Parameters analyzed of Higuchi's fractal dimension for EEG brain signals

Vega, Christian Flores; Noel, Julien

doi:10.1109/sps.2015.7168285

Cited by 13 publications

(7 citation statements)

References 21 publications

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“…The PCA is introduced to be able to exploit all the information in features but to reduce the training time of models. The feature sets based on chaos theory and fractal dimensions are selected because they have been used in studying EEG signals due to its natural non-linear a chaotic behavior [11,[32][33][34] and in studying epilepsy in EEG signals [25,35]. The whole procedure to extract a feature vector from a signal instance is explained in Fig.…”

Section: Feature Setsmentioning

confidence: 99%

Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals

Hernández-Del-Toro

Reyes-García

Villaseñor-Pineda

2021

Biomedical Signal Processing and Control

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An asynchronous Brain-Computer Interface (BCI) based on imagined speech is a tool that allows to control an external device or to emit a message at the moment the user desires to by decoding EEG signals of imagined speech. In order to correctly implement these types of BCI, we must be able to detect from a continuous signal, when the subject starts to imagine words. In this work, five methods of feature extraction based on wavelet decomposition, empirical mode decomposition, frequency energies, fractal dimension and chaos theory features are presented to solve the task of detecting imagined words segments from continuous EEG signals as a preliminary study for a latter implementation of an asynchronous BCI based on imagined speech. These methods are tested in three datasets using four different classifiers and the higher F 1 scores obtained are 0.73, 0.79, and 0.68 for each dataset, respectively. This results are promising to build a system that automatizes the segmentation of imagined words segments for latter classification.

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Section: Feature Setsmentioning

confidence: 99%

Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals

Hernández-Del-Toro

Reyes-García

Villaseñor-Pineda

2021

Biomedical Signal Processing and Control

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“…The slope of a plot log(L(k)) against log(1/k) is equal to FD, which can be calculated using the least-squares linear fitting. In this study k = 217 was set, which comes from half of the number of data samples, N, as explained in Section 2.2 [47]. Accordingly, the higher HFD value is, the higher EEG complexity is.…”

Section: Higuchi's Fractal Dimension (Hfd)mentioning

confidence: 99%

Classification of Pain Event Related Potential for Evaluation of Pain Perception Induced by Electrical Stimulation

Tripanpitak

Viriyavit

Huang

et al. 2020

Sensors

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Variability in individual pain sensitivity is a major problem in pain assessment. There have been studies reported using pain-event related potential (pain-ERP) for evaluating pain perception. However, none of them has achieved high accuracy in estimating multiple pain perception levels. A major reason lies in the lack of investigation of feature extraction. The goal of this study is to assess four different pain perception levels through classification of pain-ERP, elicited by transcutaneous electrical stimulation on healthy subjects. Nonlinear methods: Higuchi's fractal dimension, Grassberger-Procaccia correlation dimension, with auto-correlation, and moving variance functions were introduced into the feature extraction. Fisher score was used to select the most discriminative channels and features. As a result, the correlation dimension with a moving variance without channel selection achieved the best accuracies of 100% for both the two-level and the three-level classification but degraded to 75% for the four-level classification. The best combined feature group is the variance-based one, which achieved accuracy of 87.5% and 100% for the four-level and three-level classification, respectively. Moreover, the features extracted from less than 20 trials could not achieve sensible accuracy, which makes it difficult for an instantaneous pain perception levels evaluation. These results show strong evidence on the possibility of objective pain assessment using nonlinear feature-based classification of pain-ERP.

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“…The usage of their HFD spectrum in combination with other features improved the task recognition accuracy in both multi-channel and one-channel subject-dependent algorithms up to 97.87 percent and 84.15 percent, correspondingly 34 . Vega and Noel 54 also reported HFD as a robust tool for cognitive task discrimination between five states: relaxed state, multiplication, imagining writing a letter, imagining rotation of an object, and erasing and redrawing figures.…”

Section: Introductionmentioning

confidence: 99%

“…Our study is the first one aiming to discriminate the cortical functions of math experts from those of novices during long and complex math tasks. Since the pioneering nature of our study, we decided to focus only on one type of features, and based on the previous literature, we chose HFD as the most suitable in distinguishing different cognitive states 43 , 52 , 54 . The math demonstrations of this study with a duration of up to 1 min form a part of the current trend in investigating the brain with naturalistic stimuli.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear and machine learning analyses on high-density EEG data of math experts and novices

Poikonen

Zaluska

Wang

et al. 2023

Sci Rep

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Current trend in neurosciences is to use naturalistic stimuli, such as cinema, class-room biology or video gaming, aiming to understand the brain functions during ecologically valid conditions. Naturalistic stimuli recruit complex and overlapping cognitive, emotional and sensory brain processes. Brain oscillations form underlying mechanisms for such processes, and further, these processes can be modified by expertise. Human cortical functions are often analyzed with linear methods despite brain as a biological system is highly nonlinear. This study applies a relatively robust nonlinear method, Higuchi fractal dimension (HFD), to classify cortical functions of math experts and novices when they solve long and complex math demonstrations in an EEG laboratory. Brain imaging data, which is collected over a long time span during naturalistic stimuli, enables the application of data-driven analyses. Therefore, we also explore the neural signature of math expertise with machine learning algorithms. There is a need for novel methodologies in analyzing naturalistic data because formulation of theories of the brain functions in the real world based on reductionist and simplified study designs is both challenging and questionable. Data-driven intelligent approaches may be helpful in developing and testing new theories on complex brain functions. Our results clarify the different neural signature, analyzed by HFD, of math experts and novices during complex math and suggest machine learning as a promising data-driven approach to understand the brain processes in expertise and mathematical cognition.

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Parameters analyzed of Higuchi's fractal dimension for EEG brain signals

Cited by 13 publications

References 21 publications

Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals

Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals

Classification of Pain Event Related Potential for Evaluation of Pain Perception Induced by Electrical Stimulation

Nonlinear and machine learning analyses on high-density EEG data of math experts and novices

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