Rogue bioelectrical waves in the brain: the Hurst exponent as a potential measure for presurgical mapping in epilepsy

Abstract Background Epilepsy is a neurological disorder from which almost 50 million people have been suffering. These statistics indicate the importance of epilepsy diagnosis. Electroencephalogram (EEG) signals analysis is one of the most common methods for epilepsy characterization; hence, various strategies were applied to classify epileptic EEGs. Methods In this paper, four different nonlinear features such as Fractal dimensions including Higuchi method (HFD) and Katz method (KFD), Hurst exponent, and L-Z complexity measure were extracted from EEGs and their frequency sub-bands. The features were ranked later by implementing Relieff algorithm. The ranked features were applied sequentially to three different classifiers (MLPNN, Linear SVM, and RBF SVM). Results According to the dataset used for this study, there are five classification problems named ABCD/E, AB/CD/E, A/D/E, A/E, and D/E. In all cases, MLPNN was the most accurate classifier. Its performances for mentioned classification problems were 99.91%, 98.19%, 98.5%, 100% and 99.84%, respectively. Conclusion The results demonstrate that KFD is the highest-ranking feature; In addition, beta and theta sub-bands are the most important frequency bands because, for all cases, the top features were KFDs extracted from beta and theta sub-bands. Moreover, high levels of accuracy have been obtained just by using these two features which reduce the complexity of the classification.

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Magneto-encefalografie

Acta Neuropsychiatrica ◽

10.1017/s0924270800037091 ◽

1996 ◽

Vol 8 (3) ◽

pp. 64-70

Author(s):

M.J. Peters ◽

F. Reinders

Keyword(s):

Magnetic Field ◽

Epileptic Focus ◽

Current Dipole ◽

Non Invasive ◽

Active Population ◽

Presurgical Mapping ◽

The Brain ◽

Weak Fields ◽

The Magnetic Field ◽

Realistic Shape

SummaryA magnetoencephalogram (MEG) is the registration of the magnetic field in points near the head. Because MEG's are weak fields, they have to be measured by means of superconducting sensors. The electric active population of neurons can be computed from the distribution of the magnetic field at a certain instant of time. This is called the inverse problem. In order to solve this probem, both the generators and the head have to be modelled. Usually, a patch of active neurons is modelled as a current dipole. Commonly, the head is described by three compartments, representing the brain, the skull and the scalp. The compartments may have the shape of spheres or they may have a realistic shape. Integration of EEG and MEG with MRI leads to a technique for functional imaging of the brain with a time resolution of one millisecond and a spatial resolution of one centimetre. Clinical applications are the non-invasive localization of an epileptic focus or the presurgical mapping of the sensorimotor cortex.

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Evaluation of the Coupling between the Brain and Facial Muscles Reactions to Moving Visual Stimuli

Fluctuation and Noise Letters ◽

10.1142/s0219477521500425 ◽

2021 ◽

pp. 2150042

Author(s):

Mirra Soundirarajan ◽

Ondrej Krejcar ◽

Hamidreza Namazi

Keyword(s):

Visual Stimulus ◽

Hurst Exponent ◽

Daily Life ◽

Brain Activity ◽

Pearson Correlation ◽

Visual Stimuli ◽

Physiological Signals ◽

Eeg Signals ◽

Facial Muscles ◽

The Brain

Since the brain regulates our facial reactions, there should be a relationship between their activities. Moving (dynamic) visual stimuli are an important type of visual stimuli that we are dealing with in our daily life. Since EMG and EEG signals contain information, we evaluated the coupling of the reactions of facial muscles and brain to various moving visual stimuli by analysis of the embedded information in these signals. We benefited from Shannon entropy to quantify the information. The results showed that a decrement in the information of visual stimulus is mapped on a decrement of the information of EMG and EEG signals, and therefore, the activities of facial muscles and the brain are correlated (Pearson correlation [Formula: see text]). Besides, the analysis of the Hurst exponent of EEG signals demonstrated that increasing the information of EEG signals causes the increment in its memory. This method can also be used to evaluate the coupling among other organs’ activity and brain activity by analysis of related physiological signals.

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