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Brain machine interface

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https://www.readbyqxmd.com/read/28227970/combining-a-hybrid-robotic-system-with-a-bain-machine-interface-for-the-rehabilitation-of-reaching-movements-a-case-study-with-a-stroke-patient
#1
F Resquin, J Ibanez, J Gonzalez-Vargas, F Brunetti, I Dimbwadyo, S Alves, L Carrasco, L Torres, Jose Luis Pons, F Resquin, J Ibañez, J Gonzalez-Vargas, F Brunetti, I Dimbwadyo, S Alves, L Carrasco, L Torres, Jose Luis Pons, S Alves, Jose Luis Pons, F Brunetti, J Gonzalez-Vargas, F Resquin, I Dimbwadyo, L Carrasco, L Torres, J Ibanez
Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the user's movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227965/multiscale-brain-machine-interface-decoders
#2
Han-Lin Hsieh, Maryam M Shanechi, Han-Lin Hsieh, Maryam M Shanechi, Han-Lin Hsieh, Maryam M Shanechi
Brain-machine interfaces (BMI) have vastly used a single scale of neural activity, e.g., spikes or electrocorticography (ECoG), as their control signal. New technology allows for simultaneous recording of multiple scales of neural activity, from spikes to local field potentials (LFP) and ECoG. These advances introduce the new challenge of modeling and decoding multiple scales of neural activity jointly. Such multi-scale decoding is challenging for two reasons. First, spikes are discrete-valued and ECoG/LFP are continuous-valued, resulting in fundamental differences in statistical characteristics...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227845/high-performance-wearable-two-channel-hybrid-bci-system-with-eye-closure-assist
#3
Yubing Jiang, Hyeonseok Lee, Gang Li, Wan-Young Chung, Yubing Jiang, Hyeonseok Lee, Gang Li, Wan-Young Chung, Wan-Young Chung, Gang Li, Hyeonseok Lee, Yubing Jiang
Generally, eye closure (EC) and eye opening (EO)-based alpha blocking has widely recognized advantages, such as being easy to use, requiring little user training, while motor imagery (MI) is difficult for some users to have concrete feelings. This study presents a hybrid brain-computer interface (BCI) combining MI and EC strategies - such an approach aims to overcome some disadvantages of MI-based BCI, improve the performance and universality of the BCI. The EC/EO is employed to control the machine to switch in different states including forward, stop, changing direction motions, while the MI is used to control the machine to turn left or right for 90° by imagining the hands grasp motions when the system is switched into "changing direction" state...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227841/comparing-eeg-its-time-derivative-and-their-joint-use-as-features-in-a-bci-for-2-d-pointer-control
#4
Dimitrios Andreou, Riccardo Poli, Dimitrios Andreou, Riccardo Poli, Riccardo Poli, Dimitrios Andreou
Efficient and accurate classification of event related potentials is a core task in brain-computer interfaces (BCI). This is normally obtained by first extracting features from the voltage amplitudes recorded via EEG at different channels and then feeding them into a classifier. In this paper we evaluate the relative benefits of using the first order temporal derivatives of the EEG signals, not the EEG signals themselves, as inputs to the BCI: an area that has not been thoroughly examined. Specifically, we compare the classification performance of features extracted from the first derivative, with those derived from the amplitude, as well as their combination using data from a P300-based BCI mouse...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227808/adaptive-decoding-using-local-field-potentials-in-a-brain-machine-interface
#5
Rosa So, Camilo Libedinsky, Kai Keng Ang, Wee Chiek Clement Lim, Kyaw Kyar Toe, Cuntai Guan, Rosa So, Camilo Libedinsky, Kai Keng Ang, Wee Chiek Clement Lim, Kyaw Kyar Toe, Cuntai Guan, Rosa So, Wee Chiek Clement Lim, Kai Keng Ang, Kyaw Kyar Toe, Camilo Libedinsky, Cuntai Guan
Brain-machine interface (BMI) systems have the potential to restore function to people who suffer from paralysis due to a spinal cord injury. However, in order to achieve long-term use, BMI systems have to overcome two challenges - signal degeneration over time, and non-stationarity of signals. Effects of loss in spike signals over time can be mitigated by using local field potential (LFP) signals for decoding, and a solution to address the signal non-stationarity is to use adaptive methods for periodic recalibration of the decoding model...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227510/feasibility-of-an-ultra-low-power-digital-signal-processor-platform-as-a-basis-for-a-fully-implantable-brain-computer-interface-system
#6
Po T Wang, Keulanna Gandasetiawan, Colin M McCrimmon, Alireza Karimi-Bidhendi, Charles Y Liu, Payam Heydari, Zoran Nenadic, An H Do, Po T Wang, Keulanna Gandasetiawan, Colin M McCrimmon, Alireza Karimi-Bidhendi, Charles Y Liu, Payam Heydari, Zoran Nenadic, An H Do, An H Do, Zoran Nenadic, Colin M McCrimmon, Keulanna Gandasetiawan, Payam Heydari, Alireza Karimi-Bidhendi, Po T Wang, Charles Y Liu
