Conferences related to Brain Machine Interfaces

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

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.

  • 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI)

    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 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 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.

  • 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

    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 2018 will be the 15th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2018 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

    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 2017 will be the 14th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2017 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forumfor the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2016 willbe the thirteenth meeting in this series. The previous meetings have played a leading role in facilitatinginteraction between researchers in medical and biological imaging. The 2016 meeting will continue thistradition of fostering crossfertilization among different imaging communities and contributing to an integrativeapproach to biomedical imaging across all scales of observation.

  • 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015)

    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 2015 will be the 12th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014)

    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 2014 will be the eleventh meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013)

    To serve the biological, biomedical, bioengineering, bioimaging and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2011 IEEE 8th International Symposium on Biomedical Imaging (ISBI 2011)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2010 IEEE 7th International Symposium on Biomedical Imaging (ISBI 2010)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2009 IEEE 6th International Symposium on Biomedical Imaging (ISBI 2009)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2008 IEEE 5th International Symposium on Biomedical Imaging (ISBI 2008)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2007 IEEE 4th International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2007)

  • 2006 IEEE 3rd International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2006)

  • 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2004)

  • 2002 1st IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2002)


2020 IEEE International Conference on Robotics and Automation (ICRA)

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.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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)


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Periodicals related to Brain Machine Interfaces

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Biomedical Circuits and Systems, IEEE Transactions on

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 ...


Biomedical Engineering, IEEE Reviews in

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.


Biomedical Engineering, IEEE Transactions on

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

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.


Engineering in Medicine and Biology Magazine, IEEE

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.


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Most published Xplore authors for Brain Machine Interfaces

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Xplore Articles related to Brain Machine Interfaces

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A simulation study on decoding algorithms for brain-machine interfaces with the non-stationary neuronal ensemble activity

2016 16th International Conference on Control, Automation and Systems (ICCAS), 2016

Intracortical brain-machine interfaces (BMIs) aim to provide motor functions via high-performance neural prosthetics to patients with tetraplegia. BMI simulation can be useful in certain circumstances to evaluate functional environments of neuronal conditions, avoiding clinical issues such as infection or tissue damage. In this study, we performed a stimulation study on an intracortical BMI for the reconstruction of three dimensional arm ...


Brain - Machine Interfaces in the Context of Artificial Intelligence Development

2018 14th Symposium on Neural Networks and Applications (NEUREL), 2018

This paper is focused on the impact of the current technology's quick development over the human society in the near future. The authors attempt to extrapolate the existing tendencies in order to understand the future development and the most probable changes in the relation between the new intelligent machines and the humans. Special attention is given to the direct Brain ...


Selection of the best mental tasks for a SVM-based BCI system

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014

In this work, a study that analyzes the best combinations of mental tasks in a Brain-Computer Interface (BCI) using a classifier based on Support Vector Machine (SVM) is presented. To that end, 12 mental tasks of different nature are analyzed and the results of the classification for the combinations of two, three and four tasks are obtained. Four volunteers performed ...


Clinical ethical concerns in the implantation of brain-machine interfaces: Part II: Specific clinical and technical issues affecting ethical soundness

IEEE Pulse, 2013

In our article, "Clinical Ethical Concerns in the Implantation of Brain- Machine Interfaces: Part I," published in the January/February issue of IEEE Pulse [1], we suggested that implantable brain-machine interfaces (BMIs) are ethically unsound in all but a handful of rare cases. This argument hinges on the invasiveness of the implantation surgery and the existence of effective noninvasive alternatives for ...


Clinical Application of Implantable Brain Machine Interfaces

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018

Implantable brain machine interfaces (BMI) enable severely disabled people high-performance real-time robot control and communication, utilizing high- quality intracranial neural signals. Electrocorticograms (ECoG) are useful for implantable BMIs because of not only their zero time-lag property but their high spatiotemporal resolution with long term stability also. Fully implantable devices for ECoG recording offer long-term home-use with 24/7 supports. This will ...


