential that starts more than 1 second before spontaneous, voluntary movements. Despite decades-long research in cognitive neuroscience, it has yet remained unclear whether the onset of the RP triggers a chain of events that unfolds in time and cannot be cancelled or whether people can cancel movements after onset of the RP. In this study, this question was addressed in a realtime experiment in which subjects were required to terminate their decision to move upon seeing a stop signal. This signal was elicited by a BCI that had been trained to detect RPs in the ongoing EEG. It was found that subjects could indeed cancel intended movements after the onset of the RP, however only up to a point of no return at approximately 200 ms before movement onset. The finding that the onset of the RP does not trigger a ballistic process that cannot be stopped throws some light on the controversial debate regarding the role of the RP in movement preparation.

Brain-computer interfaces (BCIs) have been studied extensively in order to establish a non-muscular communication channel mainly for patients with impaired motor functions. However, many limitations remain for BCIs in clinical use. In this study, we propose a hybrid BCI that is based on only frontal brain areas and can be operated in an eyes-closed state for end users with impaired motor and declining visual functions. In our experiment, electroencephalography (EEG) and near-infrared spectroscopy (NIRS) were simultaneously measured while 12 participants performed mental arithmetic (MA) and remained relaxed (baseline state: BL). To evaluate the feasibility of the hybrid BCI, we classified MA- from BL-related brain activation. We then compared classification accuracies using two unimodal BCIs (EEG and NIRS) and the hybrid BCI in an offline mode. The classification accuracy of the hybrid BCI (83.9 ± 10.3 %) was shown to be significantly higher than those of unimodal EEG-based (77.3 ± 15.9 %) and NIRS-based BCI (75.9 ± 6.3 %). The analytical results confirmed performance improvement with the hybrid BCI, particularly for only frontal brain areas. Our study shows that an eyes-closed hybrid BCI approach based on frontal areas could be applied to neurodegenerative patients who lost their motor functions, including oculomotor functions.

Restorative brain–computer interfaces (BCIs) have been proposed to enhance stroke rehabilitation. Restorative BCIs are able to close the sensorimotor loop by rewarding motor imagery (MI) with sensory feedback. Despite the promising results from early studies, reaching clinically significant outcomes in a timely fashion is yet to be achieved. This lack of efficacy may be due to suboptimal feedback provision. To the best of our knowledge, the optimal feedback update interval (FUI) during MI remains unexplored. There is evidence that sensory feedback disinhibits the motor cortex. Thus, in this study, we explore how shorter than usual FUIs affect behavioural and neurophysiological measures following BCI training for stroke patients using a single-case proof-of-principle study design. The action research arm test was used as the primary behavioural measure and showed a clinically significant increase (36%) over the course of training. The neurophysiological measures including motor evoked potentials and maximum voluntary contraction showed distinctive changes in early and late phases of BCI training. Thus, this preliminary study may pave the way for running larger studies to further investigate the effect of FUI magnitude on the efficacy of restorative BCIs. It may also elucidate the role of early and late phases of motor learning along the course of BCI training.

Objective: Brain Computer Interfaces (BCIs) are slowly making their appearance on the consumer market, accompanied by a higher popularity among the general public. This new group of users requires easy-to-use headsets with robustness to non-precise placement. In this paper, an optimized fixed montage EEG headset for VEP BCIs is proposed. Approach: The proposed layout covers only the most relevant area with large sensors to account for slight misplacement. To obtain large sensors, without having them physically available, we tie multiple sensors together and simulate the effect by averaging the signal of multiple sensors. Main results: In simulations based on recorded 256-channel EEG data, it is shown that a circular center-surround configuration with sensor tying, leading to only 8 channels covering a large part of the occipital lobe, can provide high performance and good robustness to misplacement. Automatically optimized layouts were unable to achieve better performance, demonstrating the utility of this manual design. Finally, the performance and benefits of sensor tying in the manual design are then validated in a physical experiment. Significance: The resulting proposed layout fulfills most requirements of an easy to use consumer EEG headset.

Brain-computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. In this study, we demonstrated an intracortical BCI that decodes attempted speaking movements from neural activity in motor cortex and translates it to text in real-time, using a recurrent neural network decoding approach. With this BCI, our study participant, who can no longer speak intelligibly due to amyotrophic lateral sclerosis, achieved a 9.1% word error rate on a 50-word vocabulary and a 23.8% word error rate on a 125,000-word vocabulary. This dataset contains all of the neural activity recorded during these experiments, consisting of 12,100 spoken sentences as well as instructed delay experiments designed to investigate the neural representation of orofacial movement and speech production. The data have also been formatted for developing and evaluating machine learning decoding methods, and we intend to host a decoding competition. To this end, the data also contain ..., , The data consists of .mat files that are intended to be loaded with MATLAB or Python (scipy.io.loadmat). See the individual readme.txt files for a detailed description of the data contents and format.  *** Note: Please use the latest release, which contains bug fixes to various aspects of the data. See the README.md file for notes as to what has changed in the latest release ***, # Data from: A high-performance speech neuroprosthesis

Brain-computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. In this study, we demonstrated an intracortical BCI that decodes attempted speaking movements from neural activity in motor cortex and translates it to text in real-time, using a recurrent neural network decoding approach. With this BCI, our study participant, who can no longer speak intelligibly due amyotrophic lateral sclerosis, achieved a 9.1% word error rate on a 50 word vocabulary and a 23.8% word error rate on a 125,000 word vocabulary. Neural activity was recorded with microelectrode arrays, and neural features are provided in the form of binned threshold crossings and spike band power (20 ms bins).

This dataset contains all of the neural activity recorded during these experiments, consisting of 12,100 spoken sentences as well as instructed delay experiments designed to investigate the neural representation ...

High-Density EEG Facilitates Detection of Small Stimuli in C-VEP BCIs, Data

The main objective of this audit is to improve the care of patients who undergo Percutaneous Coronary Intervention (PCI) procedures in the UK. The audit provides a mechanism to collect procedure specific data based on the minimum British Cardiovascular Interventional Society dataset. This audit project was delivered in collaboration with the British Cardiovascular Interventional Society. The audit described here allows clinicians to assess key aspects of the quality of their care when performing percutaneous coronary intervention (PCI). This is a United Kingdom wide audit performed by the Audit Lead of the British Cardiovascular Intervention Society (BCIS) with participation from hospitals performing PCI procedures.

Background: Increasing the communication speed of brain-computer interfaces (BCIs) is a major aim of current BCI-research. The idea to automatically detect error-related potentials (ErrPs) in order to veto erroneous decisions of a BCI has been existing for more than one decade, but this approach was so far little investigated in online mode. Methods: In our study with eleven participants, an ErrP detection mechanism was implemented in an electroencephalography (EEG) based gaze-independent visual speller. Results: Single-trial ErrPs were detected with a mean accuracy of 89.1% (AUC 0.90). The spelling speed was increased on average by 49.0% using ErrP detection. The improvement in spelling speed due to error detection was largest for participants with low spelling accuracy. Conclusion: The performance of BCIs can be increased by using an automatic error detection mechanism. The benefit for patients with motor disorders is potentially high since they often have rather low spelling accuracies compared to healthy people.