Classification and interactive segmentation of EEG synchrony patterns

Alba, Alfonso; Marroquín, José L.; Arce-Santana, Edgar R.; Harmony, Thalı́a

doi:10.1016/j.patcog.2009.03.005

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…At first, this is compatible with the idea that neural groups are engaged then disengaged, a few at a time. Theoretically, this finding is akin to state transition theories of brain function (régimes with attractors); methodologically, it led us to develop a framework for sequencing dynamic patterns from spatiotemporally recorded neurophysiological activity (Tognoli and Kelso, 2009, see also Lehmann, 1990; Kaplan et al, 2005; Abeles et al, 1995; Freeman and Holmes, 2005; Nikolaev et al, 2010; Alba et al, 2010). The inference that transitions exist at the level of cortical sources because we can see transitions in EEG measurements is simple enough.…”

Section: Metastability: a Spatiotemporal Perspectivementioning

confidence: 99%

The Metastable Brain

Tognoli

Kelso

2014

Neuron

597

545

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Neural ensembles oscillate across a broad range of frequencies and are transiently coupled or “bound” together when people attend to a stimulus, perceive, think and act. This is a dynamic, self-assembling process, with parts of the brain engaging and disengaging in time. But how is it done? The theory of Coordination Dynamics proposes a mechanism called metastability, a subtle blend of integration and segregation. Tendencies for brain regions to express their individual autonomy and specialized functions (segregation, modularity) coexist with tendencies to couple and coordinate globally for multiple functions (integration). Although metastability has garnered increasing attention, it has yet to be demonstrated and treated within a fully spatiotemporal perspective. Here, we illustrate metastability in continuous neural and behavioral recordings, and we discuss theory and experiments at multiple scales suggesting that metastable dynamics underlie the real-time coordination necessary for the brain's dynamic cognitive, behavioral and social functions.

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Section: Metastability: a Spatiotemporal Perspectivementioning

confidence: 99%

The Metastable Brain

Tognoli

Kelso

2014

Neuron

597

545

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“…The EEG signals are segmented into different sizes according to the final goal. The window size may be in the range from 0.1s to 60s [13]. The EEG signals were segmented into 1s frame length with no overlap between successive frames.…”

Section: Data Segmentationmentioning

confidence: 99%

Epileptic Seizures Detection Using DCT-II and KNN Classifier in Long-Term EEG Signals

Jumaah

Shihab

Farhan

2020

eijs

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Epilepsy is one of the most common diseases of the nervous system around the world, affecting all age groups and causing seizures leading to loss of control for a period of time. This study presents a seizure detection algorithm that uses Discrete Cosine Transformation (DCT) type II to transform the signal into frequency-domain and extracts energy features from 16 sub-bands. Also, an automatic channel selection method is proposed to select the best subset among 23 channels based on the maximum variance. Data are segmented into frames of one Second length without overlapping between successive frames. K-Nearest Neighbour (KNN) model is used to detect those frames either to ictal (seizure) or interictal (non-seizure) based on Euclidean distance. The experimental results are tested on 21 patients included in the CHB-MIT dataset. The average F1-score was found to be 93.12, whereas the False-Positive Rate (FPR) average was determined to be 0.07.

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“…LDA is a very popular and classical classification method in which the data is projected to a lower-dimensional vector space so that the ratio of the between-class distance to the within-class distance is maximized [12].…”

Section: A Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

“…Hence, the solution to the problem becomes more difficult. The researchers try to solve the above problems [6,12] but most of the proposed methods contain so complex mathematical knowledge and takes more time to develop. For this reason, in this study a novel feature extraction method with a quick response time and simple mathematics is proposed and then it is tested on the binary classification of imaginary and real right/left hand movements.…”

Section: Introductionmentioning

confidence: 99%

Classification of real and imaginary hand movements for a BCI design

Özmen

Gümüşel

2013

2013 36th International Conference on Telecommunications and Signal Processing (TSP)

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This paper searches the discrimination ability of a feature extraction method for EEG analysis. The method is tested on the classification of imagined and real right/left hand movements. The study serves for brain computer interface (BCI) applications which help people to control their body via thoughts. According to the results of the three different classifiers which are LDA, SVM and NN, it is concluded that imagination of hand movements can be used instead of real hand movements especially for tetraplegic patients. The classification accuracies of imaginary hand movements of two subjects are 96% and 99% and accuracies of real hand movements are 85% and 77% respectively.

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Classification and interactive segmentation of EEG synchrony patterns

Cited by 8 publications

References 31 publications

The Metastable Brain

The Metastable Brain

Epileptic Seizures Detection Using DCT-II and KNN Classifier in Long-Term EEG Signals

Classification of real and imaginary hand movements for a BCI design

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