Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146582
Xujiong Dong, Haofei Wang, Zhaokang Chen, Bertram E. Shi
We describe a hybrid brain computer interface that integrates information from a four-class motor imagery based EEG classifier with information about gaze trajectories from an eye tracker. The novel aspect of this system is that no explicit gaze behavior is required of the user. Rather, the natural gaze behavior of the user integrated probabilistically to smooth the noisy classification results from the motor imagery based EEG. The goal is to provide for a more natural interaction with the BCI system than if gaze were used as an explicit command signal, as is commonly done. Our results on a 2D cursor control task show that integration of gaze information significantly improves task completion accuracy and reduces task completion time. In particular, our system achieves over 80% target completion accuracy on a cursor control task requiring guidance to one of 12 targets.
{"title":"Hybrid Brain Computer Interface via Bayesian integration of EEG and eye gaze","authors":"Xujiong Dong, Haofei Wang, Zhaokang Chen, Bertram E. Shi","doi":"10.1109/NER.2015.7146582","DOIUrl":"https://doi.org/10.1109/NER.2015.7146582","url":null,"abstract":"We describe a hybrid brain computer interface that integrates information from a four-class motor imagery based EEG classifier with information about gaze trajectories from an eye tracker. The novel aspect of this system is that no explicit gaze behavior is required of the user. Rather, the natural gaze behavior of the user integrated probabilistically to smooth the noisy classification results from the motor imagery based EEG. The goal is to provide for a more natural interaction with the BCI system than if gaze were used as an explicit command signal, as is commonly done. Our results on a 2D cursor control task show that integration of gaze information significantly improves task completion accuracy and reduces task completion time. In particular, our system achieves over 80% target completion accuracy on a cursor control task requiring guidance to one of 12 targets.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131469777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146633
Maruan Al-Shedivat, R. Naous, E. Neftci, G. Cauwenberghs, K. Salama
Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms.
{"title":"Inherently stochastic spiking neurons for probabilistic neural computation","authors":"Maruan Al-Shedivat, R. Naous, E. Neftci, G. Cauwenberghs, K. Salama","doi":"10.1109/NER.2015.7146633","DOIUrl":"https://doi.org/10.1109/NER.2015.7146633","url":null,"abstract":"Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130197723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146729
Linda Xu, S. Loh, C. Taswell
Clinical telegaming integrates telecare and videogaming to enable a more convenient and enjoyable experience for patients when providers diagnose, monitor, and treat a variety of health problems via web-enabled telecommunications. In recent years, clinical telegaming systems have been applied to physical therapy and rehabilitation, evaluation of mental health, and prevention and management of obesity and diabetes. Parkinson's disease (PD) is suitable for development of new clinical telegaming applications because PD patients are known to experience motor symptoms that can be improved by physical therapy. Recent research suggests that sensory processing deficits may also play an important role in these motor impairments because successful motor function requires multisensory integration. In this paper, we describe a new web-enabled software system that uses clinical telegaming to evaluate and improve multisensory integration ability in users. This software has the potential to be used in diagnostic and therapeutic telegaming for PD patients.
