International IEEE/EMBS Conference on Neural Engineering (Int IEEE EMBS Conf Neural Eng)

Publications in this journal

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  Available from: edu.cn

  Conference Proceeding: EEG-based emotion recognition during watching movies

  Dan Nie, Xiao-Wei Wang, Li-Chen Shi, Bao-Liang Lu
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  ABSTRACT: This study aims at finding the relationship between EEG signals and human emotions. EEG signals are used to classify two kinds of emotions, positive and negative. First, we extracted features from original EEG data and used a linear dynamic system approach to smooth these features. An average test accuracy of 87.53% was obtained by using all of the features together with a support vector machine. Next, we reduced the dimension of features through correlation coefficients. The top 100 and top 50 subject-independent features were achieved, with average test accuracies of 89.22% and 84.94%, respectively. Finally, a manifold model was applied to find the trajectory of emotion changes.
  Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011
• Conference Proceeding: Miniature ultrasonically powered wireless nerve cuff stimulator

  P.J. Larson, B.C. Towe
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  ABSTRACT: We present a wireless neural stimulator composed of only three discrete components. The capsule was 8 mm long and was designed to be clasped directly upon a peripheral nerve. Power was supplied by low-intensity 1 MHz ultrasound transmitted into the body. The prototype was capable of generating currents in excess of 1 mA. For in vivo testing the device was implanted in a rat hind limb on the sciatic nerve, and when insonated with pulse intensities of 10-150 mW/cm² the stimulator excited motor axons inducing predictable contractions of the lower leg muscles.
  Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011
• Conference Proceeding: Preliminary validation of an implantable bi-directional neural interface for chronic, in vivo investigation of brain networks

  S. Stanslaski, P. Stypulkowski, J. Giftakis, J. Kemp, B. Isaason, P. Cong, A. Shafquat, P. Afshar, T. Denison
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  ABSTRACT: This paper describes the preliminary technology validation of a bi-directional neural interface in an awake large animal model (ovine). The device addresses the major requirements of a chronic research system, including operation within the implantable environment and electrical stimulation with concurrent bioelectric sensing. Preliminary chronic measurements of network dynamics demonstrate that a chronically stable bi-directional interface to the nervous system is achievable. This was shown through chronic impedance and evoked potential measurements in the thalamo-cortical circuit of Papez. Characterization of bioelectric sensing in the presence of stimulation was also performed through measurements of the noise floor in the presence and absence of stimulation. Further technology validation was performed by using the prototype to correlate activity within and between structures in the circuit of Papez in the presence and absence of stimulation.
  Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011
• Conference Proceeding: Model-based spatiotemporal analysis and control of a network of spiking Basal Ganglia neurons

  Jianbo Liu, H.K. Khalil, K.G. Oweiss
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  ABSTRACT: Precise spatiotemporal control of the output of a network of intricately connected neurons through microstimulation is highly desirable in many neural prosthetic applications. This control, however, is challenging, in part due to the large number of unobserved variables in the system under consideration, the complexity underlying the local mechanisms of microstimulation, and the interplay between the intrinsic network structure and its dynamic response to external stimulation. In this work we use a simplified firing rate model, identified from a network of Hodgkin-Huxley (HH) type spiking Basal Ganglia (BG) neurons, to study the response of the network to patterned microstimulation, and to design effective feedback control laws to approximate a desired spatiotemporal pattern. Mathematical analysis of the simplified model using Singular Value Decomposition (SVD) suggests that the BG neural circuit under study exhibits strong spatiotemporal selectivity and only responds strongly to a range of specific spatiotemporal stimulation patterns. We use the concept of functional controllability based on SVD to evaluate the effectiveness of various combinations of stimulation sites for a given set of neurons to be controlled. The results suggest that the functional controllability is largely decided by the network connectivity and the connection strength. Finally, we demonstrate that the controller design based on the simplified model is indeed effective in driving the output neurons to follow a prescribed spatiotemporal firing pattern in the network output.
  Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011
• Conference Proceeding: Towards an automated, minimal invasive, precision craniotomy on small animals

  B.M. Pohl, A. Schumacher, U.G. Hofmann
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  ABSTRACT: Animal models for surgical interventions of neurological disorders are gaining more and more importance with the advent of sophisticated micro-technologies. One unavoidable procedure to access brain tissue is the craniotomy, the removal of skull bone. Since this procedure is always performed manually, it may lead to unwanted complications due too extensive bone removals, unwanted penetrations of the cerebral tissue or severe bleeding. In this work, a module to augment our Spherical Assistant for Stereotaxic Surgery (SASSU), a microrobot for small animal surgeries by monitoring the applied force of an electric drill, was designed. The exchangeable module consists of either a force or a sound measurement system, controlling a high speed microdrill.
  Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011
• Conference Proceeding: Experiments on using combined short window bivariate autoregression for EEG classification

