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Biomedical Engineering, IEEE Transactions on

Issue 6 • Date June 2004

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Displaying Results 1 - 25 of 36
  • Table of contents

    Page(s): c1 - c4
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  • IEEE Transactions on Biomedical Engineering publication information

    Page(s): c2
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  • Nanostructured surface modification of ceramic-based microelectrodes to enhance biocompatibility for a direct brain-machine interface

    Page(s): 881 - 889
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (446 KB) |  | HTML iconHTML  

    Many different types of microelectrodes have been developed for use as a direct Brain-Machine Interface (BMI) to chronically recording single neuron action potentials from ensembles of neurons. Unfortunately, the recordings from these microelectrode devices are not consistent and often last for only a few weeks. For most microelectrode types, the loss of these recordings is not due to failure of the electrodes but most likely due to damage to surrounding tissue that results in the formation of nonconductive glial-scar. Since the extracellular matrix consists of nanostructured microtubules, we have postulated that neurons may prefer a more complex surface structure than the smooth surface typical of thin-film microelectrodes. We, therefore, investigated the suitability of a nano-porous silicon surface layer to increase the biocompatibility of our thin film ceramic-insulated multisite electrodes. In-vitro testing demonstrated, for the first time, decreased adhesion of astrocytes and increased extension of neurites from pheochromocytoma cells on porous silicon surfaces compared to smooth silicon surfaces. Moreover, nano-porous surfaces were more biocompatible than macroporous surfaces. Collectively, these results support our hypothesis that nano-porous silicon may be an ideal material to improve biocompatibility of chronically implanted microelectrodes. We next developed a method to apply nano-porous surfaces to ceramic insulated, thin-film, microelectrodes and tested them in vivo. Chronic testing demonstrated that the nano-porous surface modification did not alter the electrical properties of the recording sites and did not interfere with proper functioning of the microelectrodes in vivo. View full abstract»

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  • Microelectrode array fabrication by electrical discharge machining and chemical etching

    Page(s): 890 - 895
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (473 KB) |  | HTML iconHTML  

    Wire electrical discharge machining (EDM), with a complementary chemical etching process, is explored and assessed as a method for developing microelectrode array assemblies for intracortically recording brain activity. Assembly processes based on these methods are highlighted, and results showing neural activity successfully recorded from the brain of a mouse using an EDM-based device are presented. Several structures relevant to the fabrication of microelectrode arrays are also offered in order to demonstrate the capabilities of EDM. View full abstract»

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  • Chronic neural recording using silicon-substrate microelectrode arrays implanted in cerebral cortex

    Page(s): 896 - 904
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (496 KB) |  | HTML iconHTML  

    An important aspect of the development of cortical prostheses is the enhancement of suitable implantable microelectrode arrays for chronic neural recording. The objective of this study was to investigate the recording performance of silicon-substrate micromachined probes in terms of reliability and signal quality. These probes were found to consistently and reliably provide high-quality spike recordings over extended periods of time lasting up to 127 days. In a consecutive series of ten rodents involving 14 implanted probes, 13/14 (93%) of the devices remained functional throughout the assessment period. More than 90% of the probe sites consistently recorded spike activity with signal-to-noise ratios sufficient for amplitudes and waveform-based discrimination. Histological analysis of the tissue surrounding the probes generally indicated the development of a stable interface sufficient for sustained electrical contact. The results of this study demonstrate that these planar silicon probes are suitable for long-term recording in the cerebral cortex and provide an effective platform technology foundation for microscale intracortical neural interfaces for use in humans. View full abstract»

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  • Evaluation of spike-detection algorithms fora brain-machine interface application

    Page(s): 905 - 911
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (354 KB) |  | HTML iconHTML  

    Real time spike detection is an important requirement for developing brain machine interfaces (BMIs). We examined three classes of spike-detection algorithms to determine which is best suited for a wireless BMI with a limited transmission bandwidth and computational capabilities. The algorithms were analyzed by tabulating true and false detections when applied to a set of realistic artificial neural signals with known spike times and varying signal to noise ratios. A design-specific cost function was developed to score the relative merits of each detector; correct detections increased the score, while false detections and computational burden reduced it. Test signals both with and without overlapping action potentials were considered. We also investigated the utility of rejecting spikes that violate a minimum refractory period by occurring within a fixed time window after the preceding threshold crossing. Our results indicate that the cost-function scores for the absolute value operator were comparable to those for more elaborate nonlinear energy operator based detectors. The absolute value operator scores were enhanced when the refractory period check was used. Matched-filter-based detectors scored poorly due to their relatively large computational requirements that would be difficult to implement in a real-time system. View full abstract»