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Educational Resources on Brain Machine Interfaces

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IEEE-USA E-Books

  • A simulation study on decoding algorithms for brain-machine interfaces with the non-stationary neuronal ensemble activity

    Intracortical brain-machine interfaces (BMIs) aim to provide motor functions via high-performance neural prosthetics to patients with tetraplegia. BMI simulation can be useful in certain circumstances to evaluate functional environments of neuronal conditions, avoiding clinical issues such as infection or tissue damage. In this study, we performed a stimulation study on an intracortical BMI for the reconstruction of three dimensional arm movements. We compared two key decoding algorithms, the Kalman filter (KF) and the optimal linear estimator (OLE), when neuronal preferred directions (PDs) varied over time. The simulation results revealed that the KF decoded the movement direction better than the OLE for non-stationary neuronal ensemble activity. These results may provide a guidance for selecting an appropriate decoding algorithm for various environments of intracortical BMIs.

  • Brain - Machine Interfaces in the Context of Artificial Intelligence Development

    This paper is focused on the impact of the current technology's quick development over the human society in the near future. The authors attempt to extrapolate the existing tendencies in order to understand the future development and the most probable changes in the relation between the new intelligent machines and the humans. Special attention is given to the direct Brain - Machine Interfaces, a possible solution to make man compatible with his future AI environment.

  • Selection of the best mental tasks for a SVM-based BCI system

    In this work, a study that analyzes the best combinations of mental tasks in a Brain-Computer Interface (BCI) using a classifier based on Support Vector Machine (SVM) is presented. To that end, 12 mental tasks of different nature are analyzed and the results of the classification for the combinations of two, three and four tasks are obtained. Four volunteers performed registers of the 12 tasks. The main goal is to find the combination of more than three mental tasks that obtains the highest reliability to apply it in future complex applications that require the use of more than three control commands. After a selection procedure, the results obtained show higher success rates. Using the information provided by every single electrode, an average of 87.10% is obtained as success rate for the classification of two mental tasks, 65.67% for three mental tasks and 50.76% for four mental tasks. Moreover combinations of the best electrodes are studied, improving the accuracy of the system. Using the best five electrodes, averages of 91.42%, 72.89% and 59.75% are obtained classifying two, three and four mental tasks respectively. These results suggest that it is possible to differentiate with enough reliability between more than three mental tasks using the methodology proposed.

  • Clinical ethical concerns in the implantation of brain-machine interfaces: Part II: Specific clinical and technical issues affecting ethical soundness

    In our article, "Clinical Ethical Concerns in the Implantation of Brain- Machine Interfaces: Part I," published in the January/February issue of IEEE Pulse [1], we suggested that implantable brain-machine interfaces (BMIs) are ethically unsound in all but a handful of rare cases. This argument hinges on the invasiveness of the implantation surgery and the existence of effective noninvasive alternatives for most patients. In this article, we seek to prove this assertion by discussing complications that may invalidate the device and/or require additional surgery, and we present suggestions for how implantable BMIs can be made more ethical in the future.

  • Clinical Application of Implantable Brain Machine Interfaces

    Implantable brain machine interfaces (BMI) enable severely disabled people high-performance real-time robot control and communication, utilizing high- quality intracranial neural signals. Electrocorticograms (ECoG) are useful for implantable BMIs because of not only their zero time-lag property but their high spatiotemporal resolution with long term stability also. Fully implantable devices for ECoG recording offer long-term home-use with 24/7 supports. This will help not only patients with restoring motor and communication control but also help their caregivers with reducing burdens of caregiving day and night. Until now, we established ECoG-based robot control and communication. High gamma activity (80-150 Hz) was a good decoding feature for ECoG-based real time decoding and control. Independent component analyses effectively extract neural information with dimensional reduction and contribute to improving decoding accuracy. Also, we are developing a 128-channel fully-implantable BMI device (WHERBS) for long-term home-use with 24/7 supports. We completed GLP tests and non-clinical long-term implantation. The next step is a clinical trial to confirm safety and efficacy of the implantable BMI.

