Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG

Kemp, B.; Zwinderman, A. H.; Tuk, B.; Kamphuisen, H. A. C.; Oberyé, Janine

doi:10.1109/10.867928

Cited by 638 publications

(374 citation statements)

References 22 publications

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“…In total, 39 records sampled at 100 Hz and filtered from 0.5 to 100 Hz were employed in the experiments. Further details of the database can be found at [18,21].…”

Section: Experimental Datasetmentioning

confidence: 99%

“…The changes in sleep stages cause changes in the EEG, and these changes are also expected to be reflected in the entropy results, as observed in many other works, see [8] (pp. 193-208, Chapter 10) and [18,20,23]. The specific features computed in this study using the standard algorithms were: Fractal Dimension (FD), Detrended Fluctuation Analysis (DFA), Shannon entropy (H), Approximate Entropy (ApEn), Sample Entropy (SampEn), and Multiscale Entropy (MSE).…”

Section: Feature Extractionmentioning

confidence: 99%

“…The EEG signals used in the experiments were drawn from the SC Sleep-EDF Database [Expanded] [18]. This database is freely available through Physionet [19].…”

Section: Experimental Datasetmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Rodríguez-Sotelo

Osorio-Forero

Jiménez-Rodríguez

et al. 2014

Entropy

106

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Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.

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“…In total, 39 records sampled at 100 Hz and filtered from 0.5 to 100 Hz were employed in the experiments. Further details of the database can be found at [18,21].…”

Section: Experimental Datasetmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Rodríguez-Sotelo

Osorio-Forero

Jiménez-Rodríguez

et al. 2014

Entropy

106

View full text Add to dashboard Cite

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“…Hz and the other four is downloaded from MIT [17], the frequency of which is 100 Hz. A segment of 30 minutes was extracted from each night from the central EEG channel for spindles scoring.…”

Section: Resultsmentioning

confidence: 99%

The Application of Visual Organization Principle in the Detection of Sleep Spindles

Jin¹,

Zou²,

Zhang³

2017

JBiSE

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In order to detect the sleep spindles simply and efficiently, a novel time-domain approach to detect sleep spindles based on the principles of visual organization is proposed. The code idea of the visual organization is to organize the primary visual elements according to some rules of organization, and to form a more meaningful object of visual processing, as the input of next process. After the collected EEG is processed with the merging algorithm based on the principle of visual organization, it can extract the time-domain feature frequency and duration time better. Use these features with a simple algorithm to detect spindles achieving sensitivity of 92.5% and specificity of 98.1%, which verifies the validity of this method to detect the sleep spindles.

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“…Currently, the existing EEG databases available for research purposes mostly contain the EEG signals of motor imagination [38,39], sleep stages [40] and epilepsy [41], and only a few EEG databases related to the emotional states are available, such as Mahnob-HCI [42], the SJTU Emotion EEG Dataset (SEED) database [43] and the DEAP database [4]. This work utilized the DEAP database [4] that provided the most EEG data from participants; emotional stimuli came from video music clips, and the emotional states of the participants were defined in 2D emotion models (i.e., the arousal and the valence levels [4]), producing four quadrants of emotion: the high arousal, high valence (HAHV); the low arousal, high valence (LAHV); the low arousal, low valence (LALV); and the high arousal, low valence (HALV) emotion classes.…”

Section: Eeg Database and Its Acquisitionmentioning

confidence: 99%

Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

2017

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Abstract:The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG) signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN) as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN) as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN), and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction method is suitable for use in an EEG-based emotion recognition system.

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Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG

Cited by 638 publications

References 22 publications

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

The Application of Visual Organization Principle in the Detection of Sleep Spindles

Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

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