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  • On the variability of manual spike sorting

    Page(s): 912 - 918
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (348 KB) |  | HTML iconHTML  

    The analysis of action potentials, or "spikes," is central to systems neuroscience research. Spikes are typically identified from raw waveforms manually for off-line analysis or automatically by human-configured algorithms for on-line applications. The variability of manual spike "sorting" is studied and its implications for neural prostheses discussed. Waveforms were recorded using a micro-electrode array and were used to construct a statistically similar synthetic dataset. Results showed wide variability in the number of neurons and spikes detected in real data. Additionally, average error rates of 23% false positive and 30% false negative were found for synthetic data. View full abstract»

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  • Transmission latencies in a telemetry-linked brain-machine interface

    Page(s): 919 - 924
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (189 KB) |  | HTML iconHTML  

    To be clinically viable, a brain-machine interface (BMI) requires transcutaneous telemetry. Spike-based compression algorithms can be used to reduce the amount of telemetered data, but this type of system is subject to queuing-based transmission delays. This paper examines the relationships between the ratio of output to average input bandwidth of an implanted device and transmission latency and required queue depth. The examination was performed with a computer model designed to simulate the telemetry link. The input to the model was presorted spike data taken from a macaque monkey performing a motor task. The model shows that when the output bandwidth/average input bandwidth is in unity, significant transmission latencies occur. For a 32-neuron system, transmitting 50 bytes of data per spike and with an average neuron firing rate of 8.93 spikes/s, the average maximum delay was approximately 3.2 s. It is not until the output bandwidth is four times the average input bandwidth that average maximum delays are reduced to less than 10 ms. A comparison of neuron firing rate and resulting latencies shows that high latencies result from neuron bursting. These results will impact the design of transcutaneous telemetry in a BMI. View full abstract»

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  • Model-based neural decoding of reaching movements: a maximum likelihood approach

    Page(s): 925 - 932
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (357 KB) |  | HTML iconHTML  

    A new paradigm for decoding reaching movements from the signals of an ensemble of individual neurons is presented. This new method not only provides a novel theoretical basis for the task, but also results in a significant decrease in the error of reconstructed hand trajectories. By using a model of movement as a foundation for the decoding system, we show that the number of neurons required for reconstruction of the trajectories of point-to-point reaching movements in two dimensions can be halved. Additionally, using the presented framework, other forms of neural information, specifically neural "plan" activity, can be integrated into the trajectory decoding process. The decoding paradigm presented is tested in simulation using a database of experimentally gathered center-out reaches and corresponding neural data generated from synthetic models. View full abstract»

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  • Modeling and decoding motor cortical activity using a switching Kalman filter

    Page(s): 933 - 942
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (462 KB) |  | HTML iconHTML  

    We present a switching Kalman filter model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A "hidden state" models the probability of each mixture component and evolves over time in a Markov chain. The model generalizes previous encoding and decoding methods, addresses the non-Gaussian nature of firing rates, and can cope with crudely sorted neural data common in on-line prosthetic applications. View full abstract»

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  • Ascertaining the importance of neurons to develop better brain-machine interfaces

    Page(s): 943 - 953
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (609 KB) |  | HTML iconHTML  

    In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble. View full abstract»

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  • Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis

    Page(s): 954 - 962
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (436 KB) |  | HTML iconHTML  

    Highly accurate asynchronous detection of movement related patterns in individual electrocorticogram channels has been shown using detection based on either event-related potentials (ERPs) or event-related desynchronization and synchronization (ERD/ERS). A method using wavelet-packet features selected with a genetic algorithm was proposed to simultaneously detect ERP and ERD/ERS and was tested on data from seven subjects and four motor tasks. The proposed wavelet method performed better than previous methods with perfect detection for four subject/task combinations and hit percentages greater than 90% with false positive percentages less than 15% for at least one task for all seven subjects. View full abstract»

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  • Force field apparatus for investigating movement control in small animals

    Page(s): 963 - 965
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (162 KB) |  | HTML iconHTML  

