Brain computer interfaces
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.
The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.
The IEEE International Microwave Symposium (IMS) is the world s foremost conference covering the UHF, RF, wireless, microwave, millimeter-wave, terahertz, and optical frequencies; encompassing everything from basic technologies to components to systems including the latest RFIC, MIC, MEMS and filter technologies, advances in CAD, modeling, EM simulation and more. The IMS includes technical and interactive sessions, exhibits, student competitions, panels, workshops, tutorials, and networking events.
The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...
The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications.
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
2007 IEEE 9th Workshop on Multimedia Signal Processing, 2007
A brain-computer interface (BCI) is a communication system that translates brain activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain activity, without using peripheral nerves and muscles. The major goal of BCI research is to develop systems that allow disabled users to communicate with ...
2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2011
The use of brain-generated signals for human-robot interaction has gained increasing attention in the last years. Indeed brain-controlled robots can potentially be employed to substitute motor capabilities (e.g. brain- controlled prosthetics for amputees or patients with spinal cord injuries); to help in the restoration of such functions (e.g. as a tool for stroke rehabilitation) as well as non-clinical applications like ...
Proceedings of the IEEE, 2012
2009 International Conference on Adaptive and Intelligent Systems, 2009
In this presentation a look is taken at how the use of implant and electrode technology can be employed to create biological brains for robots, to enable human enhancement and to diminish the effects of certain neural illnesses. In all cases the end result is to increase the range of abilities of the recipients. An indication is given of a ...
IEEE Transactions on Rehabilitation Engineering, 2000
Current movement-based brain-computer interfaces (BCI's) utilize spontaneous electroencephalogram (EEG) rhythms associated with movement, such as the /spl mu/ rhythm, or responses time-locked to movements that are averaged across multiple trials, such as the readiness potential (RP), as control signals. In one study, the authors report that the /spl mu/ rhythm is not only modulated by the expression of self-generated movement ...
Brain Panel Introduction - Paul Sadja: 2016 Technology Time Machine
Q&A with Dr. Maryam Shanechi: IEEE Brain Podcast, Episode 6 Part 2
Brain Panelist - Jack Gallant: 2016 Technology Time Machine
Brain Panelist - James Kozloski: 2016 Technology Time Machine
Dr. Scott Fish
Q&A with Connor Russomanno: IEEE Digital Reality Podcast, Episode 1
Computational Intelligence for Brain Computer Interface
Miguel Nicolellis: Brain-Machine Interfaces: From Basic Science to Neurological Rehabilitation
Q&A with Dr. Al Emondi: IEEE Brain Podcast, Episode 13
Augmented Reality in Operating Rooms
Q&A with Cindy Chestek: IEEE Brain Podcast, Episode 12
Q&A with Dr. Maryam Shanechi: IEEE Brain Podcast, Episode 6 Part 1
Toward Cognitive Integration of Prosthetic Devices - IEEE WCCI 2014
Brain Inspired Computing Systems - Luping Shi: 2016 International Conference on Rebooting Computing
Signal Processing and Machine Learning
A Manhattan Project for the Prosthetic Arms Race
Towards a distributed mm-scale chronically-implantable neural interface - IEEE Brain Workshop
Q&A with Kip Ludwig: IEEE Brain Podcast, Episode 7
The EU Human Brain Project - A Systematic Path from Data to Synthesis
A brain-computer interface (BCI) is a communication system that translates brain activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain activity, without using peripheral nerves and muscles. The major goal of BCI research is to develop systems that allow disabled users to communicate with other persons, to control artificial limbs, or to control their environment. To achieve this goal, many aspects of BCI systems are currently being investigated. Research areas include evaluation of invasive and noninvasive technologies to measure brain activity, evaluation of control signals (i.e. patterns of brain activity that can be used for communication), development of algorithms for translation of brain signals into computer commands, and the development of new BCI applications. In this paper we give an overview of the aspects of BCI research mentioned above and highlight recent developments and open problems.
The use of brain-generated signals for human-robot interaction has gained increasing attention in the last years. Indeed brain-controlled robots can potentially be employed to substitute motor capabilities (e.g. brain- controlled prosthetics for amputees or patients with spinal cord injuries); to help in the restoration of such functions (e.g. as a tool for stroke rehabilitation) as well as non-clinical applications like telepresence or entertainment. This half-day tutorial gives an introduction to the field of brain-computer interfaces and presents several design principles required to successfully employ them for robot control.
