Search results for "Eeg data"
showing 9 items of 9 documents
Group analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition
2019
Abstract Background Ongoing EEG data are recorded as mixtures of stimulus-elicited EEG, spontaneous EEG and noises, which require advanced signal processing techniques for separation and analysis. Existing methods cannot simultaneously consider common and individual characteristics among/within subjects when extracting stimulus-elicited brain activities from ongoing EEG elicited by 512-s long modern tango music. New method Aiming to discover the commonly music-elicited brain activities among subjects, we provide a comprehensive framework based on fast double-coupled nonnegative tensor decomposition (FDC-NTD) algorithm. The proposed algorithm with a generalized model is capable of simultaneo…
A deep learning framework for automatic diagnosis of unipolar depression.
2019
Abstract Background and purpose In recent years, the development of machine learning (ML) frameworks for automatic diagnosis of unipolar depression has escalated to a next level of deep learning frameworks. However, this idea needs further validation. Therefore, this paper has proposed an electroencephalographic (EEG)-based deep learning framework that automatically discriminated depressed and healthy controls and provided the diagnosis. Basic procedures In this paper, two different deep learning architectures were proposed that utilized one dimensional convolutional neural network (1DCNN) and 1DCNN with long short-term memory (LSTM) architecture. The proposed deep learning architectures au…
Epileptic seizures and cerebrovascular disease
1989
- A series of 88 patients with completed stroke was selected in which heralding, early and late seizures were distinguished according to their onset. Relationships between CT scan and clinical EEG data are discussed with particular emphasis on possible mechanisms of seizures. Thus, small emboli or haemodynamic factors are stressed in the cases of heralding seizures, metabolic disturbances with cytotoxic effects in early seizures and chronic epileptic focus in late seizures.
Automatic SCSB analysis of motor and autonomic nervous functions compared with sleep stages
1996
All-night recordings of respiration, ballistocardiogram, and body movements were obtained using the static charge-sensitive bed (SCSB) and automatically analysed data were compared with sleep stages. The mean sum of eight SCSB variability parameters was lowest in slow wave sleep (SWS), higher during stage 2 (S2), and highest in REM sleep. The sum scores of the parameters with the highest correlations with the EEG data were classified into quiet (QS), intermediate (IS) and active (AS) states. SCSB signals during wakefulness, stage 1 and REM sleep were mostly scored as AS, whereas in S2 and especially in SWS they were scored as QS or IS. The SCSB is an easy and inexpensive tool for conducting…
EEG data acquisition system based on asynchronous sigma-delta modulator
2012
This paper describes a multichannel mobile EEG data acquisition system that consists of on-head sensors with built in electroencephalogram (EEG) signal amplifier, asynchronous sigma-delta modulator (ASDM) for analog to digital conversion and 434MHz On-Off keying (OOK) wireless data transmitter. A prototype circuit has been designed and fabricated in a 11×16mm cylinder package. After receiving the signal, appropriate processing is applied in order to reconstruct the brain wave signals.
A method for extracting subspace of deterministic sources from EEG data
2008
In this paper, an algorithm for separating linear subspaces of time-locked brain responses and other noise sources in multichannel electroencephalography data is proposed. The search criterion used by method discriminates time-locked brain components and noise components on the basis of the assumed deterministic behavior that the time-locked brain sources obey. The comprehensive derivation of the method is given together with the description and the analysis of the results of the method's application to simulated and real EEG data sets. The possibilities of improving the results are also discussed.
Extraction of ERP from EEG data
2007
In this article, a simple but novel technique for extracting a linear subspace related to event related potentials (ERPs) from ElectroEncephaloGraphy (EEG) data is introduced. The technique consists of a sequence of basic linear operations applied to multidimensional EEG data in a problem-specific manner. The derivation of the proposed technique is given and results with real data are described together with overall conclusions.
Assessment of nonnegative matrix factorization algorithms for electroencephalography spectral analysis.
2020
AbstractBackgroundNonnegative matrix factorization (NMF) has been successfully used for electroencephalography (EEG) spectral analysis. Since NMF was proposed in the 1990s, many adaptive algorithms have been developed. However, the performance of their use in EEG data analysis has not been fully compared. Here, we provide a comparison of four NMF algorithms in terms of accuracy of estimation, stability (repeatability of the results) and time complexity of algorithms with simulated data. In the practical application of NMF algorithms, stability plays an important role, which was an emphasis in the comparison. A Hierarchical clustering algorithm was implemented to evaluate the stability of NM…
Recognition of rapid-eye-movement sleep from single-channel EEG data by artificial neural networks: a study in depressive patients with and without a…
1996
An automatic procedure for the online recognition of REM sleep appears to be a necessary tool for selective REM sleep deprivation in depressive patients. To develop such a procedure we applied an artificial neural network to preprocessed single-channel EEG activity. EOG and EMG information was purposely not provided as input to the network. A generalized back-propagation algorithm was used for computer simulation. The sleep profile scored manually according to Rechtschaffen and Kales served as the desired output during the training period and as standard for the judgement of the network output during working mode. Polysomnographic recordings from 5 healthy subjects were pooled to train the …