  • A 128 channel 290 GMACs/W machine learning based co-processor for intention decoding in brain machine interfaces

    A machine learning co-processor in 0.35μm CMOS for motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm, time delayed sample based feature dimension enhancement, low-power analog processing and massive parallelism, it achieves an energy efficiency of 290 GMACs/W at a classification rate of 50 Hz. A portable external unit based on the proposed co-processor is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99.3%. With time-delayed feature dimension enhancement, the classification accuracy can be increased by 5% with limited number of input channels.

  • Design of an implantable intracortical microelectrode system for brain-machine interfaces

    The long-term goal in the design of Brain-Machine Interfaces is to restore communication and control to unrestrained individuals. One of the great challenges in this effort is to develop implantable systems that are capable of processing the activity of large ensembles of cortical neurons. Here, we present the design, fabrication, and testing of a flexible microelectrode array that can be hybrid-packaged with custom electronics in a fully implantable form factor. The design specifications and process flow for incorporating flip-chip bonding of an amplifier die are discussed.

  • Spike Rate Estimation Using Bayesian Adaptive Kernel Smoother (BAKS) and Its Application to Brain Machine Interfaces

    Brain Machine Interfaces (BMIs) mostly utilise spike rate as an input feature for decoding a desired motor output as it conveys a useful measure to the underlying neuronal activity. The spike rate is typically estimated by a using non-overlap binning method that yields a coarse estimate. There exist several methods that can produce a smooth estimate which could potentially improve the decoding performance. However, these methods are relatively computationally heavy for real-time BMIs. To address this issue, we propose a new method for estimating spike rate that is able to yield a smooth estimate and also amenable to real-time BMIs. The proposed method, referred to as Bayesian adaptive kernel smoother (BAKS), employs kernel smoothing technique that considers the bandwidth as a random variable with prior distribution which is adaptively updated through a Bayesian framework. With appropriate selection of prior distribution and kernel function, an analytical expression can be achieved for the kernel bandwidth. We apply BAKS and evaluate its impact on offline BMI decoding performance using Kalman filter. The results reveal that BAKS can improve the decoding performance compared to the binning method. This suggests the feasibility and the potential use of BAKS for real-time BMIs.

  • Latency Reduction during Telemetry Transmission in Brain-Machine Interfaces

    Advanced array processing techniques are becoming an indispensable requirement for integrating the rapid developments in wireless high-density electronic interfaces to the central nervous system with computational neuroscience. This work aims at describing a systems approach for latency reduction in telemetry- linked brain machine interfaces to enable real-time transmission of high volumes of neural data. We show that the tradeoff between transmission bit rate and processing complexity requires a smart processing mechanism to strip the redundancy and extract the useful information early in the data stream. The results presented demonstrate that space-time processing offers tremendous savings in communication costs compared to on-chip spike detection followed by off-chip classification. They also demonstrate that the performance asymptotically approaches that of on-chip spike detection and sorting. Detailed performance evaluation is described

  • Comparison of TDNN training algorithms in brain machine interfaces

    Linear or non-linear models are used in brain machine interfaces (BIMIs) to map the neural activity to the associated behavior, typically the primate's hand position. Linear models assume a linear relationship between neural activity and hand position that may not be the case. A solution would be time- delay neural network (TDNN) that provides effectively a nonlinear combination of linear models. However, this model results in a drastic increase of free parameters and slow convergence when trained by an error backpropagation learning rule. We propose to train the TDNN by scaled conjugate gradient, which avoids time-consuming linear search, coupled with weight decay to reduce the free parameters number and produce generally faster convergence.



Standards related to Brain Machine Interfaces

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Jobs related to Brain Machine Interfaces

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