EEG analysis by multi layer Cellular Nonlinear Networks (CNN)

Niederhoefer, Christian; Gollas, F.; Tetzlaff, Ronald

doi:10.1109/biocas.2006.4600299

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…All In order to compare with the algorithm of BEMD+HT+CNN proposed in this paper, the experiments used logit boost [10], HHT+CNN [8], CNN [9], DNN [11] to test the data. All model experimental results were obtained by 10 cross validations.…”

Section: Emi S Imulation S Ignal Experimentsmentioning

confidence: 99%

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EMI Signal Classification Based on Improved HHT and CNN

2021

Proceedings of 2021 the 11th International Workshop on Computer Science and Engineering

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This paper proposes a new single channel EMI signal classification method. In this method, we propose to use an improved HHT algorithm, BEMD algorithm and HT algorithm, to convert nonlinear and non-steady-state single-channel electromagnetic interference signals into time-frequency domain matrices, which solves the problem of traditional EMD algorithms. After that, we use the time-frequency domain matrix as the input of the CNN to classify the EMI signals. By the simulation signal experiment and EEG signal experiment verification, the proposed algorithm has a good classification effect.

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Section: Emi S Imulation S Ignal Experimentsmentioning

confidence: 99%

“…As a signal classification algorithm, we propose a method based on the improved HHT algorithm (BEMD+HT)+CNN. CNN [6] s a deep learning neural network widely used in many other fields [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

EMI Signal Classification Based on Improved HHT and CNN

2021

Proceedings of 2021 the 11th International Workshop on Computer Science and Engineering

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“…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.

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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,

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