A fully implantable brain-computer interface (BCI) can be a practical tool to restore independence to those affected by spinal cord injury. We envision that such a BCI system will invasively acquire brain signals (e.g. electrocorticogram) and translate them into control commands for external prostheses. The feasibility of such a system was tested by implementing its benchtop analogue, centered around a commercial, ultra-low power (ULP) digital signal processor (DSP, TMS320C5517, Texas Instruments). A suite of signal processing and BCI algorithms, including (de)multiplexing, Fast Fourier Transform, power spectral density, principal component analysis, linear discriminant analysis, Bayes rule, and finite state machine was implemented and tested in the DSP...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227166/modeling-distinct-sources-of-neural-variability-driving-neuroprosthetic-control
#7
Preeya Khanna, Vivek R Athalye, Suraj Gowda, Rui M Costa, Jose M Carmena, Preeya Khanna, Vivek R Athalye, Suraj Gowda, Rui M Costa, Jose M Carmena, Jose M Carmena, Vivek R Athalye, Rui M Costa, Preeya Khanna, Suraj Gowda
Many closed-loop, continuous-control brain-machine interface (BMI) architectures rely on decoding via a linear readout of noisy population neural activity. However, recent work has found that decomposing neural population activity into correlated and uncorrelated variability reveals that improvements in cursor control coincide with the emergence of correlated neural variability. In order to address how correlated and uncorrelated neural variability arises and contributes to BMI cursor control, we simulate a neural population receiving combinations of shared inputs affecting all cells and private inputs affecting only individual cells...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227165/reward-value-is-encoded-in-primary-somatosensory-cortex-and-can-be-decoded-from-neural-activity-during-performance-of-a-psychophysical-task
#8
David B McNiel, John S Choi, John P Hessburg, Joseph T Francis, David B McNiel, John S Choi, John P Hessburg, Joseph T Francis, John P Hessburg, Joseph T Francis, John S Choi, David B McNiel
Encoding of reward valence has been shown in various brain regions, including deep structures such as the substantia nigra as well as cortical structures such as the orbitofrontal cortex. While the correlation between these signals and reward valence have been shown in aggregated data comprised of many trials, little work has been done investigating the feasibility of decoding reward valence on a single trial basis. Towards this goal, one non-human primate (macaca radiata) was trained to grip and hold a target level of force in order to earn zero, one, two, or three juice rewards...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227163/maximum-correntropy-based-attention-gated-reinforcement-learning-designed-for-brain-machine-interface
#9
Hongbao Li, Fang Wang, Qiaosheng Zhang, Shaomin Zhang, Yiwen Wang, Xiaoxiang Zheng, Jose C Principe, Hongbao Li, Fang Wang, Qiaosheng Zhang, Shaomin Zhang, Yiwen Wang, Xiaoxiang Zheng, Jose C Principe, Yiwen Wang, Jose C Principe, Xiaoxiang Zheng, Qiaosheng Zhang, Shaomin Zhang, Hongbao Li, Fang Wang
Reinforcement learning is an effective algorithm for brain machine interfaces (BMIs) which interprets the mapping between neural activities with plasticity and the kinematics. Exploring large state-action space is difficulty when the complicated BMIs needs to assign credits over both time and space. For BMIs attention gated reinforcement learning (AGREL) has been developed to classify multi-actions for spatial credit assignment task with better efficiency. However, the outliers existing in the neural signals still make interpret the neural-action mapping difficult...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227162/a-non-linear-mapping-algorithm-shaping-the-control-policy-of-a-bidirectional-brain-machine-interface
#10
Fabio Boi, Marianna Semprini, Alessandro Vato, Fabio Boi, Marianna Semprini, Alessandro Vato, Fabio Boi, Marianna Semprini, Alessandro Vato
Motor brain-machine interfaces (BMIs) transform neural activities recorded directly from the brain into motor commands to control the movements of an external object by establishing an interface between the central nervous system (CNS) and the device. Bidirectional BMIs are closed-loop systems that add a sensory channel to provide the brain with an artificial feedback signal produced by the interaction between the device and the external world. Taking inspiration from the functioning of the spinal cord in mammalians, in our previous works we designed and developed a bidirectional BMI that uses the neural signals recorded form rats' motor cortex to control the movement of an external object...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227102/a-flexible-parylene-probe-for-in-vivo-recordings-from-multiple-subregions-of-the-rat-hippocampus
