Mozart’s music between predictability and surprise: results of an experimental research based on electroencephalography, entropy and Hurst exponent

OBJECTIVE: The main goal of our work was to simultaneously study musical and electroencephalogram (EEG) signal while listening to Mozart’s K448 Sonata, a piece known for the “Mozart effect”, with the aim to better understand the reasons of beneficial effect of music on the brain. DESIGN: To this purpose, in a small sample of young healthy subjects, we examined the EEG correlates of modifications of brain activity, also applying the concepts of entropy and Hurst exponent H to K448 Sonata compared to a selection of Mozart’s excerpts, so that to expose the peculiar characteristics of this compositions in terms of predictability and surprise for the listener RESULTS: Spectral analysis showed that mean beta rhythm significantly grew during the listening to K448, and that this effect remaining immediately after, but to a lesser extent. Furthermore, we found that maximum values of entropy and lower values of H were reached by K448 compared to a selection of Mozart’s pieces. CONCLUSIONS: The results support the hypothesis of an overall effect of activation of the superior cortical functions during listening to K448, and immediately afterwards, in healthy young adults, and of a greater complexity of this sonata compared to a selection of Mozart’s pieces

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction

Residual Deficits Observed In Athletes Following Concussion: Combined Eeg And Cognitive Study

The neurocognitive sequelae of a sport-related concussion and its management are poorly defined. Emerging evidence suggests that the residual deficits can persist one year or more following a brain injury. Detecting and quantifying the residual deficits are vital in making a decision about the treatment plan and may prevent further damage. For example, improper return to play (RTP) decisions in sports such as football have proven to be associated with the further chance of recurring injury, long-term neurophysiological impairments, and worsening of brain functional activity. The reliability of traditional cognitive assessment tools is debatable, and thus attention has turned to assessments based on electroencephalogram (EEG) to evaluate subtle post-concussive alterations. In this study, we calculated neurocognitive deficits in two different datasets. One dataset contains a combination of EEG analysis with three standard post-concussive assessment tools. The data for this dataset were collected for all testing modalities from 21 adolescent athletes (seven concussive and fourteen healthy) in three different trials. Another dataset contains post-concussion eyes closed EEG signal for twenty concussed and twenty age-matched controls. For EEG assessment, along with linear frequency-based features, we introduced a set of time-frequency and nonlinear features for the first time to explore post-concussive deficits. In conjunction with traditional frequency band analysis, we also presented a new individual frequency based approach for EEG assessment. A set of linear, time-frequency and nonlinear EEG markers were found to be significantly different in the concussed group compared to their matched peers in the healthy group. Although EEG analysis exhibited discrepancies, none of the cognitive assessment resulted in significant deficits. Therefore, the evidence from the study highlight that our proposed EEG analysis and markers are more efficient at deciphering post-concussion residual neurocognitive deficits and thus has a potential clinical utility of proper concussion assessment and management. Moreover, a number of studies have clearly demonstrated the feasibility of supervised and unsupervised pattern recognition algorithms to classify patients with various health-related issues. Inspired by these studies, we hypothesized that a set of robust features would accurately differentiate concussed athletes from control athletes. To verify it, features such as power spectral, statistical, wavelet, and other nonlinear features were extracted from the EEG signal and were used as an input to various classification algorithms to classify the concussed individuals. Various techniques were applied to classify control and concussed athletes and the performance of the classifiers was compared to ensure the best accuracy. Finally, an automated approach based on meaningful feature detection and efficient classification algorithm were presented to systematically identify concussed athletes from healthy controls with a reasonable accuracy. Thus, the study provides sufficient evidence that the proposed analysis is useful in evaluating the post-concussion deficits and may be incorporated into clinical assessments for a standard evaluation of athletes after a concussion

Use of the hurst exponent for analysis of electrocortical epileptiform activity induced in rats by administration of camphor essential oil or 1,8-cineole

