IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · Jun 2004
Comparative StudyBCI2000: a general-purpose brain-computer interface (BCI) system.
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. ⋯ 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.
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IEEE Trans Biomed Eng · Jun 2004
Comparative StudyBCI Competition 2003--Data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram.
The t-CWT, a novel method for feature extraction from biological signals, is introduced. It is based on the continuous wavelet transform (CWT) and Student's t-statistic. ⋯ The method was validated in the BCI Competition 2003, where it was a winner (provided best classification) on two data sets acquired in two different BCI paradigms, P300 speller and slow cortical potential (SCP) self-regulation. These results are presented here.
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IEEE Trans Biomed Eng · Jun 2004
Comparative StudyThe BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.
Interest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. ⋯ The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.
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IEEE Trans Biomed Eng · Jun 2004
Comparative StudyChronic neural recording using silicon-substrate microelectrode arrays implanted in cerebral cortex.
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. ⋯ 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.
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IEEE Trans Biomed Eng · Jun 2004
Comparative StudyAscertaining the importance of neurons to develop better brain-machine interfaces.
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. ⋯ 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.