  Tuan Hoang, Dat Tran, Phuoc Nguyen, Xu Huang, D. Sharma
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  ABSTRACT: In EEG-based classification problem, most of currently used features are univariate and extracted from single channels. However EEG signals recorded from multiple channels for a brain activity are correlated, features extracted from the EEG signals should reflect relationships among those channels. For this reason, we propose and apply a bivariate feature called Combined Short-Window BiVariate AutoRegres-sive model (CSWBVAR) for EEG classification problems. Given a pair of channels, we firstly divide each of them in to overlapping segments or short windows, and then estimate BVAR parameters for each pair of segments. CSWBVAR is formed by combining extracted BVAR parameters together with a pre-defined overlapping window parameter. We analyzed and compared CSWBVAR feature and univariate feature using the dataset III for motor imagery problem of BCI Competition II (2003). Preliminary results show that using CSWBVAR feature can improve classification accuracy up to 7% comparing with using univariate one with the same linear-kernel SVM classifier.
  Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011
• Conference Proceeding: Optimizing recording depth to decode movement goals from cortical field potentials

  D.A. Markowitz, Y.T. Wong, C.M. Gray, B. Pesaran
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  ABSTRACT: Brain-machine interfaces decode movement goals and trajectories from neural activity that is recorded using chronically-implanted microelectrode arrays. Fixed geometry arrays are limited for this purpose because electrodes cannot be moved after implantation, and optimization of the electrode recording configuration requires the re-implantation of a new array. Here, we optimize local field potential (LFP) recordings using a chronically-implanted microelectrode array with electrodes that can be moved after implantation. In a series of recordings, we systematically vary the depth of each electrode in the frontal eye field of a monkey performing eye movements. We find that a decoder predicting movement goals from LFP activity on 32 electrodes provides information rates as high as 5.0 bits/s and that performance varies significantly with recording depth. These results indicate that recording depth is a critical parameter for the performance of LFP-based brain-machine interfaces that decode movement goals.
  Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011
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  Available from: ac.uk

  Conference Proceeding: On fusion of heart and brain signals for hybrid BCI

  S. Shahid, G. Prasad, R.K. Sinha
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  ABSTRACT: This paper investigates the fusion of ECG with EEG in devising a hybrid brain-computer interface (hBCI). Effortful motor imagery (MI) based BCI experiments were arranged with a twelve seconds of cue-based MI paradigm on six healthy individuals over two sessions of 160 trials, while ECG and EEG signals were simultaneously recorded. The proposed hBCI uses bispectrum based features from EEG and ECG along with an LDA classifier. The off-line analysis shows an improvement in MI task detection accuracy if both ECG and EEG features are considered together. In addition, the time domain analysis of ECG signal shows that the average heart rate increases during MI state, which clearly shows that the cardiac system responds to MI related tasks.
  Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011
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  Available from: berkeley.edu

  Conference Proceeding: Adaptive Kalman filtering for closed-loop Brain-Machine Interface systems

  S. Dangi, S. Gowda, R. Heliot, J.M. Carmena
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  ABSTRACT: Brain-Machine Interface (BMI) decoding algorithms are often trained offline, but this paradigm ignores both the non-stationarity of neural signals and the feedback that exists in online, closed-loop control. To address these problems, we have developed an Adaptive Kalman Filter (AKF), a Kalman filter variant that adaptively updates its model parameters during training. For a Kalman filter decoder, batch retraining methods require completely re-estimating the parameter matrices from sufficient data to perform regression accurately, even if only small changes are necessary. Conversely, the AKF is designed to update the decoder parameters continuously and more intelligently. We simulated a population of 41 neurons learning to control a 2D computer cursor. The AKF yielded significantly faster skill acquisition and better robustness to perturbation and neuron loss than a standard Kalman filter with periodic batch retraining.
  Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011
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  Available from: stanford.edu

  Conference Proceeding: Combining wireless neural recording and video capture for the analysis of natural gait

