65 resources related to Integrative Neuroscience
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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.
2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)
The 27th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2018, will be held in Nanjing and Tai'an, China, from 27 to 31 of August 2018. This leading forum covers state-of-the-art innovative results, the latest developments cover a wide range of topics related to Robot and Human Interactive Communication.The theme of the conference is social intelligence in interactive robots. Original contributions including basic findings, multi-disciplinary approaches towards friendly, open and useful robots in practical/real life applications such as healthcare, industry edutainment, etc., are highly encouraged.
The series of BIBE Conferences was initiated in 2000 and is the first of its kind in IEEE inspiringothers to follow its path. The 18th annual IEEE International Conference on Bioinformatics andBioengineering aims at building synergy between Bioinformatics and Bioengineering, twocomplementary disciplines that hold great promise for the advancement of research anddevelopment in complex medical and biological systems, agriculture, environment, publichealth, drug design. Research and development in these two areas are impacting the scienceand technology in fields such as medicine, food production, forensics, etc. by advancingfundamental concepts in molecular biology, by helping us understand living organisms atmultiple levels, by developing innovative implants and bio-prosthetics, and by improving toolsand techniques for the detection, prevention and treatment of diseases.
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.
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.
Imaging methods applied to living organisms with emphasis on innovative approaches that use emerging technologies supported by rigorous physical and mathematical analysis and quantitative evaluation of performance.
Devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware. Emphasis is on artificial neural networks.
Rehabilitation aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation, and hardware and software applications for rehabilitation engineering and assistive devices.
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017
This paper describes the design of a FES system automatically controlled in a closed loop using a Microsoft Kinect sensor, for assisting both cylindrical grasping and hand opening. The feasibility of the system was evaluated in real-time in stroke patients with hand function deficits. A hand function exercise was designed in which the subjects performed an arm and hand exercise ...
2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), 2015
After a general consideration of the various approaches to electrical stimulation of the retina, a thorough in vitro investigation of retinal responses to voltage-controlled stimuli is discussed within the context of the Alpha IMS subretinal implant (Retina Implant AG, Reutlingen, Germany). This is supplemented by a clinical trial interim report describing results obtained in 29 patients blind from retinitis pigmentosa ...
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
Existing methods for withdrawal reflex detection from surface electromyography (sEMG) do not consider the potential presence of electrical crosstalk, which in practical applications may entail reduced detection accuracy. This study estimated muscle fiber conduction velocities (CV) for the tibialis anterior (TA) and soleus (SOL) muscles of both genuine reflexes and identified crosstalk, measured during antagonistic reflex responses. These estimations were ...
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014
The brain is a massively interconnected network of specialized circuits. Even primary sensory areas, once thought to support relatively simple, feed-forward processing, are now known to be parts of complex feedback circuits. All brain functions depend on millisecond timescale interactions across these brain networks. Current approaches cannot measure or manipulate such large-scale interactions. Here we demonstrate that polymer-based, penetrating, micro- ...
2009 9th IEEE-RAS International Conference on Humanoid Robots, 2009
To recognize how people interact with objects is essential for humans and artificial systems like robots. However, this recognition task is difficult and requires the capturing of the details of effector and goal object under a wide range of image transformations, such as view or position changes. Here, we demonstrate how specific effector-object interactions can be efficiently recognized by a ...
ICASSP 2012 Plenary-Dr. Mitsuo Kawato
Sharing New Breakthroughs in Neuroscience
Robotics History: Narratives and Networks Oral Histories:Michael Arbib
Laura Specker Sullivan: Neuroscience & Brain Panel - Forecasting the Future by Looking at the Past - TTM 2018
Life Sciences Grand Challenge Conference - Phillip A. Sharp
Q&A: Neuroscience and Brain Panel - TTM 2018
Q&A with Jack Gallant: IEEE Brain Podcast, Episode 11
Q&A with Emery Brown: IEEE Brain Podcast, Episode 3
Engineering Our Future - Maja Mataric, Ph.D
Q&A with Dr. Al Emondi: IEEE Brain Podcast, Episode 13
A Conversation with Danielle Bassett: IEEE TechEthics Interview
Christoph Guger: Neuroscience & Brain Panel - The Future of Non-invasive Brain-computer Interfaces - TTM 2018
The EU Human Brain Project - A Systematic Path from Data to Synthesis
Keynote: Poppy Crum - TTM 2018
Q&A with Dr. Jacob Robinson: IEEE Brain Podcast, Episode 5
Emerging Technologies for the Control of Human Brain Dynamics: IEEE TechEthics Keynote with Danielle Bassett
Brain Panelist - James Kozloski: 2016 Technology Time Machine
CB: Exploring Neuroscience with a Humanoid Research Platform
Solving Real World Problems with Computing: Exploring Careers in Engineering and Technology
This paper describes the design of a FES system automatically controlled in a closed loop using a Microsoft Kinect sensor, for assisting both cylindrical grasping and hand opening. The feasibility of the system was evaluated in real-time in stroke patients with hand function deficits. A hand function exercise was designed in which the subjects performed an arm and hand exercise in sitting position. The subject had to grasp one of two differently sized cylindrical objects and move it forward or backwards in the sagittal plane. This exercise was performed with each cylinder with and without FES support. Results showed that the stroke patients were able to perform up to 29% more successful grasps when they were assisted by FES. Moreover, the hand grasp- and-hold and hold-and-release durations were shorter for the smaller of the two cylinders. FES was appropriately timed in more than 95% of all trials indicating successful closed loop FES control. Future studies should incorporate options for assisting forward reaching in order to target a larger group of stroke patients.
