Tamburro et al., 2018 - Google Patents
A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordingsTamburro et al., 2018
View HTML- Document ID
- 8749875298019347278
- Author
- Tamburro G
- Fiedler P
- Stone D
- Haueisen J
- Comani S
- Publication year
- Publication venue
- PeerJ
External Links
Snippet
Background EEG may be affected by artefacts hindering the analysis of brain signals. Data- driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often …
- OWIKHYCFFJSOEH-UHFFFAOYSA-N isocyanate 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Khanmohammadi et al. | Adaptive seizure onset detection framework using a hybrid PCA–CSP approach | |
Tang et al. | Validation of SOBI components from high-density EEG | |
Lawhern et al. | Detection and classification of subject-generated artifacts in EEG signals using autoregressive models | |
Frølich et al. | Classification of independent components of EEG into multiple artifact classes | |
Yuvaraj et al. | Hemispheric asymmetry non-linear analysis of EEG during emotional responses from idiopathic Parkinson’s disease patients | |
Ghaderyan et al. | Time-varying singular value decomposition analysis of electrodermal activity: A novel method of cognitive load estimation | |
Amin et al. | A novel approach based on data redundancy for feature extraction of EEG signals | |
Chakraborty et al. | Automated detection of epileptic seizures using multiscale and refined composite multiscale dispersion entropy | |
Temko et al. | Instantaneous measure of EEG channel importance for improved patient-adaptive neonatal seizure detection | |
Alotaibi et al. | Ensemble Machine Learning Based Identification of Pediatric Epilepsy. | |
Chen et al. | An empirical EEG analysis in brain death diagnosis for adults | |
Zangeneh Soroush et al. | EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms | |
Suchetha et al. | Sequential Convolutional Neural Networks for classification of cognitive tasks from EEG signals | |
Anastasiadou et al. | Unsupervised detection and removal of muscle artifacts from scalp EEG recordings using canonical correlation analysis, wavelets and random forests | |
Suhail et al. | Distinguishing cognitive states using electroencephalography local activation and functional connectivity patterns | |
Patnaik et al. | Deep RNN learning for EEG based functional brain state inference | |
Placidi et al. | A fast and scalable framework for automated artifact recognition from EEG signals represented in scalp topographies of independent components | |
Ahmad et al. | Discriminating the different human brain states with EEG signals using Fractal dimension: A nonlinear approach |