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Principle of a Brain-Computer Interface (BCI) based on Evoked Related Potentials (ERPs): EEG signals are recorded (1) while a computer generates a series of stimulations that generates EEG features (2).
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Brain-Computer Interface (BCI) is a technology that translates the brain electrical activity into a command for a device such as a robotic arm, a wheelchair or a spelling device. BCIs have long been described as an assistive technology for severely disabled patients because they completely bypass the need for muscular activity. The clinical reality...
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... Interfaces (BCI) is an old technology [1] that translates the brain electrical activity into a command for a device [2]. As illustrated in Fig. 1, BCIs are usually characterized ...
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BCI (Brain Computer Interface) is technology to control external devices by measuring the brain activity, such as electroencephalogram (EEG), so that handicapped people communicate with environment physically using the technology. Among them, EEG is widely used in various fields, especially robot agent control by using several signal response chara...
The motor imagery (MI)-based brain-computer interface (BCI) is an intuitive interface that provides control over computer applications directly from brain activity. However, it has shown poor performance compared to other BCI systems such as P300 and SSVEP BCI. Thus, this study aimed to improve MI-BCI performance by training participants in MI with...
In recent years, research on Brain-Computer Interface (BCI) technology for healthy users has attracted considerable interest, and BCI games are especially popular. This study reviews the current status of, and describes future directions, in the field of BCI games. To this end, we conducted a literature search and found that BCI control paradigms u...
Calibration is still an important issue for user
experience in Brain-Computer Interfaces (BCI). Common experimental designs often involve a lengthy training period that
raises the cognitive fatigue, before even starting to use the BCI.
Reducing or suppressing this subject-dependent calibration is
possible by relying on advanced machine learning tec...
A Brain–Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have de...
Citations
... Assistive keyboards and related tools use additional methods to meet the needs of persons with physical disabilities. These methods include humming (Poláček et al., 2011) or a brain-computer interface (BCI), as discussed by Millán et al. (2010), Thompson et al. (2009), and Mayaud et al. (2013). Additionally, assistive keyboards were designed to suit people with a particular disability. ...
... The development of EEG sensing technology, including portability and autonomy of the amplification unit (e.g., http://openbci.com), the performance, comfort and usability of the electrode-cap (e.g.,[37][38][39]), new type of sensors (e.g.,[40]), on-line EEG signal quality monitoring[41,42], and all ergonomic matters such as accessibility, usability and acceptance of the technology[43]. ...
Despite its short history, the use of Riemannian geometry in brain-computer interface (BCI) decoding is currently attracting increasing attention, due to accumulating documentation of its simplicity, accuracy, robustness and transfer learning capabilities, including the winning score obtained in five recent international predictive modeling BCI data competitions. The Riemannian framework is sharp from a mathematical perspective, yet in practice it is simple, both algorithmically and computationally. This allows the conception of online decoding machines suiting real-world operation in adverse conditions. We provide here a review on the use of Riemannian geometry for BCI and a primer on the classification frameworks based on it. While the theoretical research on Riemannian geometry is technical, our aim here is to show the appeal of the framework on an intuitive geometrical ground. In particular, we provide a rationale for its robustness and transfer learning capabilities and we elucidate the link between a simple Riemannian classifier and a state-of-the-art spatial filtering approach. We conclude by reporting details on the construction of data points to be manipulated in the Riemannian framework in the context of BCI and by providing links to available open-source Matlab and Python code libraries for designing BCI decoders.
... This would certainly result in an improved quality of ICU stay. The Robust Brain-computer Interface for virtual Keyboard (RoBIK) project [40] aimed at the development of a BCI system for the communication of patients that could be used on a daily basis in the hospital with limited external intervention. In order to achieve this goal a multidisciplinary approach was chosen and developments were carefully framed by clinical specifications and validation, according to a user-centered design. ...
... This would certainly result in an improved quality of ICU stay. The Robust Brain-computer Interface for virtual Keyboard (RoBIK) project [40] aimed at the development of a BCI system for the communication of patients that could be used on a daily basis in the hospital with limited external intervention. In order to achieve this goal a multidisciplinary approach was chosen and developments were carefully framed by clinical specifications and validation, according to a user-centered design. ...
This study presents the outcome of the 5-year-long French national project aiming at the development and evaluation of an effective brain-computer interface (BCI) prototype for the communication of patients with acute motor disabilities. It presents results from two clinical studies: a clinical feasibility study carried out partly in the intensive care unit (ICU) and the clinical evaluation of an innovative BCI prototype. In this second study the BCI performance of patients was compared to that of healthy volunteers and benchmarked against a traditional assistive technology (scanning device). Altogether, 15 of 22 patients could control the BCI system with an accuracy significantly above the chance level. The bit-rate of the traditional assistive technology proved superior, even though an equivalent bit-rate could be achieved using personalized parameters for the BCI. Fatigue was found to be the primary limitation factor, which was particularly true for patients and during the use of the BCI. A classifier based on Riemannian geometry was found to contribute significantly to the accuracy of the BCI system. This study demonstrates that the communication of patients with severe motor impairments can be effectively restored using an adequately designed BCI system. All electrophysiological data are freely available at the Physionet.org platform.
