IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · May 2008
Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver's drowsiness detection and warning.
Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. ⋯ In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.
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IEEE Trans Biomed Eng · May 2008
Single-trial EEG source reconstruction for brain-computer interface.
A new way to improve the classification rate of an EEG-based brain-computer interface (BCI) could be to reconstruct the brain sources of EEG and to apply BCI methods to these derived sources instead of raw measured electrode potentials. EEG source reconstruction methods are based on electrophysiological information that could improve the discrimination between BCI tasks. In this paper, we present an EEG source reconstruction method for BCI. ⋯ The features are ranked with mutual information before being fed to a proximal support vector machine. The dataset IV of the BCI competition II and data from four subjects serve as test data. Results show that the EEG inverse solution improves the classification rate and can lead to results comparable to the best currently known methods.
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IEEE Trans Biomed Eng · May 2008
Analysis of FMRI data with drift: modified general linear model and Bayesian estimator.
The slowly varying drift poses a major problem in the analysis of functional magnetic resonance imaging (fMRI) data. In this paper, based on the observation that noise in fMRI is long memory fractional noise and the slowly varying drift resides in a subspace spanned only by large scale wavelets, we examine a modified general linear model (GLM) in wavelet domain under Bayesian framework. ⋯ Results obtained from simulated as well as real fMRI data show that the proposed Bayesian estimator can accurately capture the noise structure, and hence, result in robust estimation of the parameters in GLM. Besides, the proposed model selection criterion works well and could efficiently remove the drift.
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This paper presents a new algorithm for electrocardiogram (ECG) signal compression based on local extreme extraction, adaptive hysteretic filtering and Lempel-Ziv-Welch (LZW) coding. The algorithm has been verified using eight of the most frequent normal and pathological types of cardiac beats and an multi-layer perceptron (MLP) neural network trained with original cardiac patterns and tested with reconstructed ones. Aspects regarding the possibility of using the principal component analysis (PCA) to cardiac pattern classification have been investigated as well. A new compression measure called "quality score," which takes into account both the reconstruction errors and the compression ratio, is proposed.
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IEEE Trans Biomed Eng · Apr 2008
Robust unsupervised detection of action potentials with probabilistic models.
We develop a robust and fully unsupervised algorithm for the detection of action potentials from extracellularly recorded data. Using the continuous wavelet transform allied to probabilistic mixture models and Bayesian probability theory, the detection of action potentials is posed as a model selection problem. Our technique provides a robust performance over a wide range of simulated conditions, and compares favorably to selected supervised and unsupervised detection techniques.