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2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering
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2008, Journal of Neuroscience
2006, Neurosurgery
2009, Neurosurgical FOCUS
2011
2008, Journal of Neural Engineering
2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering
2009, Journal of Neural Engineering
2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering
2004, Journal of Neural Engineering
A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.
2004, Journal of Neural Engineering
Brain–Computer Interface (BCI) provides an alternate means of communication to the people who have lost control of their body due to some neuromuscular disorder and a new modality for control to healthy people. BCI detects changes in brain activity and encodes brain signals into commands to control an application of interest. Brain activity can be recorded in the form of electrophysiological signals, magnetic signals or metabolic signals. A BCI system detects, processes, identifies and classifies the brain signals and then translates these neural signals into commands. This paper discusses current state of art of BCI research, emphasises on its applications and considers various issues and challenges which make it difficult to move BCI technology from labs to home. Reference to this paper should be made as follows: Tyagi, A. and Nehra, V. (2013) 'Brain–computer interface: a thought translation device turning fantasy into reality', Int.
2010
Most Brain–Computer Interface (BCI) research aims at helping people who are severely paralyzed to regain control over their environment and to communicate with their social environment. There has been a tremendous increase in BCI research the last years, which might lead to the belief that we are close to a commercially available BCI applications to patients. However, studies with users from the future target group (those who are indeed paralyzed) are still outnumbered by studies on technical aspects of BCI applications and studies with healthy young participants. This might explain why the number of patients who use a BCI in daily life, without experts from a BCI group being present, can be counted on one hand.
2011, Journal of Neural Engineering
2012, Journal of Neural Engineering
2008, Journal of neuroscience methods
Interaction with machines is mediated by human–machine interfaces (HMIs). Brain–machine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle activity. In this context, low-performing interfaces can be considered as prosthetic applications. On the other hand, for able-bodied users, a BMI would only be practical if conceived as an augmenting interface. In this paper, a method is introduced for pointing out effective ...
2007, Journal of Neural Engineering
2013, Epilepsy & Behavior
For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world – a brain–computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and control technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or 'locked in', with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10–25 bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis control, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to control independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that control; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and control capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances. q
2008
This paper is aimed to introduce IDIAP Brain Computer In- terface (IBCI) research that successfully applied Ambience Intelligence (AmI) principles in designing intelligent brain-machine interactions. We proceed through IBCI applications describing how machines can decode and react to the human mental commands, cognitive and emotive states. We show how eective human-machine interaction for brain computer interfacing (BCI) can be achieved
2014, Frontiers in Integrative Neuroscience
2013, Bio-Algorithms and Med-Systems
Despite the quick development of medicine and associated medical technology, there are still many patients with very severe neurological deficits who need far more sophisticated solutions, such as brain computer interfaces (BCIs). Our research aims at becoming familiar with BCI technology and the assessment of the possibili-ties of selected subjects in the area of P300-based BCI. An indirect aim is a discussion on the procedures of patient preparation for BCI installation, possible threats and limi-tations. Our research presents one of the possible efforts towards better documentation of investigating neural cor-relates of brain processing.
This paper summarizes some of the basic questions on this Brain Computer Interface What types? What technologies? What are your applications? Is there a market? These issues are addressed in the first part. The synthesis of research on the original development since 1995 of the BrainGate Brain-Computer Interface is included in the second part. In May 2009, the FDA provided a new IDE for the BrainGate2 Neural Interface System pilot clinical trial. In the third part, this development is recorded. How close are we to controlling with our brain external objects or our own previously immobilized body? Not yet so close. By September 2021 it is planned to collect primary data.
advances.am.wroc.pl
A brain-computer interface (BCI) establishes a link between the human brain and the external devices. BCIs measure the brain activity for fetching the user’s intent and subsequently provide the control signals to the supporting hardware. This technology has varied uses ranging from assistive devices for disabled individuals to advanced simulator control. The main use of BCI is as an assistive technology for individuals suffering from loss of motor control caused by spinal cord injury, amyotrophic lateral sclerosis or any other possible incidence. BCIs take advantage of the brain’s electrochemical signals. There are billions of neurons in human brain with trillions of interconnections known as synapses. These devices also make use of neuroplasticity which is the brain’s ability to change physically and functionally over time. Author has discussed the basics of BCI in this paper and has presented details regarding brain waves, control centers of various organs in brain, invasive and non-invasive sensors. This paper also presents a summary of the research work going on in this area.
2011, Frontiers in Neuroscience
Brain–machine interfaces (BMIs) enable humans to interact with devices by modulating their brain signals. Despite impressive technological advancements , several obstacles remain. The most commonly used BMI control signals are derived from the brain areas involved in primary sensory-or motor-related processing. However, these signals only reflect a limited range of human intentions. Therefore, additional sources of brain activity for controlling BMIs need to be explored. In particular, higher-order cognitive brain signals, specifically those encoding goal-directed intentions, are natural candidates for enlarging the repertoire of BMI control signals and making them more efficient and intuitive. Thus, here, we identify the prefrontal brain area as a key target region for future BMIs, given its involvement in higher-order, goal-oriented cognitive processes. Brain–Machine Interfaces: An Overview
2013, International Journal of Computational Intelligence Studies
2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering
Brain computer interface technology represents a highly growing field of research with application systems. Its contributions in medical fields range from prevention to neuronal rehabilitation for serious injuries. Mind reading and remote communication have their unique fingerprint in numerous fields such as educational, self-regulation, production, marketing, security as well as games and entertainment. It creates a mutual understanding between users and the surrounding systems. This paper shows the application areas that could benefit from brain waves in facilitating or achieving their goals. We also discuss major usability and technical challenges that face brain signals utilization in various components of BCI system. Different solutions that aim to limit and decrease their effects have also been reviewed.
2014, Brain-Computer Interfaces
2013, IRBM
2010, Human-Computer Interaction Series
2000, IEEE Transactions on Biomedical Engineering
2008, International Journal of Pattern Recognition and Artificial Intelligence