{"title":"Web-enabled software for clinical telegaming evaluation of multisensory integration and response to auditory and visual stimuli","authors":"Linda Xu, S. Loh, C. Taswell","doi":"10.1109/NER.2015.7146729","DOIUrl":"https://doi.org/10.1109/NER.2015.7146729","url":null,"abstract":"Clinical telegaming integrates telecare and videogaming to enable a more convenient and enjoyable experience for patients when providers diagnose, monitor, and treat a variety of health problems via web-enabled telecommunications. In recent years, clinical telegaming systems have been applied to physical therapy and rehabilitation, evaluation of mental health, and prevention and management of obesity and diabetes. Parkinson's disease (PD) is suitable for development of new clinical telegaming applications because PD patients are known to experience motor symptoms that can be improved by physical therapy. Recent research suggests that sensory processing deficits may also play an important role in these motor impairments because successful motor function requires multisensory integration. In this paper, we describe a new web-enabled software system that uses clinical telegaming to evaluate and improve multisensory integration ability in users. This software has the potential to be used in diagnostic and therapeutic telegaming for PD patients.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121763163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146606
H. Lorach, Georges A. Goetz, Y. Mandel, R. Smith, David Boinagrov, X. Lei, R. Dalal, P. Huie, T. Kamins, J. Harris, K. Mathieson, A. Sher, D. Palanker
Patients with retinal degeneration lose sight due to gradual demise of photoreceptors. Electrical stimulation of the surviving retinal neurons provides an alternative route for delivery of visual information. We developed subretinal photovoltaic arrays to convert pulsed light into bi-phasic pulses of current to stimulate the nearby inner retinal neurons. Bright pulsed illumination is provided by image projection from video goggles and avoids photophobic effects by using near-infrared (NIR, 880-915nm) light. Experiments in-vitro and in-vivo demonstrate that the network-mediated retinal stimulation preserves many features of natural vision, such as flicker fusion, adaptation to static images, and most importantly, high spatial resolution. Our implants with 70μm pixels restored visual acuity to half of the normal level in rats with retinal degeneration. Ease of implantation and tiling of these wireless arrays to cover a large visual field, combined with their high resolution opens the door to highly functional restoration of sight.
{"title":"Photovoltaic restoration of high visual acuity in rats with retinal degeneration","authors":"H. Lorach, Georges A. Goetz, Y. Mandel, R. Smith, David Boinagrov, X. Lei, R. Dalal, P. Huie, T. Kamins, J. Harris, K. Mathieson, A. Sher, D. Palanker","doi":"10.1109/NER.2015.7146606","DOIUrl":"https://doi.org/10.1109/NER.2015.7146606","url":null,"abstract":"Patients with retinal degeneration lose sight due to gradual demise of photoreceptors. Electrical stimulation of the surviving retinal neurons provides an alternative route for delivery of visual information. We developed subretinal photovoltaic arrays to convert pulsed light into bi-phasic pulses of current to stimulate the nearby inner retinal neurons. Bright pulsed illumination is provided by image projection from video goggles and avoids photophobic effects by using near-infrared (NIR, 880-915nm) light. Experiments in-vitro and in-vivo demonstrate that the network-mediated retinal stimulation preserves many features of natural vision, such as flicker fusion, adaptation to static images, and most importantly, high spatial resolution. Our implants with 70μm pixels restored visual acuity to half of the normal level in rats with retinal degeneration. Ease of implantation and tiling of these wireless arrays to cover a large visual field, combined with their high resolution opens the door to highly functional restoration of sight.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133679585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146745
Tommaso Proietti, N. Jarrassé, A. Roby-Brami, G. Morel
Neurorehabilitation efficiency increases with therapy intensity and subject's involvement during physical exercises. Robotic exoskeletons could bring both features, if they could adapt the level of assistance to patient's motor capacities. To this aim, we developed an exoskeleton controller, based on adaptive techniques, that can actively modulate the stiffness of the robotic device in function of the subject's activity. We tested this control law on one healthy subject with an upper-limb exoskeleton. The experiment consisted in learning a trajectory imposed by the robot. The early results show the different features allowed by our controller with respect to controllers commonly used for neurorehabilitation with exoskeletons.
