Decomposition of EEG Signals for Multichannel Neural Activity Analysis in Animal Experiments

Vigneron, Vincent; Chen, Hsin; Chen, Yen‐Tai; Lai, Hsueh Chou; Chen, You‐Yin

doi:10.1007/978-3-642-15995-4_59

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The detection of neural spike activity is a technical challenge that is a prerequisite for studying many types of brain function (for more details see Vigneron et al [ 41 ]).…”

Section: Experimental Methodsmentioning

confidence: 99%

Sparse Data Analysis Strategy for Neural Spike Classification

Vigneron

Chen

2014

Computational Intelligence and Neuroscience

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Many of the multichannel extracellular recordings of neural activity consist of attempting to sort spikes on the basis of shared characteristics with some feature detection techniques. Then spikes can be sorted into distinct clusters. There are in general two main statistical issues: firstly, spike sorting can result in well-sorted units, but by with no means one can be sure that one is dealing with single units due to the number of neurons adjacent to the recording electrode. Secondly, the waveform dimensionality is reduced in a small subset of discriminating features. This shortening dimension effort was introduced as an aid to visualization and manual clustering, but also to reduce the computational complexity in automatic classification. We introduce a metric based on common neighbourhood to introduce sparsity in the dataset and separate data into more homogeneous subgroups. The approach is particularly well suited for clustering when the individual clusters are elongated (that is nonspherical). In addition it does need not to select the number of clusters, it is very efficient to visualize clusters in a dataset, it is robust to noise, it can handle imbalanced data, and it is fully automatic and deterministic.

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“…The detection of neural spike activity is a technical challenge that is a prerequisite for studying many types of brain function (for more details see Vigneron et al [ 41 ]).…”

Section: Experimental Methodsmentioning

confidence: 99%

Sparse Data Analysis Strategy for Neural Spike Classification

Vigneron

Chen

2014

Computational Intelligence and Neuroscience

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“…Recurrence quantification analysis [5] (RQA) was used to identify the sleep apnea syndrome using EEG signals. All the sleep stages were distinctly discriminated by means of the RQA accurately [6].…”

Section: The Achievements On Eeg Signal Analysismentioning

confidence: 99%

The EEG processing algorithm based on improved ICA

Xian-bin

Pang

Song³

2013

SPIE Proceedings

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EEG signals contain rich information on brain activity, reflect the electrical activity of brain tissue and brain functional state. By studying the EEG, which help a lot for disease diagnosis and clinical research. Yet the popular algorithm for EEG analysis requires further improvement in effect. Regarding this problem, this paper introduced a improved Independent Components Analysis(ICA) to EEG processing, and a novel method is proposed. The step and feature of the algorithm proposed is described in detail. Finally the simulation experiment demonstrates the effectiveness of the algorithm.

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