Dynamics of EEG-signals in epilepsy: Spatio temporal analysis by Cellular Nonlinear Networks

Niederhofer, C.; Gollas, F.; Tetzlaff, Ronald

doi:10.1109/ecctd.2007.4529595

Cited by 5 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Litt et al [38], who also used EEG analysis for seizure onset prediction, states that there is considerable evidence that seizures develop over up to hours prior to the occurrence of the seizure. Niederhofer et al [48] studied a generalized EEG-signal analysis based on spatiotemporal linear prediction methods and found a distinct increase in the values of the predictor coefficients just prior to ictal onset.…”

Section: Prediction Of Ictal States Using Eeg Analysis 251 the Preimentioning

confidence: 99%

Automated epileptic seizure onset detection

Ponnambalam

2010

2010 International Conference on Autonomous and Intelligent Systems, AIS 2010

View full text Add to dashboard Cite

Section: Prediction Of Ictal States Using Eeg Analysis 251 the Preimentioning

confidence: 99%

Automated epileptic seizure onset detection

Ponnambalam

2010

2010 International Conference on Autonomous and Intelligent Systems, AIS 2010

View full text Add to dashboard Cite

“…Niederhoefer et al [27,28] reviewed different approaches to the analysis of EEG signals based on cellular neural networks. They studied several methods of EEG analysis based on multi-layer convolutional neural networks (CNN) for seizure and discussed approximation of the correlation dimension, prediction of EEG-signals, and an EEG pattern detection algorithm.…”

Section: Introductionmentioning

confidence: 99%

Computational model for detection of abnormal brain connections in children with autism

Askari¹,

Setarehdan²,

Sheikhani³

et al. 2018

J. Integr. Neurosci.

View full text Add to dashboard Cite

In neuropsychological disorders significant abnormalities in brain connectivity are observed in some regions. A novel model demonstrates connectivity between different brain regions in children with autism. Wavelet decomposition is used to extract features such as relative energy and entropy from electroencephalograph signals. These features are used as input to a 3Dcellular neural network model that indicates brain connectivity. Results show significant differences and abnormalities in the left hemisphere, (p < 0.05) at electrodes AF3, F3, P7, T7, and O1 in the alpha band, AF3, F7, T7, and O1 in the beta band, and T7 and P7 in the gamma band for children with autism when compared with non-autistic controls. Abnormalities in the connectivity of frontal and parietal lobes and the relations of neighboring regions for all three bands (particularly the gamma band) were detected for autistic children. Evaluation demonstrated the alpha frequency band had the best level of distinction (96.6%) based on the values obtained from a cellular neural network that employed support vector machine methods.Many investigators have studied the EEGs and analyzed the signals of autistic subjects [14][15][16]. Sheikhani et al.[15] conducted a study of EEG signals based on Lempel-Ziv frequency methods (LZ) and the short-time Fourier transform (STFT). After evaluating results,

show abstract

“…Used features are based on extracted features, feature reduction methods and feature selection measures. Samiee et al, [8] used different algorithms with different results for a specific data set. The need to find the best algorithm to do a five-class finger flexion classification is to choose flexed finger among one hand's fingers.…”

Section: Introductionmentioning

confidence: 99%

Classification of ECoG Motor Image using Fusion Technique

Aswinseshadri¹,

Bai²

2014

IJCA

View full text Add to dashboard Cite

Brain-Computer Interfaces (BCIs) ensure non-muscular communication between a user and external device by using brain activity. Currently, BCIs were applied in the medical field to increase quality of life of patients suffering from neuromuscular disorders. Most BCI systems use scalp recorded electroencephalographic activity, while Electrocorticography (ECoG) is a minimally-invasive alternative to Electroencephalogram (EEG), which ensures higher and superior signal characteristics enabling rapid user training and quicker communication. This paper presents a BCI system; ECoG signals are pre-processed and features are extracted from using Wavelet Packet Tree and Common Spatial Pattern. The extracted features are fused using Median Absolute Deviation (MAD) to improve the discrimination power of the feature vector. BCI Competition III, Data Set I having ECoG recordings motor imagery is used in investigation to evaluate the presented methodology.

show abstract

Dynamics of EEG-signals in epilepsy: Spatio temporal analysis by Cellular Nonlinear Networks

Cited by 5 publications

References 13 publications

Automated epileptic seizure onset detection

Automated epileptic seizure onset detection

Computational model for detection of abnormal brain connections in children with autism

Classification of ECoG Motor Image using Fusion Technique

Contact Info

Product

Resources

About