  J.D. Foster, O. Freifeld, P. Nuyujukian, S.I. Ryu, M.J. Black, K.V. Shenoy
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  ABSTRACT: Neural control of movement is typically studied in constrained environments where there is a reduced set of possible behaviors. This constraint may unintentionally limit the applicability of findings to the generalized case of unconstrained behavior. We hypothesize that examining the unconstrained state across multiple behavioral contexts will lead to new insights into the neural control of movement and help advance the design of neural prosthetic decode algorithms. However, to pursue electrophysiological studies in such a manner requires a more flexible framework for experimentation. We propose that head-mounted neural recording systems with wireless data transmission, combined with markerless computer-vision based motion tracking, will enable new, less constrained experiments. As a proof-of-concept, we recorded and wirelessly transmitted broadband neural data from 32 electrodes in premotor cortex while acquiring single-camera video of a rhesus macaque walking on a treadmill. We demonstrate the ability to extract behavioral kinematics using an automated computer vision algorithm without use of markers and to predict kinematics from the neural data. Together these advances suggest that a new class of “freely moving monkey” experiments should be possible and should help broaden our understanding of the neural control of movement.
  Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011
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  Available from: edu.cn

  Conference Proceeding: Central and peripheral coding of joint position by descending γ-static commands and muscle spindle afferents

  Xin He, Ning Lan
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  ABSTRACT: The purpose of this study is to investigate plausible central and peripheral coding strategies of joint angle by descending γ_s commands and proprioceptive afferents in humans. Experimental evidence in both human and animal has shown that the firing rate of Ia afferents was linearly correlated to joint angle. Yet, experiments did not elucidate how Ia encoding of joint angle would be affected by α-γ co-activation, and how the peripheral neuromuscular system is informed of central representation of joint configuration. This study is aimed at addressing these issues using a realistic virtual arm (VA) model. In simulated experiments, elbow and shoulder angles of the VA are moved to different angular positions. The γ_s commands to muscles remain constant, or are adjusted with joint angle in linear and nonlinear fashions. The Ia afferents of muscles are evaluated in each case of γ_s modulation. Results show that Ia firing rates of mono-articular muscles can be fine-tuned to the characteristics of experimental recordings, while γ_s commands are nonlinearly modulated with joint angle. This suggests that γ_s commands could serve as the output of central encoding of joint information for posture and movement, and Ia afferents could be used to decode joint angle information faithfully.
  Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011
• Conference Proceeding: EEG-based evaluation system for motion sickness estimation

  Chun-Shu Wei, Li-Wei Ko, Shang-Wen Chuang, Tzyy-Ping Jung, Chin-Teng Lin
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  ABSTRACT: Motion sickness is a common symptom which occurs when the brain receives conflicting sensory information. Although many motion sickness-related biomarkers have been identified, estimating humans' motion sickness level (MSL) remains a challenge in operational environments. Traditionally, questionnaire and physical check are the common ways to passively evaluate subject's sickness level. This study proposes a physiology-based estimation system that can automatically assess subject's motion-sickness level in operational environments. Our previous study showed that increases in self-reported MSL in a Virtual-reality based driving experiment on a motion platform were accompanied by elevated alpha (8-12Hz) power most prominently in the occipital midline electroencephalogram (EEG). This study explores the feasibility of an automatic MSL estimation based on spontaneous EEG spectrum. To this end, this study employed three different estimators: 1) Linear regression (LR), 2) Radial basis function neural network (RBFNN), and 3) Support vector regression (SVR). The results of this study showed that SVR outperformed LR and RBFNN in estimating MSL from EEG spectrum. The averaged accuracy of MSL estimation by SVR was 86.92±6.09% across 6 subjects. This demonstration could lead to a practical system for noninvasive monitoring of the motion sickness in real-world environments.
  Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011
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  Available from: kuleuven.ac.be

  Conference Proceeding: BOLD correlates of Alpha and Beta EEG-rhythm during a motor task

  C. Cooreman, R. Sclocco, M.G. Tana, K. Vanderperren, E. Visani, F. Panzica, S. Franceschetti, S. Van Huffel, S. Cerutti, A.M. Bianchi
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  ABSTRACT: In this study, simultaneously acquired EEG and fMRI data from a motor experiment are analyzed. The motor task consists in moving the right hand and is performed by a group of healthy volunteers. The objective is to find the most adequate way to model the movement-related blood oxygen level-dependent (BOLD) response present in the fMRI data. The analysis of the fMRI data is performed using Statistical Parametric Mapping (SPM) and estimating two different models. In the first one (motor event model), the BOLD response is modeled following the time instants of the motor events. The second one (brain wave model) incorporates the dynamics of the 5 canonical EEG rhythms (a, p, y, 8, 6) to describe the BOLD response. From the results, it can be concluded that the motor event model better describes the BOLD response related to the movement itself, but that the brain wave model is better suited to characterize the BOLD response of complementary brain processes.
  Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011