    As part of our overall effort to build a closed loop brain-machine interface (BMI), we have developed a simple, low weight, and low inertial torque manipulandum that is ideal for use in motor system investigations with small animals such as rats. It is inexpensive and small but emulates features of large and very expensive systems currently used in monkey and human research. Our device consists of a small programmable torque-motor system that is attached to a manipulandum. Rats are trained to grasp this manipulandum and move it to one or more targets against programmed force field perturbations. Here we report several paradigms that may be used with this device and results from rat's making reaching movements in a variety of force fields. These and other available experimental manipulations allow one to experimentally separate several key variables that are critical for understanding and ultimately emulating the feedforward and feedback mechanisms of motor control. View full abstract»

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  • Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI)

    Page(s): 966 - 970
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (231 KB) |  | HTML iconHTML  

    A brain-computer interface (BCI) based on functional magnetic resonance imaging (fMRI) records noninvasively activity of the entire brain with a high spatial resolution. We present a fMRI-based BCI which performs data processing and feedback of the hemodynamic brain activity within 1.3 s. Using this technique, differential feedback and self-regulation is feasible as exemplified by the supplementary motor area (SMA) and parahippocampal place area (PPA). Technical and experimental aspects are discussed with respect to neurofeedback. The methodology now allows for studying behavioral effects and strategies of local self-regulation in healthy and diseased subjects. View full abstract»

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  • An EEG-driven brain-computer interface combined with functional magnetic resonance imaging (fMRI)

    Page(s): 971 - 974
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (183 KB) |  | HTML iconHTML  

    Self-regulation of slow cortical potentials (SCPs) has been successfully used to prevent epileptic seizures as well as to communicate with completely paralyzed patients. The thought translation device (TTD) is a brain-computer interface (BCI) that was developed for training and application of SCP self-regulation. To investigate the neurophysiological mechanisms of SCP regulation the TTD was combined with functional magnetic resonance imaging (fMRI). The technical aspects and pitfalls of combined fMRI data acquisition and EEG neurofeedback are discussed. First data of SCP feedback during fMRI are presented. View full abstract»

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  • An exploratory study of factors affecting single trial P300 detection

    Page(s): 975 - 978
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    A threshold detector for single-trial P300 detection has been evaluated. The detector operates on the 0-4 Hz band, isolated from the raw electroencephalogram using low-pass filtering, wavelet transforms, or the piecewise prony method (PPM). A detection rate around 70% was found, irregardless of stimulus type, interstimulus interval (ISI), probability of occurrence (Pr) of the target stimuli, intrasession and intrasession effects, or filtering method. This suggests that P300-based brain-machine interfaces can use an ISI as short as 1 s and a Pr of 45%, to increase throughput. View full abstract»

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  • An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate

    Page(s): 979 - 984
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (212 KB) |  | HTML iconHTML  

    An improvement of the information transfer rate of brain-computer communication is necessary for the creation of more powerful and convenient applications. This paper presents an asynchronously controlled three-class brain-computer interface-based spelling device [virtual keyboard (VK)], operated by spontaneous electroencephalogram and modulated by motor imagery. Of the first results of three able-bodied subjects operating the VK, two were successful, showing an improvement of the spelling rate σ, the number of correctly spelled letters/min, up to σ=3.38 (average σ=1.99). View full abstract»

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  • Brain-computer interface design for asynchronous control applications: improvements to the LF-ASD asynchronous brain switch

    Page(s): 985 - 992
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (395 KB) |  | HTML iconHTML  

    The low-frequency asynchronous switch design (LF-ASD) was introduced as a direct brain-computer interface (BCI) technology for asynchronous control applications. The LF-ASD operates as an asynchronous brain switch (ABS) which is activated only when a user intends control and maintains an inactive state output when the user is not meaning to control the device (i.e., they may be idle, thinking about a problem, or performing some other action). Results from LF-ASD evaluations have shown promise, although the reported error rates are too high for most practical applications. This paper presents the evaluation of four new LF-ASD designs with data collected from individuals with high-level spinal cord injuries and able-bodied subjects. These new designs incorporated electroencephalographic energy normalization and feature space dimensionality reduction. The error characteristics of the new ABS designs were significantly better than the LF-ASD design with true positive rate increases of approximately 33% for false positive rates in the range of 1%-2%. The results demonstrate that the dimensionality of the LF-ASD feature space can be reduced without performance degradation. The results also confirm previous findings that spinal cord-injured subjects can operate ABS designs to the same ability as able-bodied subjects. View full abstract»