#11
Huijing Xu, Ahuva Weltman, Min-Chi Hsiao, Kee Scholten, Ellis Meng, Theodore W Berger, Dong Song, Huijing Xu, Ahuva Weltman, Min-Chi Hsiao, Kee Scholten, Ellis Meng, Theodore W Berger, Dong Song, Ellis Meng, Dong Song, Min-Chi Hsiao, Theodore W Berger, Ahuva Weltman, Kee Scholten, Huijing Xu
The hippocampus is crucial to the formation of long-term memory and declarative memory. It is divided into three sub-fields the CA1, the CA3 and the DG. To understand the neuronal circuitry within the hippocampus and to study the role of the hippocampus in memory function requires the collection of neural activities from multiple subregions of the hippocampus simultaneously. Micro-wire electrode arrays are commonly used as an interface with neural systems. However, recording from multiple deep brain regions with curved anatomical structures such as the thin cell body layers of the hippocampus requires the micro-wires to be arranged into a highly accurate, complex layout that is difficult to fabricated manually...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227094/mutual-information-based-feature-selection-for-low-cost-bcis-based-on-motor-imagery
#12
L Schiatti, L Faes, J Tessadori, G Barresi, L Mattos, L Schiatti, L Faes, J Tessadori, G Barresi, L Mattos, G Barresi, L Mattos, J Tessadori, L Faes, L Schiatti
In the present study a feature selection algorithm based on mutual information (MI) was applied to electro-encephalographic (EEG) data acquired during three different motor imagery tasks from two dataset: Dataset I from BCI Competition IV including full scalp recordings from four subjects, and new data recorded from three subjects using the popular low-cost Emotiv EPOC EEG headset. The aim was to evaluate optimal channels and band-power (BP) features for motor imagery tasks discrimination, in order to assess the feasibility of a portable low-cost motor imagery based Brain-Computer Interface (BCI) system...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227092/eeg-based-single-trial-detection-of-errors-from-multiple-error-related-brain-activity
#13
Guofa Shou, Lei Ding, Guofa Shou, Lei Ding, Guofa Shou, Lei Ding
A key ability of the human brain is to monitor erroneous events and adjust behaviors accordingly. Electrophysiological and neuroimaging studies have demonstrated different brain activities related to errors. Meanwhile, the recognition of error-related brain activity as one aspect of performance monitoring has been reported for potential applications in clinical neuroscience and brain-machine interface, where single-trial analysis and classification would provide novel insights on dynamic brain responses to errors...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227091/hybrid-bci-approach-to-control-an-artificial-tibio-femoral-joint
#14
Luis Mercado, Angel Rodriguez-Linan, Luis M Torres-Trevino, G Quiroz, Luis Mercado, Angel Rodriguez-Linan, Luis M Torres-Trevino, G Quiroz, Luis Mercado, Angel Rodriguez-Linan, Luis M Torres-Trevino, G Quiroz
Brain-Computer Interfaces (BCIs) for disabled people should allow them to use their remaining functionalities as control possibilities. BCIs connect the brain with external devices to perform the volition or intent of movement, regardless if that individual is unable to perform the task due to body impairments. In this work we fuse electromyographic (EMG) with electroencephalographic (EEG) activity in a framework called "Hybrid-BCI" (hBCI) approach to control the movement of a simulated tibio-femoral joint...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226823/properties-of-primary-motor-cortical-local-field-potentials-in-the-leg-and-trunk-representations-during-arm-movements
#15
Adil A Tobaa, Matthew D Best, Karthikeyan Balasubramanian, Kazutaka Takahashi, Nicholas G Hatsopoulos, Adil A Tobaa, Matthew D Best, Karthikeyan Balasubramanian, Kazutaka Takahashi, Nicholas G Hatsopoulos, Adil A Tobaa, Matthew D Best, Karthikeyan Balasubramanian, Kazutaka Takahashi, Nicholas G Hatsopoulos
Large, spatially-distributed populations of motor cortical neurons are recruited during upper limb movements. Here, we examined how beta attenuation, a mesoscopic reflection of unit engagement, varies across a spatially expansive sampling of primary motor cortex in a non-human primate (macaca mulatta). We found that electrodes in both the trunk and leg representation of motor cortex exhibit qualitatively similar behavior to electrodes in the arm representation during a planar reaching task, despite the fact that there were no overt movements of the trunk or leg...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226811/day-to-day-variability-in-hybrid-passive-brain-computer-interfaces-comparing-two-studies-assessing-cognitive-workload
