Independent Component Automatic Clustering and Its Application on Multi-Trails Imaginary Hand Movement Related EEG
Dept. of Biomed. Eng., Tianjin Univ., TianjinDOI: 10.1109/VECIMS.2009.5068883 Conference: Virtual Environments, Human-Computer Interfaces and Measurements Systems, 2009. VECIMS '09. IEEE International Conference on
Source: IEEE Xplore
How to extract task-relevant components from spontaneous electroencephalogram background is an open problem in EEG signal analysis. An Independent Component Automatic Clustering (ICAC) method, which combined Independent Component Analysis (ICA) with automatic clustering, is developed in this paper. In ICAC, the ICA decomposed components were grouped into several clusters and sorted automatically. A majority of task-relevant components could be grouped into one cluster and be recognized easily, which can compensate the traditional ICA limitation of component sorting without any task specialized orders. We adopted this method on multi trails EEG signals during imaginary hand movement, results showed that ICAC can automatically extract task-relevant component and increase the Fisher Criterion (FC) separability significantly. Furthermore, we show that the residual mutual information between task-relevant components is not useless as previously regarded but very useful on components recognition.
Conference Paper: Automatic EEG artifact removal based on ICA and Hierarchical Clustering[Show abstract] [Hide abstract]
ABSTRACT: Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques, however, they are typically influenced by extraneous interference, like muscle movements, eye blinks, eye movements, background noise, etc. Therefore, a preprocessing step to remove artifacts is extremely important. This paper presents an effective artifact removal algorithm, based on Independent Component Analysis (ICA) and Hierarchical Clustering. Our technique utilizes general temporal and spectral features and particular information about target Event-Related Potentials (ERPs) (e.g. the timing of N200 and P300 on inhibition task or the specific electrodes contributing to the ERPs) to separate ERPs and artifact activities. Our method considers templates for desired ERPs to select event-related components for signal reconstruction. In our experimental study, we show that our proposed method can effectively enhance the ERPs for all fifteen subjects in the study, even for those that barely display ERPs in the raw recordings.
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ABSTRACT: Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from Independent Component Analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event related potential (ERP)-related independent components (ICs). However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g. identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by non-biological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature based clustering algorithm used to identify artifacts which have physiological origins and 2) the electrode-scalp impedance information employed for identifying non-biological artifacts. The results on EEG data collected from 10 subjects show that our algorithm can effectively detect, separate, and remove both physiological and non-biological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods.
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