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  • Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms

    Page(s): 993 - 1002
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    Noninvasive electroencephalogram (EEG) recordings provide for easy and safe access to human neocortical processes which can be exploited for a brain-computer interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. We systematically analyze and develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: 1) the combination of classifiers, each specifically tailored for different physiological phenomena, e.g., slow cortical potential shifts, such as the premovement Bereitschaftspotential or differences in spatio-spectral distributions of brain activity (i.e., focal event-related desynchronizations) and 2) behavioral paradigms inducing the subjects to generate one out of several brain states (multiclass approach) which all bare a distinctive spatio-temporal signature well discriminable in the standard scalp EEG. We derive information-theoretic predictions and demonstrate their relevance in experimental data. We will show that a suitably arranged interaction between these concepts can significantly boost BCI performances. View full abstract»

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  • Support vector channel selection in BCI

    Page(s): 1003 - 1010
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (628 KB) |  | HTML iconHTML  

    Designing a brain computer interface (BCI) system one can choose from a variety of features that may be useful for classifying brain activity during a mental task. For the special case of classifying electroencephalogram (EEG) signals we propose the usage of the state of the art feature selection algorithms Recursive Feature Elimination and Zero-Norm Optimization which are based on the training of support vector machines (SVM) . These algorithms can provide more accurate solutions than standard filter methods for feature selection . We adapt the methods for the purpose of selecting EEG channels. For a motor imagery paradigm we show that the number of used channels can be reduced significantly without increasing the classification error. The resulting best channels agree well with the expected underlying cortical activity patterns during the mental tasks. Furthermore we show how time dependent task specific information can be visualized. View full abstract»

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  • Brain-computer communication and slow cortical potentials

    Page(s): 1011 - 1018
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    A thought translation device (TTD) has been designed to enable direct brain-computer communication using self-regulation of slow cortical potentials (SCPs). However, accuracy of SCP control reveals high intersubject variability. To guarantee the highest possible communication speed, some important aspects of training SCPs are discussed. A baseline correction of SCPs can increase performance. Multichannel recordings show that SCPs are of highest amplitude around the vertex electrode used for feedback, but in some subjects more global distributions were observed. A new method for control of eye movement is presented. Sequential effects of trial-to-trial interaction may also cause difficulties for the user. Finally, psychophysiological factors determining SCP communication are discussed. View full abstract»

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  • Classification of single-trial electroencephalogram during finger movement

    Page(s): 1019 - 1025
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    We present an algorithm to discriminate between the single-trial electroencephalograms (EEG) of two different finger movement tasks. The method uses a spatio-temporal analysis to classify the EEG recorded during voluntary left versus right finger movement tasks. This algorithm produced a classification accuracy of 92.1% on the data from five subjects, without requiring subject training or data selection. This technique can be employed in an EEG-based brain-computer interface due to its high recognition rate, insensitivity to noise, and simplicity in computation. View full abstract»

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  • Noninvasive brain-actuated control of a mobile robot by human EEG

    Page(s): 1026 - 1033
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    Brain activity recorded noninvasively is sufficient to control a mobile robot if advanced robotics is used in combination with asynchronous electroencephalogram (EEG) analysis and machine learning techniques. Until now brain-actuated control has mainly relied on implanted electrodes, since EEG-based systems have been considered too slow for controlling rapid and complex sequences of movements. We show that two human subjects successfully moved a robot between several rooms by mental control only, using an EEG-based brain-machine interface that recognized three mental states. Mental control was comparable to manual control on the same task with a performance ratio of 0.74. View full abstract»

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  • BCI2000: a general-purpose brain-computer interface (BCI) system

    Page(s): 1034 - 1043
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (406 KB) |  | HTML iconHTML  

    Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BCI2000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups. View full abstract»

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Aims & Scope

IEEE Transactions on Biomedical Engineering contains basic and applied papers dealing with biomedical engineering. Papers range from engineering development in methods and techniques with biomedical applications to experimental and clinical investigations with engineering contributions.

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Meet Our Editors

Editor-in-Chief
Bin He
Department of Biomedical Engineering