Brain Machine Interface
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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
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)
The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.
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 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.
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
One of the flagship conferences for the IEEE Robotics and Automation Society (RAS)
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.
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.
Both general and technical articles on current technologies and methods used in biomedical and clinical engineering; societal implications of medical technologies; current news items; book reviews; patent descriptions; and correspondence. Special interest departments, students, law, clinical engineering, ethics, new products, society news, historical features and government.
2018 6th International Conference on Brain-Computer Interface (BCI), 2018
While brain-machine interfaces (BMIs) strive to provide neural prosthetic solutions to people with sensory, motor and cognitive disabilities, they have been typically tested in strictly controlled laboratory settings. Making BMIs versatile and applicable to real life situations is a significant challenge. For example, in real life we can flexibly and independently control multiple behavioral variables, such as programming motor goals, ...
IEEE Transactions on Emerging Topics in Computational Intelligence, 2018
Modern cars can support their drivers by assessing and autonomously performing different driving maneuvers based on information gathered by in-car sensors. We propose that brain- machine interfaces (BMIs) can provide complementary information that can ease the interaction with intelligent cars in order to enhance the driving experience. In our approach, the human remains in control, while a BMI is used ...
2017 IEEE 12th International Conference on ASIC (ASICON), 2017
Brain-machine interface (BMI) is one of the most important tools in the neuroscience research and neuroprosthetics development. The investigation and development of BMI have achieved significant progress in the past decade. However, several bottlenecks from the electrical engineering perspective still have to be overcome. The next generation BMI system would feature bi- directional neural interface with on-chip neural feature extraction ...
2013 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-BIO), 2013
We analyze the power and voltage transfer in a wireless link from an on-body transmit antenna to 1×1×1 mm3antenna in a cortical implant to provide power and data telemetry for a battery-free brain-machine interface microelectronic system. We compare the wireless link performance with regular, segmented, and tilted transmit loop antennas. Moreover, we analyze the performance improvement achieved by inserting a ...
2016 32nd Southern Biomedical Engineering Conference (SBEC), 2016
Increasingly accurate control of prosthetic limbs has been made possible by a series of advancements in brain machine interface (BMI) control theory. One promising control technique for future BMI applications is reinforcement learning (RL). RL based BMIs require a reinforcing signal to inform the controller whether or not a given movement was intended by the user. This signal has been ...
Signal Processing and Machine Learning
IEEE 125th Anniversary Media Event: Brain-Machine Interface Technology
Brain Panelist - Jack Gallant: 2016 Technology Time Machine
EMBC '09 - Technology's Role in Understanding and Treating Conditions of the Brain.
The role of robotics and neuroprosthetics in neurorehabilitation: novel HMI technologies - IEEE Brain Workshop
Computational Intelligence for Brain Computer Interface
Feeding the Machine: The World's Most Sophisticated Artificial Stomach
Q&A with Dr. Maryam Shanechi: IEEE Brain Podcast, Episode 6 Part 2
Toward Cognitive Integration of Prosthetic Devices - IEEE WCCI 2014
Towards a distributed mm-scale chronically-implantable neural interface - IEEE Brain Workshop
A Manhattan Project for the Prosthetic Arms Race
Dean Kamen's Artificial Arm
Multimodal Telepresent Control of DLR Rollin' JUSTIN
Life Sciences Grand Challenge Conference - Shangkai Gao
Brain Panel Introduction - Paul Sadja: 2016 Technology Time Machine
Tapping the Computing Power of the Unconscious Brain
Q&A with Dr. Maryam Shanechi: IEEE Brain Podcast, Episode 6 Part 1
Surgeons Got Game
Brain Panelist - James Kozloski: 2016 Technology Time Machine
While brain-machine interfaces (BMIs) strive to provide neural prosthetic solutions to people with sensory, motor and cognitive disabilities, they have been typically tested in strictly controlled laboratory settings. Making BMIs versatile and applicable to real life situations is a significant challenge. For example, in real life we can flexibly and independently control multiple behavioral variables, such as programming motor goals, orienting attention in space, fixating objects with the eyes, and remembering relevant information. Several neurophysiological experiments, conducted in monkeys, manipulated multiple behavioral variables in a controlled way; these multiple variables were decoded from the activity of same neuronal ensembles. Additionally, in the other monkey experiments, multiple motor variables were extracted from cortical ensembles in real time, such as controlling two virtual arms using a BMI. The next improvement has been achieved using brain-machine-brain interfaces (BMBIs) that simultaneously extract motor intentions from brain activity and generate artificial sensations using intracortical microstimulation (ICMS). For example, a BMBI can perform active tactile exploration of virtual objects. Such versatile BMIs bring us closer to the development of clinical neural prostheses for restoration and rehabilitation of neural function.
Modern cars can support their drivers by assessing and autonomously performing different driving maneuvers based on information gathered by in-car sensors. We propose that brain- machine interfaces (BMIs) can provide complementary information that can ease the interaction with intelligent cars in order to enhance the driving experience. In our approach, the human remains in control, while a BMI is used to monitor the driver's cognitive state and use that information to modulate the assistance provided by the intelligent car. In this paper, we gather our proof-of-concept studies demonstrating the feasibility of decoding electroencephalography correlates of upcoming actions and those reflecting whether the decisions of driving assistant systems are in-line with the drivers' intentions. Experimental results while driving both simulated and real cars consistently showed neural signatures of anticipation, movement preparation, and error processing. Remarkably, despite the increased noise inherent to real scenarios, these signals can be decoded on a single- trial basis, reflecting some of the cognitive processes that take place while driving. However, moderate decoding performance compared to the controlled experimental BMI paradigms indicate there exists room for improvement of the machine learning methods typically used in the state-of-the-art BMIs. We foresee that neural fusion correlates with information extracted from other physiological measures, e.g., eye movements or electromyography as well as contextual information gathered by in-car sensors will allow intelligent cars to provide timely and tailored assistance only if it is required; thus, keeping the user in the loop and allowing him to fully enjoy the driving experience.