... The choice of which approach to use is driven by the trade-off between performance and the risk associated with the BCI type. Typically the more invasive the BCI technique, the better the performance, but the higher the surgical risk [25]. ...
Natural user interfaces (NUIs) are alternatives to traditional input methods using different methods of interaction. The goal of NUIs is to provide a natural means of interaction that is effectively invisible to the user. Commercial Brain Computer Interfaces (BCIs) have the potential to meet this need. Towards this end, it was investigated whether a user's experience with traditional input methods affects their efficiency with the Emotiv (a commercially available BCI). This was used as an indicator of the suitability of the headset as a NUI. Two groups of participants, categorised based on their differing access to traditional input methods, were tested navigating a pair of robots through a test course, first with the Emotiv and then using a standard keyboard. The conclusion reached is that the Emotiv would be suitable to use as a NUI, but only when used in a non-critical context. The findings are that the technology is presently not yet ready to replace the traditional approach utilising mouse and keyboard. However, the Emotiv would be suitable for use as a NUI as long as the tasks are non-critical and are not time sensitive.
... Despite these drawbacks, complex BCI applications have been designed including virtual keyboards [5], quadcopter [6], wheelchairs [7] and neuroprostheses [8]. Some of these systems -relying on both neural evoked responses (e.g., P300based BCI) or modulation of sensory-motor rhythms (e.g., Motor imagery (MI)-based BCI)-have successfully been tested Copyright (c) 2013 IEEE. ...
One of the problems of non-invasive Brain- Computer Interface (BCI) applications is the occurrence of anomalous (unexpected) signals that might degrade BCI performance. This situation might slip the operator's attention since raw signals are not usually continuously visualized and monitored during BCI-actuated device operation. Anomalous data can for instance be the result of electrode misplacement, degrading impedance or loss of connectivity. Since this problem can develop at run-time, there is a need of a systematic approach to evaluate electrode reliability during online BCI operation. In this paper, we propose two metrics detecting how much each channel is deviating from its expected behavior. This quantifies electrode reliability at run-time which could be embedded into BCI data processing to increase performance. We assess the effectiveness of these metrics in quantifying signal degradation by conducting three experiments: electrode swap, electrode manipulation and offline artificially degradation of P300 signals.
... Qualitatively, results were similar when the signal processing approach was improved [99]. (3) Web browser -for example, (i) the Virtual Keyboard (RoBIK) project, which employs a matrix-speller paradigm to provide the user with different tags which are mapped to elements of the web browser [170]; and (ii) a system that employs a matrix speller paradigm to allow complete keyboard and mouse control to navigate through web browser options [23]. ...
A condição de vida da pessoa com tetraplegia lhe impõe desafios para prosseguir após a lesão medular ou doença limitante, pois se tornam dependentes devido a pouca ou nenhuma condição motora dos membros inferiores e superiores. Essas pessoas podem ser produtivas ou terem uma qualidade de vida melhor quando por meio de recursos tecnológicos. Diante do exposto, este trabalho objetiva identificar quais tecnologias têm sido aplicadas no processo de reabilitação do indivíduo tetraplégico através de uma revisão sistemática de literatura. O procedimento a ser realizado foi dividido em quatro etapas: planejamento, avaliação, execução e relatório. A busca foi realizada nas bases LILACS, Science Direct, DBL e IEEE Xplore utilizando strings de busca definidas. Por meio deste trabalho, conseguiu-se verificar as principais tecnologias que estão sendo desenvolvidas para auxiliar na reabilitação e/ou na melhoria de vida de pessoas tetraplégicas, auxiliando na realização de tarefas do cotidiano.
The brain-computer interface (BCI) is a multidisciplinary research field aimed at helping people with neuromotor disabilities. A BCI system enables the control of mechatronic devices by using cognitive intentions translated into electroencephalographic signals. This paper presents the implementation of LabVIEW-based display systems that can be controlled by a brain-computer interface based on detecting the voluntary eye blinks used as commands. The interactive virtual or physical display systems are helpful thanks to running or simulating various graphical animations or transmitting different text messages in a user-customizable way. The proposed LabVIEW-based virtual display systems provide versatile functionalities such as: customizing the own visual animations and the movement of any text message by switching the direction (to the left or to the right) depending on the user's choice. This paper presents five original virtual LEDs based display systems developed in LabVIEW graphical programming environment. The implemented LabVIEW applications included: an 8x8 LEDs matrix for simulating graphical animations, 2x16 LCD TEXT for showing text messages, and a 7-segments display for implementing chronometer functionality. Moreover, the LabVIEW virtual display systems were interfaced with the physical display systems (8x8 LEDs matrix controlled by MAX7219 driver and 2x16 LCD TEXT) connected to the Arduino Uno board.
