Comparison of fractal and power spectral EEG features: Effects of topography and sleep stages

Weiss, Béla; Clemens, Zsófia; Bódizs, Róbert; Halász, Péter

doi:10.1016/j.brainresbull.2010.12.005

Cited by 50 publications

(35 citation statements)

References 84 publications

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“…Table 2 also shows the results achieved using the same method proposed in this paper, but computing classical features employed in most of the similar sleep stages studies instead of entropy metrics: the absolute power, power asymmetry, central power, coherence, phase coherence, power ratios, and relative power. Examples of application of these features and more details about them can be found in [36,44,45]. The classification results are similar to those obtained with the entropy metrics.…”

Section: Featuresupporting

confidence: 63%

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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Section: Featuresupporting

confidence: 63%

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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“…Similarly, valence recognition with FD value features could achieve better results compared to PSD features because of their slightly higher absolute correlations. This evidence coincides with results from previous studies in the field of EEG-based affective computing [37], [38], which reported that the FD approach is superior to PSD in recognizing affective states because of the superior ability to analyze the non-linear behavior of the brain. Note that the SVM achieved better results than the other classifiers, i.e., C4.5 and MLP, and that similar results were also obtained in previous works [1].…”

Section: Discussionsupporting

confidence: 91%

Continuous Music-Emotion Recognition Based on Electroencephalogram

Thammasan

Moriyama

Fukui

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYResearch on emotion recognition using electroencephalogram (EEG) of subjects listening to music has become more active in the past decade. However, previous works did not consider emotional oscillations within a single musical piece. In this research, we propose a continuous music-emotion recognition approach based on brainwave signals. While considering the subject-dependent and changing-over-time characteristics of emotion, our experiment included self-reporting and continuous emotion annotation in the arousal-valence space. Fractal dimension (FD) and power spectral density (PSD) approaches were adopted to extract informative features from raw EEG signals and then we applied emotion classification algorithms to discriminate binary classes of emotion. According to our experimental results, FD slightly outperformed PSD approach both in arousal and valence classification, and FD was found to have the higher correlation with emotion reports than PSD. In addition, continuous emotion recognition during music listening based on EEG was found to be an effective method for tracking emotional reporting oscillations and provides an opportunity to better understand human emotional processes.

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“…Powers over band (2-4 Hz), band (4-8 Hz), band (8)(9)(10)(11)(12), band (12-18 Hz), band (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and band ( 30 Hz) were estimated with periodogram method [16]. AR model was also successfully applied in EEG based mental tasks classification [19] and other EEG applications [31].…”

Section: B Feature Extractionmentioning

confidence: 99%

“…In our work, fractal dimension models are used to quantify the complexity of EEG signals to represent the dynamic properties of brain activities. Nonlinear FD features have been proven effective in EEG studies [21]- [24]. Compared to the linear PSD features, fractal dimension features could perform better in EEG-based applications such as emotion recognition [25], neurofeedback [26], [27].…”

mentioning

confidence: 99%

Real-Time Mental Arithmetic Task Recognition From EEG Signals

Wang

Sourina

2013

IEEE Trans. Neural Syst. Rehabil. Eng.

171

102

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Abstract-Electroencephalography (EEG)-based monitoring the state of the user's brain functioning and giving her/him the visual/audio/tactile feedback is called neurofeedback technique, and it could allow the user to train the corresponding brain functions. It could provide an alternative way of treatment for some psychological disorders such as attention deficit hyperactivity disorder (ADHD), where concentration function deficit exists, autism spectrum disorder (ASD), or dyscalculia where the difficulty in learning and comprehending the arithmetic exists. In this paper, a novel method for multifractal analysis of EEG signals named generalized Higuchi fractal dimension spectrum (GHFDS) was proposed and applied in mental arithmetic task recognition from EEG signals. Other features such as power spectrum density (PSD), autoregressive model (AR), and statistical features were analyzed as well. The usage of the proposed fractal dimension spectrum of EEG signal in combination with other features improved the mental arithmetic task recognition accuracy in both multi-channel and one-channel subject-dependent algorithms up to 97.87% and 84.15% correspondingly. Based on the channel ranking, four channels were chosen which gave the accuracy up to 97.11%. Reliable real-time neurofeedback system could be implemented based on the algorithms proposed in this paper.Index Terms-Brain state, electroencephalography (EEG), fractal dimension, mental arithmetic task, neurofeedback.

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Comparison of fractal and power spectral EEG features: Effects of topography and sleep stages

Cited by 50 publications

References 84 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

Continuous Music-Emotion Recognition Based on Electroencephalogram

Real-Time Mental Arithmetic Task Recognition From EEG Signals

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