[PDF] Top 20 A sleep monitoring method with EEG signals
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A sleep monitoring method with EEG signals
... is a record taken from the ”Physio Bank” [3], in the form EDF ”European Data Format”; EDF is a file that contains separate lines ac- cording to the ...several signals; in our case he can present an ... Voir le document complet
9
EEG functional connectivity prior to sleepwalking : evidence of interplay between sleep and wakefulness
... is a well-established measure of linear relationship between two time series at specific ...occurs with a non-zero time lag, it is only sensitive to signals that are time-lagged to each other ... Voir le document complet
36
Multivariate Bayesian classification of epilepsy EEG signals
... 256Hz EEG recordings from paediatric subjects suffering from intractable seizures [4, ...contains a seizure event, whose onset time has been labeled by an expert ...extract a short epoch from each ... Voir le document complet
7
Manifold-regression to predict from MEG/EEG brain signals without source modeling
... and a biophysics-informed method based on the MNE source imaging technique ...MNE method essentially consists in a standard Tikhonov regularized inverse solution and is therefore linear (See ... Voir le document complet
18
Decoding intracranial EEG data with multiple kernel learning method
... r a c t Background: Machine learning models have been successfully applied to neuroimaging data to make predictions about behavioral and cognitive states of ...contains a rich spectrum of signals ... Voir le document complet
10
Multi-array EEG signals mapped with three dimensional images for clinical epilepsy studies
... whom a surgical intervention must be ...into a multimodal platform that should meet the requirements of the monitoring constraints (signal and real time video observations with the possibility ... Voir le document complet
9
Automatic seizure detection based on imaged-EEG signals through fully convolutional networks
... is a routine process in epilepsy units requiring manual intervention of well‑ trained ...automatic method to detect epileptic seizures using an imaged‑EEG representation of brain ...analyzed ... Voir le document complet
14
2020 — Unobtrusive sleep monitoring using bed-sheet pressure sensors
... provide a reliable estimate of sleep/wake states and further sleep parameters and behaviours such as sleep latency, TST, sleep efficiency, PLM index, and awakenings by detecting and ... Voir le document complet
154
Brain source localization using a physics-driven structured cosparse representation of EEG signals
... proposed a brain source localization method, named CoRE, which uses a physics-driven structured cosparse rep- resentation of EEG ...using a spherical head model and a ... Voir le document complet
7
Multivariate Bayesian classification of epilepsy EEG signals
... presented a new multivariate Bayesian classifica- tion method to detect epileptic seizure events in EEG ...The method is based on a multilevel 2D wavelet decompo- sition that captures ... Voir le document complet
6
A Robust Method for Drilling Monitoring using Acoustic Emission
... process monitoring have been obtained with AE sensors mounted on the ...of a study concerning the drilling of carbon steel and nodular gray iron shows that distance from the AE sensors to the hole as ... Voir le document complet
7
Basis Selection for Increased Interclass Separability of EEG Signals
... selection method for the classification of EEG ...classifying signals associated with real and imaginary hand motion of a healthy ... Voir le document complet
3
A comparative study of different artefact removal algorithms for EEG signals acquired during functional MRI.
... analysing a block-design might be related to the fact that interictal spikes are not orthogonal to residual spikes of ...artefacts with the frequency based (FT) method (spikes are much more evident ... Voir le document complet
20
Fast statistical model-based classification of epileptic EEG signals
... adopted a supervised testing approach and used the 39 signal segments described above to train and test the ...on a leave-one-out ...parameters) with data from 13 seizure signals and 26 ... Voir le document complet
14
A deep learning architecture to detect events in EEG signals during sleep
... such a dedicated neural net- work architecture to detect any type of event over the sleep EEG ...on a convolu- tional neural network which extracts high-level features from the raw input ... Voir le document complet
7
Domain adaptation with optimal transport improves EEG sleep stage classifiers
... i.e. monitoring the progress of ...] with 5 EEG like channels. It was trained by minimizing a cross-entropy loss weighted by the inverse of the stage proportions in order to mimic the balanced ... Voir le document complet
5
EEG Biometrics During Sleep and Wakefulness: Performance Optimization and Security Implications
... by EEG, it surfaces as a positive deflection in voltage with a delay between stimulus and response of 250 to 500 ms” (Polich, ...obtain a VEP, subjects are instructed to watch a ... Voir le document complet
103
Monitoring Pilot’s Mental Workload Using ERPs and Spectral Power with a Six-Dry-Electrode EEG System in Real Flight Conditions
... on EEG signals from each ...of EEG recording, in order to use signals for calibration that were not used for the ...features, EEG signals were band-pass filtered ([1 15 Hz]), ... Voir le document complet
15
Exploration of multivariate EEG /MEG signals using non-stationary models
... of a set of time correlation ...diversity with a hybrid method, consisting of the non-Gaussian ICA of concatenated short-time Fourier ... Voir le document complet
145
Topography-Time-Frequency Atomic Decomposition for Event-Related M/EEG Signals.
... new method presented here is designed to track fluctuations in brain electromagnetic activity, for any given set of frequency ...Our method has several original features. First, we introduce a ... Voir le document complet
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