Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification

Schetinin, Vitaly; Jakaite, Livia; Nyah, Ndifreke; Novakovic, Dusica; Krzanowski, W. J.

doi:10.1142/s0129065717500642

Cited by 29 publications

(12 citation statements)

References 72 publications

(105 reference statements)

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“…This inductive approach is based on sorting out of gradually complicating models and selection of the optimal solution by minimum of external criterion characteristic. Initially suggested in 1971 by Ivakhnenko [18], this algorithm was developed and implemented in multiple practical scenarios, including handling of biomedical data [19, 20]. GMDH analysis was performed with dedicated GMDH DS software (GMDH LLC, USA, New York) version 6.4.…”

Section: Methodsmentioning

confidence: 99%

Multiparametric quantitative and texture 18F-FDG PET/CT analysis for primary malignant tumour grade differentiation

Novikov¹

2019

Eur Radiol Exp

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Background18F-FDG positron emission tomography/computed tomography (PET/CT) is a successfully used imaging modality in oncology. The aim of the study was to investigate a connection of epithelial tumour differentiation grade with both semiquantitative and quantitative metabolic PET data focusing on creation of multiparametric model of tumour grade prediction utilising both standardised uptake value-based and texture-based 18F-FDG PET parameters and to investigate an influence of different image segmentation techniques on these parameters and modelling.Methods18F-FDG PET/CT data from 84 patients with epithelial malignant tumours was retrospectively analysed to create sets of both conventional semiquantitative (based on standardised uptake values), volumetric, and quantitative texture metabolic parameters of primary tumours with four different segmentation techniques.ResultsMost of the calculated volumetric and texture parameters showed to be influenced by segmentation technique. There was no significant difference in values of only three parameters, in all four segmentation methods: homogeneity, energy, and sphericity. Almost every extracted parameter in all segmentation technique subsets showed significant ability to discriminate individual tumour grade versus the subset of remaining two tumour grades. No parameters were able to discriminate all three tumour grades separately simultaneously or without the overlapping of threshold values. Group method of data handling (GMDH) modelling included all the above-mentioned extracted parameters. The highest value to discriminate tumour grade was achieved using ITK-SNAP segmentation, with an accuracy ranging from 91 to 100%.ConclusionsMultiparametric modelling with GMDH utilising both semiquantitative and quantitative texture metabolic PET parameters seems to be an interesting tool for non-invasive malignant epithelial tumours grade differentiation.

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Section: Methodsmentioning

confidence: 99%

Multiparametric quantitative and texture 18F-FDG PET/CT analysis for primary malignant tumour grade differentiation

Novikov¹

2019

Eur Radiol Exp

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“…But some features are susceptible to noise that can only be used for intra-experiment data. Therefore, the time-robustness of features used for individual identification is more important when using in the practical identification system ( Arnau-Gonzalez et al, 2017 ; Schetinin et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG

Yang

Zhong

et al. 2021

Front. Hum. Neurosci.

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An ongoing interest towards identification based on biosignals, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), is growing in the past decades. Previous studies indicated that the inherent information about brain activity may be used to identify individual during resting-state of eyes open (REO) and eyes closed (REC). Electroencephalographic (EEG) records the data from the scalp, and it is believed that the noisy EEG signals can influence the accuracies of one experiment causing unreliable results. Therefore, the stability and time-robustness of inter-individual features can be investigated for the purpose of individual identification. In this work, we conducted three experiments with the time interval of at least 2 weeks, and used different types of measures (Power Spectral Density, Cross Spectrum, Channel Coherence and Phase Lags) to extract the individual features. The Pearson Correlation Coefficient (PCC) is calculated to measure the level of linear correlation for intra-individual, and Support Vector Machine (SVM) is used to obtain the related classification accuracy. Results show that the classification accuracies of four features were 85–100% for intra-experiment dataset, and were 80–100% for fusion experiments dataset. For inter-experiments classification of REO features, the optimized frequency range is 13–40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. For inter-experiments classification of REC, the optimized frequency range is 8–40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. The classification results of Phase Lags are much lower than the other three features. These results show the time-robustness of EEG, which can further use for individual identification system.

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“…Cognitive activity recognition systems [4] provide a bridge between the inside cognitive world and the outside physical world. They are recently used in assisted living [5], smart homes [6], and entertainment industry [7]; EEG-based person identification technique empowers the security systems deployed in bank or customs [8], [9]; EEG signal-based neurological diagnosis can be used to detect the organic brain injury and abnormal synchronous neuronal activity such as epileptic seizure [10], [11]. The classification of cognitive activity faces several challenges.…”

Section: Introductionmentioning

confidence: 99%

“…[7]; EEG-based person identification technique empowers the security systems deployed in bank or customs [8], [9]; EEG signal-based neurological diagnosis can be used to detect the organic brain injury and abnormal synchronous neuronal activity such as epileptic seizure [10], [11].…”

Section: Introductionmentioning

confidence: 99%

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Know Your Mind: Adaptive Cognitive Activity Recognition with Reinforced CNN

Zhang

Yao

Wang

et al. 2019

2019 IEEE International Conference on Data Mining (ICDM)

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Electroencephalography (EEG) signals reflect and measure activities in certain brain areas. Its zero clinical risk and easy-to-use features make it a good choice of providing insights into the cognitive process. However, effective analysis of time-varying EEG signals remains challenging. First, EEG signal processing and feature engineering are time-consuming and highly rely on expert knowledge, and most existing studies focus on domain-specific classification algorithms, which may not apply to other domains. Second, EEG signals usually have low signal-to-noise ratios and are more chaotic than other sensor signals. In this regard, we propose a generic EEG-based cognitive activity recognition framework that can adaptively support a wide range of cognitive applications to address the above issues. The framework uses a reinforced selective attention model to choose the characteristic information among raw EEG signals automatically. It employs a convolutional mapping operation to dynamically transform the selected information into a feature space to uncover the implicit spatial dependency of EEG sample distribution. We demonstrate the effectiveness of the framework under three representative scenarios: intention recognition with motor imagery EEG, person identification, and neurological diagnosis, and further evaluate it on three widely used public datasets. The experimental results show our framework outperforms multiple state-of-the-art baselines and achieves competitive accuracy on all the datasets while achieving low latency and high resilience in handling complex EEG signals across various domains. The results confirm the suitability of the proposed generic approach for a range of problems in the realm of Brain-Computer Interface applications.

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Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification

Cited by 29 publications

References 72 publications

Multiparametric quantitative and texture 18F-FDG PET/CT analysis for primary malignant tumour grade differentiation

Multiparametric quantitative and texture 18F-FDG PET/CT analysis for primary malignant tumour grade differentiation

The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG

Know Your Mind: Adaptive Cognitive Activity Recognition with Reinforced CNN

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