IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
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IEEE Trans Neural Syst Rehabil Eng · Dec 2006
Very low-noise ENG amplifier system using CMOS technology.
In this paper, we describe the design and testing of a system for recording electroneurographic signals (ENG) from a multielectrode nerve cuff (MEC). This device, which is an extension of the conventional nerve signal recording cuff, enables ENG to be classified by action potential velocity. In addition to electrical measurements, we provide preliminary in vitro data obtained from frogs that demonstrate the validity of the technique for the first time. ⋯ The ten-channel system we describe was realized in a 0.8 microm CMOS technology and detailed measured results are presented. The overall gain is 10 000 and the total input-referred root mean square (rms) noise in a bandwidth 1 Hz-5 kHZ is 291 nV. The active area is 12 mm(2) and the power consumption is 24 mW from +/-2.5 V power supplies.
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IEEE Trans Neural Syst Rehabil Eng · Sep 2006
Clinical TrialUtilizing gamma band to improve mental task based brain-computer interface design.
A common method for designing brain-computer Interface (BCI) is to use electroencephalogram (EEG) signals extracted during mental tasks. In these BCI designs, features from EEG such as power and asymmetry ratios from delta, theta, alpha, and beta bands have been used in classifying different mental tasks. ⋯ Elman neural network (ENN) trained by the resilient backpropagation algorithm was used to classify the power and asymmetry ratios from EEG into different combinations of two mental tasks. The results indicated that ((1) the classification performance and training time of the BCI design were improved through the use of additional gamma band features; (2) classification performances were nearly invariant to the number of ENN hidden units or feature extraction method.
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IEEE Trans Neural Syst Rehabil Eng · Sep 2006
Effects of neural refractoriness on spatio-temporal variability in spike initiations with Electrical stimulation.
In this paper, the effects of neural refractoriness on action potential (spike) initiations with electrical stimulation are investigated using computer modeling and simulation techniques. The computational model was composed of a myelinated nerve fiber with 50 nodes of Ranvier, each consisting of stochastic sodium and potassium channels, making it possible to represent the fluctuations of spike initiation. A series of two-pulse stimuli was presented by a stimulating electrode above the central (26th) node of Ranvier. ⋯ It was shown that the distribution of spike initiations tended to become greater spatially and longer temporally as the masker-probe intervals (MPIs) of the two-pulse stimuli shortened. It was also shown that the number of activated sodium channels as functions of space and time tended to become smaller due to inactivation of sodium channels and varied spatially and temporally as MPIs shortened. These findings may imply that the stochastic sodium channels during a relative refractory period may contribute to enhancing the fluctuations in spike initiations, and give us an insight into encoding information with electric stimuli to improve the performance of the prosthetic devices, especially cochlear implants.
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IEEE Trans Neural Syst Rehabil Eng · Jun 2006
BCI Meeting 2005--workshop on BCI signal processing: feature extraction and translation.
This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback.
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IEEE Trans Neural Syst Rehabil Eng · Jun 2006
Comparative StudyThe BCI competition. III: Validating alternative approaches to actual BCI problems.
A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. ⋯ BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.