In this presentation a look is taken at how the use of implant and electrode technology can be employed to create biological brains for robots, to enable human enhancement and to diminish the effects of certain neural illnesses. In all cases the end result is to increase the range of abilities of the recipients. An indication is given of a number of areas in which such technology has already had a profound effect, a key element being the need for a clear interface linking a biological brain directly with computer technology. The emphasis is clearly placed on practical scientific studies that have been and are being undertaken and reported on. The area of focus is notably the use of electrode technology, where a connection is made directly with the cerebral cortex and/or nervous system. The presentation will consider the future in which robots have biological, or part-biological, brains and in which neural implants link the human nervous system bi-directionally with technology and the Internet.
Current movement-based brain-computer interfaces (BCI's) utilize spontaneous electroencephalogram (EEG) rhythms associated with movement, such as the /spl mu/ rhythm, or responses time-locked to movements that are averaged across multiple trials, such as the readiness potential (RP), as control signals. In one study, the authors report that the /spl mu/ rhythm is not only modulated by the expression of self-generated movement but also by the observation and imagination of movement. In another study, the authors show that simultaneous self-generated multiple limb movements exhibit properties distinct from those of single limb movements. Identification and classification of these signals with pattern recognition techniques provides the basis for the development of a practical BCI.
Summary form only given, as follows. Neural networks have been intensively studied as a discipline in their own right in the last five years (late 1980s, early 1990s). Initial claims were extremely ambitious; by using the brain's computing principles, networks would eliminate programming, revolutionize computer architecture and sensor interfacing, make analog VLSI a reality, and give guidance to a new understanding of human cognition. Work in two areas is described: statistical methods to deal with classification, prediction, and control in data-rich, intuition-poor problems; and VLSI solutions, both in digital and analog styles, to accommodate these architectures.<<ETX>>
Virtual reality promisers to extend the realm of possible brain-computer interface (BCI) prototypes. Most of the work using electroencephalograph (EEG) signals in VR has focussed on brain-body actuated control, where biological signals from the body as well as the brain are used. The authors show that when subjects are allowed to move and act normally in an immersive virtual environment, cognitive evoked potential signals can still be obtained and used reliably. A single trial accuracy average of 85% for recognizing the differences between evoked potentials at red and yellow stop lights is presented and future directions discussed.
The ultimate goal of the authors' research is to utilize voluntary motor- related potentials recorded from the scalp in a direct Brain Computer Interface for asynchronous control applications. This type of interface will allow an individual with a high-level impairment to have effective and sophisticated control of devices such as wheelchairs, robotic assistive appliances, computers, and neural prostheses.
The rapidly emerging field of brain-machine interfaces (BMI) establishes a spectacular convergence between literature and neuroscience. But brain-machine interfaces emerge from two well-defined practical goals: creating more powerful computers and giving new hope to a broad segment of the disabled population. During the last century, the study of computers and of the brain have evolved in a reciprocal metaphor: the brain is investigated as an organ that processes information and computers have been developed starting from the dream of creating an artificial brain. Brain-machine interfaces provide us with the perspective of moving beyond metaphor and considering the possibility of accessing the computational power of neural tissue. Despite the speed with which today's computers execute billions of operations, our brains have still unsurpassed performance when it comes to recognizing a face or controlling the complex dynamics of the arm. The computational power of biological systems has sparked research aimed at mimicking neurobiological processes in artificial system. Perhaps the most well-known outcomes of this research are artificial neural-networks, which attempt to emulate some of the key features of neural information processing. More recently, a new idea has begun to take shape: the idea of constructing hybrid computers in which neurons are grown over a semiconductor substrate.
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Brain Computer Interfacing (BCI) aims at making use of brain signals for e.g. the control of objects, spelling, gaming and so on. This talk will first provide a very brief overview of Brain Computer Interface from a machine learning and signal processing perspective. In particular it shows the wealth, the complexity and the difficulties of the data available, a truely enormous challenge: In real-time a multi-variate very strongly noise contaminated data stream is to be processed and neuroelectric activities are to be accurately differentiated. Finally, I report in more detail about the Berlin Brain Computer (BBCI) Interface that is based on EEG signals and take the audience all the way from the measured signal, the preprocessing and filtering, the classification to the respective application. BCI as a new channel for man- machine communication is discussed in a clincial setting and for gaming.
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