{"title":"Adaptive control of a robotic exoskeleton for neurorehabilitation","authors":"Tommaso Proietti, N. Jarrassé, A. Roby-Brami, G. Morel","doi":"10.1109/NER.2015.7146745","DOIUrl":"https://doi.org/10.1109/NER.2015.7146745","url":null,"abstract":"Neurorehabilitation efficiency increases with therapy intensity and subject's involvement during physical exercises. Robotic exoskeletons could bring both features, if they could adapt the level of assistance to patient's motor capacities. To this aim, we developed an exoskeleton controller, based on adaptive techniques, that can actively modulate the stiffness of the robotic device in function of the subject's activity. We tested this control law on one healthy subject with an upper-limb exoskeleton. The experiment consisted in learning a trajectory imposed by the robot. The early results show the different features allowed by our controller with respect to controllers commonly used for neurorehabilitation with exoskeletons.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132117089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146791
Tiaotiao Liu, X. Tian
Working memory (WM) provides temporary information storage for performance of cognitive tasks. Neural signals in hippocampus-prefrontal cortex (HPC-PFC) circuit interact and construct a network. The question raised here is how the neural signals connect and transfer in the HPC-PFC network to perform a WM task? In this study, 32-channel local field potentials (LFPs) were recorded with two electrode arrays respectively implanted in HPC and PFC during a rat Y-maze working memory task. The principle frequency band of LFPs during the task was theta, determined via short-time Fourier transform. Functional connectivity strength was further calculated quantitatively and a causal network was defined by directed transfer function (DTF). The information transfer in the network was described by information flow. The results show that (1)the DTF curve peaked before the choice point. (2) The information flow in working memory was from HPC to PFC. These findings suggest that the functional connectivity strengthens at WM state and HPC is the WM information source in the HPC- PFC network.
{"title":"LFPs network of hippocampal-prefrontal circuit during working memory task","authors":"Tiaotiao Liu, X. Tian","doi":"10.1109/NER.2015.7146791","DOIUrl":"https://doi.org/10.1109/NER.2015.7146791","url":null,"abstract":"Working memory (WM) provides temporary information storage for performance of cognitive tasks. Neural signals in hippocampus-prefrontal cortex (HPC-PFC) circuit interact and construct a network. The question raised here is how the neural signals connect and transfer in the HPC-PFC network to perform a WM task? In this study, 32-channel local field potentials (LFPs) were recorded with two electrode arrays respectively implanted in HPC and PFC during a rat Y-maze working memory task. The principle frequency band of LFPs during the task was theta, determined via short-time Fourier transform. Functional connectivity strength was further calculated quantitatively and a causal network was defined by directed transfer function (DTF). The information transfer in the network was described by information flow. The results show that (1)the DTF curve peaked before the choice point. (2) The information flow in working memory was from HPC to PFC. These findings suggest that the functional connectivity strengthens at WM state and HPC is the WM information source in the HPC- PFC network.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134551374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146601
R. So, Zhiming Xu, C. Libedinsky, Kyaw Kyar Toe, K. Ang, S. Yen, Cuntai Guan
Using a brain-machine interface (BMI), a non-human primate (NHP) was trained to control a mobile robotic platform in real time using spike activity from the motor cortex, enabling self-motion through brain-control. The decoding model was initially trained using neural signals recorded when the NHP controlled the platform using a joystick. Using this decoding model, we compared the performance of the BMI during brain control with and without the use of a dummy joystick, and found that the success ratio dropped by 40% and time taken increased by 45% when the dummy joystick was removed. Performance during full brain control was only restored after a recalibration of the decoding model. We aimed to understand the differences in the underlying neural representations of movement intentions with and without the use of a dummy joystick, and showed that there were significant changes in both directional tuning, as well as global firing rates. These results indicate that the strategies used by the NHP for self-motion were different depending on whether a dummy joystick was present. We propose that a recalibration of the decoding model is an important step during the implementation of a BMI system for self-motion.