#16
Samantha L Klosterman, Justin R Estepp, Jason W Monnin, James C Christensen, Samantha L Klosterman, Justin R Estepp, Jason W Monnin, James C Christensen, Jason W Monnin, Samantha L Klosterman, Justin R Estepp, James C Christensen
As hybrid, passive brain-computer interface systems become more advanced, it is important to grow our understanding of how to produce generalizable pattern classifiers of physiological data. One of the most difficult problems in applying machine learning algorithms to these data types is nonstationarity, which can evolve over the course of hours and days, and is more susceptible to changes resulting from complex cognitive function in comparison to simple, stimulus-based processes. This nonstationarity, referenced as day-to-day variability, results in the inability of many learning algorithms to generalize to new data...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226807/detection-of-control-or-idle-state-with-a-likelihood-ratio-test-in-asynchronous-ssvep-based-brain-computer-interface-systems
#17
Lenis M Merino, Tapsya Nayak, Garrett Hall, Daniel J Pack, Yufei Huang, Lenis M Merino, Tapsya Nayak, Garrett Hall, Daniel J Pack, Yufei Huang, Yufei Huang, Lenis M Merino, Garrett Hall, Daniel J Pack, Tapsya Nayak
We consider the detection of the control or idle state in an asynchronous Steady-state visually evoked potential (SSVEP)-based brain computer interface system. We propose a likelihood ratio test using Canonical Correlation Analysis (CCA) scores calculated from the EEG measurements. The test exploits the state-specific distributions of CCA scores. The algorithm was tested on offline measurements from 42 participants and the results should a significant improvement in detection error rate over the support vector machine classifier...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226803/closed-loop-afferent-electrical-stimulation-for-recovery-of-hand-function-in-individuals-with-motor-incomplete-spinal-injury-early-clinical-results
#18
Christopher J Schildt, Sarah H Thomas, Elizabeth S Powell, Lumy Sawaki, Sridhar Sunderam, Christopher J Schildt, Sarah H Thomas, Elizabeth S Powell, Lumy Sawaki, Sridhar Sunderam, Lumy Sawaki, Christopher J Schildt, Elizabeth S Powell, Sarah H Thomas, Sridhar Sunderam
Afferent electrical stimulation is known to augment the effect of rehabilitative therapy through use-dependent cortical plasticity. Experiments pairing transcranial magnetic stimulation (TMS) with peripheral nerve stimulation (PNS) have shown a timing-dependent effect on motor evoked potential (MEP) amplitude suggesting that PNS applied in closed-loop (CL) mode could augment this effect through positive reinforcement. We present early results from a clinical trial in which an EEG brain-machine interface (BMI) was used to apply PNS to two subjects in response to motor intent detected from sensorimotor cortex in a cue-driven hand grip task...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226801/electrocorticographic-signals-comparison-in-sensorimotor-cortex-between-contralateral-and-ipsilateral-hand-movements
#19
Yile Jin, Mingwei Lu, Xiaotian Wang, Shaomin Zhang, Junming Zhu, Xiaoxiang Zheng, Yile Jin, Mingwei Lu, Xiaotian Wang, Shaomin Zhang, Junming Zhu, Xiaoxiang Zheng
Brain machine interfaces (BMIs) have emerged as a technology to restore lost functionality in motor impaired patients. Most BMI systems employed neural signals from contralateral hemisphere. But many studies have also demonstrated the possibility to control hand movement using signals from ipsilateral one. However, the relationship of neural signals in sensorimotor cortex between contralateral and ipsilateral hand movement control is still unclear. In this study, the electrocorticographic signals (ECoG) of sensorimotor cortex were analyzed in two epilepsy participants when they performed a visual guided rock-scissors-paper task by using contralateral and ipsilateral hand respectively...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226798/decoding-speech-using-the-timing-of-neural-signal-modulation
#20
Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang, Vikash Gilja, Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang, Vikash Gilja, Vikash Gilja, Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang
Brain-machine interfaces (BMIs) have great potential for applications that restore and assist communication for paralyzed individuals. Recently, BMIs decoding speech have gained considerable attention due to their potential for high information transfer rates. In this study, we propose a novel decoding approach based on hidden Markov models (HMMs) that uses the timing of neural signal changes to decode speech. We tested the decoder's performance by predicting vowels from electrocorticographic (ECoG) data of three human subjects...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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