Brain-machine interface (BMI) is one of the most important tools in the neuroscience research and neuroprosthetics development. The investigation and development of BMI have achieved significant progress in the past decade. However, several bottlenecks from the electrical engineering perspective still have to be overcome. The next generation BMI system would feature bi- directional neural interface with on-chip neural feature extraction and machine learning. Moreover, the high integration, compact packing and wireless operation would allow the experiments in freely behaving animals. This paper reviews the state-of-the-art designs, summarizes the key design requirements and challenges in a BMI system, and provides insights in both circuit and system level design.
We analyze the power and voltage transfer in a wireless link from an on-body transmit antenna to 1×1×1 mm3antenna in a cortical implant to provide power and data telemetry for a battery-free brain-machine interface microelectronic system. We compare the wireless link performance with regular, segmented, and tilted transmit loop antennas. Moreover, we analyze the performance improvement achieved by inserting a magneto-dielectric core in the implant antenna. We also attest the simulation model through measurements in a liquid head phantom.
Increasingly accurate control of prosthetic limbs has been made possible by a series of advancements in brain machine interface (BMI) control theory. One promising control technique for future BMI applications is reinforcement learning (RL). RL based BMIs require a reinforcing signal to inform the controller whether or not a given movement was intended by the user. This signal has been shown to exist in cortical structures simultaneously used for BMI control. This work evaluates the ability of several common classifiers to detect impending reward delivery within primary somatosensory (S1) cortex during a grip force match to sample task performed by a nonhuman primate. The accuracy of these classifiers was further evaluated over a range of conditions to identify parameters that provide maximum classification accuracy. S1 cortex was found to provide highly accurate classification of the reinforcement signal across many classifiers and a wide variety of data input parameters. The classification accuracy in S1 cortex between rewarding and non-rewarding trials was apparent when the animal was expecting an impending delivery or an impending withholding of reward following trial completion. The high accuracy of classification in S1 cortex can be used to adapt an RL based BMI towards a user's intent. Real-time implementation of these classifiers in an RL based BMI could be used to adapt control of a prosthesis dynamically to match the intent of its user.
Brain machine interfacing (BMI) devices have become popular in resent years because of their inexpensive tool and are comfortable to use due to which the BMI could reach out the research world and enter into the outer world including entertainment. In this paper we had put forward the concept of brain machine interface (BMI) system for gaming application both in real and virtual world. The proposed system have the ability to control gaming applications by means of neuro signals acquired from human brain using commercially available wireless EEG headset. The signal acquisition seizes the neural activity of brain which is then preprocessed to boost up the SNR (signal to noise ratio). The preprocessed signal is then feature extracted to pick the necessary information and finally the classification stage switch the picked features into control commands and relays them to gaming application.
Brain-Machine-Interface (BMI) will transfer the neural activities of brain to machine for potential actions. Antenna for Brain-Machine-Interface needs to be very small, due to limited available space in human head. Ultra Wideband (UWB) technology with their low cost, low power consumption, high data rate and small form factor of antenna is one of the strong candidate for medical implanted devices. Possibility to have a miniature antenna and as a result small implanted device, put the UWB system on the top for the Brain-Machine- Interface application. This paper presents the design of a very small UWB antenna which is placed in the skull bone and covered with head tissues, i.e. fat and skin. Therefore, antenna needs to be biocompatible, hence will not be rejected by the tissue surrounding. Tissue electrical properties around the implanted antenna has significant effect on the antenna performance, thus, electrical properties of tissues were considered on the time of antenna design. There is no doubt that, in the very nearest future, the BMI could be very helpful for people with neurological problems and difficulties. The aim of this design is to provide a fitting and handy BMI implant antenna in term of size and placement at UWB frequency band.
The increasing number of signal processing tools for highly parallel neurophysiological recordings opens up new avenues for connecting technologies directly to neuronal processes. As the understanding is taking a better shape, lot more work to perform is coming up. A simple brain-machine interface may be able to reestablish the broken loop of the persons with motor dysfunction. With time the brain-machine interfacing is growing more complex due to the increased availability of instruments and processes for implementation. In this work, as a proof-of-principle we established a brain-machine interface through a few simple processes to control a robotic device using the alpha wavepsilas event-related synchronization and event-related de-synchronization extracted from EEG.
Neural interfaces are one of the most exciting emerging technologies to impact bioengineering and neuroscience because they enable an alternate communication channel linking directly the nervous system with man-made devices. This book reveals the essential engineering principles and signal processing tools for deriving control commands from bioelectric signals in large ensembles of neurons. The topics featured include analysis techniques for determining neural representation, modeling in motor systems, computing with neural spikes, and hardware implementation of neural interfaces. Beginning with an exploration of the historical developments that have led to the decoding of information from neural interfaces, this book compares the theory and performance of new neural engineering approaches for BMIs. Contents: Introduction to Neural Interfaces / Foundations of Neuronal Representations / Input-Outpur BMI Models / Regularization Techniques for BMI Models / Neural Decoding Using Generative BMI Models / Adaptive Algorithms for Point Processes / BMI Systems
Silent speech generation is an intelligent idea that can possibly assist physically challenged people who cannot convey their information as an acoustic signal. Silent speech is generated by predicting the intended speech information which occurs as a result of neural activity involved in the process of speech production. The acquired speech is synthesized and given as a feedback to the user acoustically with the delay of 50ms. This paper briefly elucidate the process of acquiring neural signal, preprocessing and feature extracting for the production of speech signal by means of Brain Machine Interface.
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