{"title":"Neural representations of movement intentions during brain-controlled self-motion","authors":"R. So, Zhiming Xu, C. Libedinsky, Kyaw Kyar Toe, K. Ang, S. Yen, Cuntai Guan","doi":"10.1109/NER.2015.7146601","DOIUrl":"https://doi.org/10.1109/NER.2015.7146601","url":null,"abstract":"Using a brain-machine interface (BMI), a non-human primate (NHP) was trained to control a mobile robotic platform in real time using spike activity from the motor cortex, enabling self-motion through brain-control. The decoding model was initially trained using neural signals recorded when the NHP controlled the platform using a joystick. Using this decoding model, we compared the performance of the BMI during brain control with and without the use of a dummy joystick, and found that the success ratio dropped by 40% and time taken increased by 45% when the dummy joystick was removed. Performance during full brain control was only restored after a recalibration of the decoding model. We aimed to understand the differences in the underlying neural representations of movement intentions with and without the use of a dummy joystick, and showed that there were significant changes in both directional tuning, as well as global firing rates. These results indicate that the strategies used by the NHP for self-motion were different depending on whether a dummy joystick was present. We propose that a recalibration of the decoding model is an important step during the implementation of a BMI system for self-motion.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134561706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146756
Eleanor Dunn, M. Lowery
Oscillatory neural activity in the beta frequency band (12-30 Hz) is elevated in Parkinson's disease and is correlated with the associated motor symptoms. These oscillations, which can be monitored through the local field potential (LFP) recorded by a deep brain stimulation (DBS) electrode, can give insight into the mechanisms of action, as well as treatment efficacy, of DBS. A detailed physiological model of the cortico-basal ganglia network during DBS of the subthalamic nucleus (STN) is presented. The model incorporates extracellular stimulation of STN afferent fibers, with both orthodromic and antidromic activation, and the LFP detected at the electrode. Pathological beta-band oscillations within the cortico-basal ganglia network were simulated and found to be attenuated following the application of DBS. The effects of varying DBS parameters, including pulse amplitude, duration and frequency, on the LFP at the DBS electrode were then assessed. The model presented here can be further used to understand the interaction of DBS with the complex dynamics of the cortico-basal ganglia network and subsequent changes observed in the LFP.
{"title":"A model of the cortico-basal ganglia network and local field potential during deep brain stimulation","authors":"Eleanor Dunn, M. Lowery","doi":"10.1109/NER.2015.7146756","DOIUrl":"https://doi.org/10.1109/NER.2015.7146756","url":null,"abstract":"Oscillatory neural activity in the beta frequency band (12-30 Hz) is elevated in Parkinson's disease and is correlated with the associated motor symptoms. These oscillations, which can be monitored through the local field potential (LFP) recorded by a deep brain stimulation (DBS) electrode, can give insight into the mechanisms of action, as well as treatment efficacy, of DBS. A detailed physiological model of the cortico-basal ganglia network during DBS of the subthalamic nucleus (STN) is presented. The model incorporates extracellular stimulation of STN afferent fibers, with both orthodromic and antidromic activation, and the LFP detected at the electrode. Pathological beta-band oscillations within the cortico-basal ganglia network were simulated and found to be attenuated following the application of DBS. The effects of varying DBS parameters, including pulse amplitude, duration and frequency, on the LFP at the DBS electrode were then assessed. The model presented here can be further used to understand the interaction of DBS with the complex dynamics of the cortico-basal ganglia network and subsequent changes observed in the LFP.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124444646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146747
Meredith J. Cler, Graham E. Voysey, C. Stepp
Poor control over the velopharyngeal (VP) port (connection between the oral and nasal cavities) leads to unintelligible speech; this VP dysfunction (VPD) can be due to structural abnormalities, poor motor control, or lack of appropriate feedback (hearing impairment). VP control is not aided by visual feedback since the relevant anatomy is not visible to the speaker or the listener. Here we present initial data from a novel, game-based speech rehabilitation platform designed for children with VPD, in which online feedback of speech nasalization is provided based on measurements of nasal skin vibration and speech acoustics. Twelve pediatric participants (three with VPD) completed one session with the video game and were all able to easily use the game. Over 90% of the participants reported that the game was at least “kind of fun” and that the equipment at least “kind of comfortable”. Over 90% of participants and 100% of their parents said they could use the game at home. Results are promising for further development and long-term testing in individuals with VPD.
