Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state.

By Ralph G. Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, P. David, Christian Erich Elger. Physical review. E, Statistical, nonlinear, and soft matter physics, 2001

2,932 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/f37ed6c360377e7807928a6587211c18a2d18b7a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Economics and Econometrics

Citations in the corpus

Deep learning for electroencephalogram (EEG) classification tasks: a review (2019, methodology): “The datasets that are used in more than one study within this review include an emotion recognition dataset ([56], various subsets used in ten studies), a mental workload dataset ([57], used in four studies), two motor imagery datasets from the same BCI competition ([58], one dataset used in six stu”
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency (2022, background): “(2) EPILEPSY [62] monitors the brain activities of 500 subjects with single-channel EEG sensor (174 Hz).”
Multiclass Support Vector Machines for EEG-Signals Classification (2007, methodology): “The EEG signals under study were surface EEG recordings from healthy volunteers with eyes closed and eyes open, and intracranial EEG recordings from epilepsy patients during the seizure-free interval from within and from outside the seizure-generating area as well as intracranial EEG recordings of e”
EEG signal classification using PCA, ICA, LDA and support vector machines (2010, methodology): “We used the publicly available data described in Andrzejak et al. (2001).”
Surrogate data for hypothesis testing of physical systems (2018, background): “[97] combined the nonlinear prediction error and an estimate of an effective correlation dimension with themethod of IAAFT surrogates to compare dynamical properties of brain activity from different recording regions and from different physiological and pathological brain states.”
Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning (2019, background): “Candy (CD) Power Ranking [51] 13 85 Boston (BT) Housing Prices [52] 14 506 Weather (WR) Conditions in World War Two [53] 31 ≈119000 CalCOFI (CI) - Oceanographic Data [54] 74 ≈876000 Epileptic (EP) Seizures [55] 179 ≈11500 Food Recipes (RE) [56] 680 ≈20000 MNIST [43] 784 70000”
An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach (2018, methodology): “To help and aid neurologists, we need a generalized automatic system that can show good performance even with fewer training samples (Andrzejak et al., 2001; Sharmila et al., 2016).”
A Survey of Deep Active Learning (2020, background): “[30, 55, 66, 186] [123, 187, 206] [36, 181, 205] Erie County [66], EEG [9], BreaKHis [210], SVEB, SVDB [186] Biomedical”
Deep Learning for Medical Anomaly Detection – A Survey (2020, methodology): “been validated using the Bonn University EEG database [7] and achieves an overall 89.”
Epileptic Seizures Detection Using Deep Learning Techniques: A Review (2020, methodology): “Multiple EEG datasets, namely, Freiburg [20], CHB-MIT [21], Kaggle [22], Bonn [23], Flint-Hills [13], Bern-Barcelona [24], Hauz Khas [13], and Zenodo [25] are available to develop the automated epileptic seizure detection systems.”
Epileptic Seizure Classification of EEG Time-Series Using Rational Discrete Short-Time Fourier Transform (2015, methodology): “Section V presents the experiments on the recordings of the Bonn EEG database [15], while Section VI discusses the results and the comparisons with state-of-the-art methods.”
Recurrent neural networks employing Lyapunov exponents for EEG signals classification (2005, methodology): “We used the data described in Andrzejak et al. (2001), which is publicly available.”
Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis (2009, methodology): “The PSD that is calculated in the previous stage (2) is used to extract several features.”