After a general consideration of the various approaches to electrical stimulation of the retina, a thorough in vitro investigation of retinal responses to voltage-controlled stimuli is discussed within the context of the Alpha IMS subretinal implant (Retina Implant AG, Reutlingen, Germany). This is supplemented by a clinical trial interim report describing results obtained in 29 patients blind from retinitis pigmentosa who have received the Alpha IMS implant. It is concluded that the surgical procedure is safe and blind patients can benefit in visual tasks of daily life with this device that has meanwhile received approval for commercial use in Europe.
Existing methods for withdrawal reflex detection from surface electromyography (sEMG) do not consider the potential presence of electrical crosstalk, which in practical applications may entail reduced detection accuracy. This study estimated muscle fiber conduction velocities (CV) for the tibialis anterior (TA) and soleus (SOL) muscles of both genuine reflexes and identified crosstalk, measured during antagonistic reflex responses. These estimations were used to develop and assess a novel method for reflex detection resistant to crosstalk. Cross correlations of two single differential (SD) sEMG signals recorded along the muscle fibers were performed and two features were extracted from the resulting correlograms (average CV and maximal cross correlation). Reflex detection based on evaluation of the extracted features was compared to a conventional reflex detection method (thresholding of interval peak z-scores), applied on both SD and double differential (DD) sEMG. Intramuscular electromyography (iEMG) was used as validation for reflex detection. Apparent CV due to electrical crosstalk alone were more than one order of magnitude higher than CV estimated for genuine reflexes. Conventional reflex detection showed excellent sensitivity but poor specificity (0.19-0.76) due to the presence of crosstalk. In contrast, cross correlation analysis allowed reflex detection with significantly improved specificity (0.91-0.97). The developed methodology may be readily implemented for more reliable reflex detection.
The brain is a massively interconnected network of specialized circuits. Even primary sensory areas, once thought to support relatively simple, feed-forward processing, are now known to be parts of complex feedback circuits. All brain functions depend on millisecond timescale interactions across these brain networks. Current approaches cannot measure or manipulate such large-scale interactions. Here we demonstrate that polymer-based, penetrating, micro- electrode arrays can provide high quality neural recordings from awake, behaving animals over periods of months. Our results indicate that polymer electrodes are a viable substrate for the development of systems that can record from thousands of channels across months to years. This is our first step towards developing a 1000+ electrode system capable of providing high- quality, long-term neural recordings.
To recognize how people interact with objects is essential for humans and artificial systems like robots. However, this recognition task is difficult and requires the capturing of the details of effector and goal object under a wide range of image transformations, such as view or position changes. Here, we demonstrate how specific effector-object interactions can be efficiently recognized by a simple, biologically plausible neural model. In line with biological evidence, the model applies a view-based approach for the recognition of grasping sequences from videos. The model generalizes to untrained views by interpolation between stored example views. In addition, it presents a novel physiologically plausible mechanism to capture the spatial relationship between effector and object. The results support the view that where and how an object will be grasped by an agent can be predicted without estimation of the 3D structure of the scene.
Despite considerable advances in the field of retinal prosthetics during recent years, significant variability remains in the quality of vision restoration for patients. One target for refinement of prosthetic vision is to selectively activate one or more of the ~20 parallel channels of visual information that are established in the retina and subsequently travel to different visual networks in the brain. These different channels result in different spike train response patterns in the retinal ganglion cells (RGCs) which constitute the sole output neuron population of the retina. Here we demonstrate, however, that the genuine visual response patterns of retinal ganglion cells can be altered by electrical stimulation, suggesting that the encoding of visual stimuli by retinal prosthesis devices may require consideration of stimulation-induced changes in the retina. Specifically, we demonstrate that ON and OFF response amplitudes increase significantly after stimulation. This leads to changes in the relative weighting of ON and OFF response types on a cell by cell basis - fundamentally altering the visual stimulus encoded by some RGCs.