{"title":"Video game speech rehabilitation for velopharyngeal dysfunction: Feasibility and pilot testing","authors":"Meredith J. Cler, Graham E. Voysey, C. Stepp","doi":"10.1109/NER.2015.7146747","DOIUrl":"https://doi.org/10.1109/NER.2015.7146747","url":null,"abstract":"Poor control over the velopharyngeal (VP) port (connection between the oral and nasal cavities) leads to unintelligible speech; this VP dysfunction (VPD) can be due to structural abnormalities, poor motor control, or lack of appropriate feedback (hearing impairment). VP control is not aided by visual feedback since the relevant anatomy is not visible to the speaker or the listener. Here we present initial data from a novel, game-based speech rehabilitation platform designed for children with VPD, in which online feedback of speech nasalization is provided based on measurements of nasal skin vibration and speech acoustics. Twelve pediatric participants (three with VPD) completed one session with the video game and were all able to easily use the game. Over 90% of the participants reported that the game was at least “kind of fun” and that the equipment at least “kind of comfortable”. Over 90% of participants and 100% of their parents said they could use the game at home. Results are promising for further development and long-term testing in individuals with VPD.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131622993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146707
Tamas Kapelner, N. Jiang, I. Vujaklija, O. Aszmann, A. Holobar, D. Farina
For the past six decades, signal processing methods for myoelectric control of prostheses consisted mainly of calculating time- and frequency domain features of the EMG signal. This type of feature extraction considers the surface EMG as colored noise, neglecting its generation as a sum of motor unit activities. In this study we propose the use of motor unit behavior for classifying motor tasks with the aim of myoelectric control. We recorded high-density surface EMG of three patients who underwent targeted muscle reinnervation, and decomposed these signals into motor unit spike trains using an automatic offline EMG decomposition method. From the motor unit spike trains we used the number of discharges in each analysis interval as a feature for a support vector machine classifier. The same classifier was used for discriminating classic time-domain EMG features, for comparison. Classification accuracy was greater for motor unit information than for the classic features (97.06%±1.74 vs 85.01%±13.66), especially when the number of classes was high (95.11% ± 1.74 vs 69.25% ± 4.04 for 11 classes). These results suggest that the identification of motor unit activity from surface EMG can be a powerful way for pattern recognition in targeted muscle reinnervation patients.
过去六十年来，假肢肌电控制的信号处理方法主要包括计算肌电信号的时域和频域特征。这种特征提取方法将表面肌电信号视为彩色噪声，忽略了其作为运动单元活动总和的产生。在本研究中，我们提出利用运动单元行为对运动任务进行分类，以达到肌电控制的目的。我们记录了三名接受靶向肌肉神经支配的患者的高密度表面肌电图，并使用自动离线肌电图分解方法将这些信号分解为运动单元尖峰列车。从运动单元尖峰列车中，我们使用每个分析区间的放电次数作为支持向量机分类器的特征。为了进行比较，我们使用了相同的分类器来区分经典的时域肌电图特征。与经典特征相比，运动单位信息的分类准确率更高（97.06%±1.74 vs 85.01%±13.66），尤其是当类别数量较多时（11 个类别的分类准确率为 95.11%±1.74 vs 69.25%±4.04 ）。这些结果表明，从表面肌电图中识别运动单位活动是对目标肌肉神经再支配患者进行模式识别的有效方法。
{"title":"Classification of motor unit activity following targeted muscle reinnervation","authors":"Tamas Kapelner, N. Jiang, I. Vujaklija, O. Aszmann, A. Holobar, D. Farina","doi":"10.1109/NER.2015.7146707","DOIUrl":"https://doi.org/10.1109/NER.2015.7146707","url":null,"abstract":"For the past six decades, signal processing methods for myoelectric control of prostheses consisted mainly of calculating time- and frequency domain features of the EMG signal. This type of feature extraction considers the surface EMG as colored noise, neglecting its generation as a sum of motor unit activities. In this study we propose the use of motor unit behavior for classifying motor tasks with the aim of myoelectric control. We recorded high-density surface EMG of three patients who underwent targeted muscle reinnervation, and decomposed these signals into motor unit spike trains using an automatic offline EMG decomposition method. From the motor unit spike trains we used the number of discharges in each analysis interval as a feature for a support vector machine classifier. The same classifier was used for discriminating classic time-domain EMG features, for comparison. Classification accuracy was greater for motor unit information than for the classic features (97.06%±1.74 vs 85.01%±13.66), especially when the number of classes was high (95.11% ± 1.74 vs 69.25% ± 4.04 for 11 classes). These results suggest that the identification of motor unit activity from surface EMG can be a powerful way for pattern recognition in targeted muscle reinnervation patients.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134211622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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