In this paper, the feasibility of using wearable wireless low-cost motion trackers equipped with magnetic/inertial sensors in daily human activities was investigated. Five previously introduced attitude estimation algorithms were chosen and implemented in Windows IoT environment. A Texas Instrument CC2650STK wireless sensor was utilized to capture the raw magnetic/inertial data. The raw data was streamed to a DragonBoard 410c single board computer in real-time via Bluetooth low energy with the low sampling frequency. The sensor attitude was estimated using the attitude estimation algorithms continuously. A Shimmer 3 AHRS system was used as a golden standard, and the orientation of the Shimmer 3 was acquired using the Shimmer Consensys PRO 1.5 software. The experimental test showed poor agreement between the estimated and reference orientations and that the calculated orientation was dependent on an initial calibration. Based on the preliminary results and the current limitations in the wearable motion trackers, we may conclude that a precise estimation of a rigid body angle may not be achieved using wearable motion trackers in daily activities and other clinical applications.
Cortical neuroprostheses that employ repeated electrical stimulation of cortical areas with fixed stimulus parameters, are faced with the problem of large trial-by-trial variability of evoked potentials. This variability is caused by the ongoing cortical signal processing, but it is an unwanted phenomenon if one aims at imprinting neural activity as precisely as possible. Here, we use local field potentials measured by one microelectrode, located at a distance of 200 microns from the stimulation site, to drive the electrically evoked potential toward a desired target potential by real-time adaptation of the stimulus intensity. The functional relationship between ongoing cortical activity, evoked potential, and stimulus intensity was estimated by standard machine learning techniques (support vector regression with problem-specific kernel function) from a set of stimulation trials with randomly varied stimulus intensities. The smallest deviation from the target potential was achieved for low stimulus intensities. Further, the observed precision effect proved time sensitive, since it was abolished by introducing a delay between data acquisition and stimulation. These results indicate that local field potentials contain sufficient information about ongoing local signal processing to stabilize electrically evoked potentials. We anticipate that adaptive low intensity microstimulation will play an important role in future cortical prosthetic devices that aim at restoring lost sensory functions.
Microsoft Kinect sensors are being widely used as low-cost marker-less motion capture systems in various kinematic studies. Previous studies investigated the reliability and validity of Microsoft Kinect sensors by employing marker- based motion capture systems. Both systems employ infrared emitters and detectors to track human posture and physical activities. This paper hypothesizes that the motion capture systems may interfere with Microsoft Kinect one sensor and may influence the sensor's performance in tracking the skeleton. Hence, this paper investigated the impact of a motion capture system on the Microsoft Kinect v2 skeleton algorithm using a mannequin in the presence of eight Qualisys Oqus 300/310 cameras and retroreflective markers. It was found that the motion capture system introduced a destructive impact on the Microsoft Kinect v2 skeleton tracking algorithm. In addition, it was observed that retroreflective markers placed near the joints caused the Microsoft Kinect v2 to give an incorrect reading of estimate the joint position. The motion capture cameras thus caused a time-varying distortion of the Microsoft Kinect estimate of the joint position. It is believed that the inference can be reduced by decreasing the number of markers and avoiding facing the motion capture cameras in sight of Microsoft Kinect v2.
A novel closed-loop system for improving gait in hemiparetic patients by supporting the production of the swing phase using electrical stimulations evoking the nociceptive withdrawal reflex was designed. The system exploits the modular organization of the nociceptive withdrawal reflex and its stimulation site- and gait-phase modulation in order to evoke movements of the hip, knee, and ankle joints during the swing phase. A modified model reference adaptive controller (MRAC) was designed to select the best stimulation parameters from a set of 12 combinations of four electrode locations on the sole of the foot and three different stimulation onset times between heel-off and toe-off. It was hypothesized that the MRAC system would result in a better walking pattern compared with an open-loop preprogrammed fixed pattern of stimulation (FPS) controller. Thirteen chronic or subacute hemiparetic subjects participated in a study to compare the performance of the two control schemes. Both control schemes resulted in a more functional gait compared to no stimulation (P <; 0.05) with a weighted joint angle peak change of 4.0 ± 1.6 (mean ± Standard deviation) degrees and 3.1 ± 1.4 degrees for the MRAC and FPS schemes, respectively. This indicates that the MRAC scheme performed better than the FPS scheme (P <; 0.001) in terms of reaching the control target.
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