In this study, we investigated the presence of long-range correlation effects in the electrocortical activity of rats using the Hurst exponent (H) calculated by dispersion analysis (DA) and an aggregated variance method (AGV). A slow decline of the autocorrelation function during time expansion and the existence of a correlation between distant time points of electrocorticograms (ECoGs) were shown to be typical of various pathophysiological states. In these cases, the H values were within a 0.5 < H < 1 range. A particularly slow decay of the autocorrelation function is typical of a long-range dependence (LRD). We found that ECoGs after i.p. administrations of camphor essential oil or its main constituent, 1,8-cineole, included attacks of uncontrolled electrical discharges and showed the presence of longrange correlation effects. Such findings are in agreement with recent data obtained by other authors and suggest that initiation of seizures can be predicted by certain ECoG indices. We estimated the critical period where the H values for ECoGs containing uncontrolled electrical discharges continuously increased within a few minutes before the attack. We believe that the AGV demonstrates certain advantages over DA in calculations of the H.Ми виявляли присутність ділянок тривалої кореляції в електрокортикограмах (ЕКоГ) щурів, використовуючи побудову експоненти Харста (Н). Останню розраховували на основі дисперсійного аналізу (DA) або методу згрупованих варіанс (AGV). В ЕКоГ, зареєстрованих у різних фізіологічних станах, спостерігалися повільне затухання аутокореляційної функції при розтягненні часової шкали та кореляція між віддаленими часовими точками. У цих випадках значення Н знаходилося в діапазоні 0.5<H<1.0. Особливо повільне затухання аутокореляційної функції свідчить про наявність довгодіапазонної залежності (LRD). Ми встановили, що в ЕКоГ, зареєстрованих після внутрішньоочеревинних ін’єкцій камфорної олії або її основного активного компонента 1,8-цинеолу, були наявні спалахи неконтрольованих розрядів (епілептиформні епізоди) та прояви тривалої кореляції. Ці спостереження узгоджуються з результатами, про котрі повідомляли інші автори, та вказують на можливість передбачати можливий розвиток судомної активності. Як виявилося, критичний період, у межах котрого величина Н для ЕКоГ з наявністю епілептиформних епізодів і котрий передує розвитку судомної активності, складає декілька хвилин. Ми вважаємо, що метод AGV при розрахунку Н має преференції порівняно з DА

An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers

This paper presents an epilepsy detection method based on discrete wavelet transform (DWT) and Machine learning classifiers. Here DWT has been used for feature extraction as it provides a better decomposition of the signals in different frequency bands. At first, DWT has been applied to the EEG signal to extract the detail and approximate coefficients or different sub-bands. After the extraction of the coefficients, principal component analysis (PCA) has been applied on different sub-bands and then a feature level fusion technique is used to extract the important features in low dimensional feature space. Three classifiers namely: Support Vector Machine (SVM) classifier, K-Nearest-Neighbor (KNN) classifier, and Naive Bayes (NB) Classifiers have been used in the proposed work for classifying the EEG signals. The proposed method is tested on Bonn databases and provides a maximum of 100% recognition accuracy for KNN, SVM, NB classifiers.Comment: Accepted in International Conference on Smart Technologies for Sustainable Development (ICSTSD2021

Analysis of observed chaotic data

Thesis (Master)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2004Includes bibliographical references (leaves: 86)Text in English; Abstract: Turkish and Englishxii, 89 leavesIn this thesis, analysis of observed chaotic data has been investigated. The purpose of analyzing time series is to make a classification between the signals observed from dynamical systems. The classifiers are the invariants related to the dynamics. The correlation dimension has been used as classifier which has been obtained after phase space reconstruction. Therefore, necessary methods to find the phase space parameters which are time delay and the embedding dimension have been offered. Since observed time series practically are contaminated by noise, the invariants of dynamical system can not be reached without noise reduction. The noise reduction has been performed by the new proposed singular value decomposition based rank estimation method.Another classification has been realized by analyzing time-frequency characteristics of the signals. The time-frequency distribution has been investigated by wavelet transform since it supplies flexible time-frequency window. Classification in wavelet domain has been performed by wavelet entropy which is expressed by the sum of relative wavelet energies specified in certain frequency bands. Another wavelet based classification has been done by using the wavelet ridges where the energy is relatively maximum in time-frequency domain. These new proposed analysis methods have been applied to electrical signals taken from healthy human brains and the results have been compared with other studies

Survey analysis for optimization algorithms applied to electroencephalogram

This paper presents a survey for optimization approaches that analyze and classify Electroencephalogram (EEG) signals. The automatic analysis of EEG presents a significant challenge due to the high-dimensional data volume. Optimization algorithms seek to achieve better accuracy by selecting practical features and reducing unwanted features. Forty-seven reputable research papers are provided in this work, emphasizing the developed and executed techniques divided into seven groups based on the applied optimization algorithm particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly, and other optimizer approaches). The main measures to analyze this paper are accuracy, precision, recall, and F1-score assessment. Several datasets have been utilized in the included papers like EEG Bonn University, CHB-MIT, electrocardiography (ECG) dataset, and other datasets. The results have proven that the PSO and GWO algorithms have achieved the highest accuracy rate of around 99% compared with other techniques

EpilepIndex: A novel feature engineering tool to detect epilepsy using EEG signals

Epilepsy is a common neurological disease characterized by seizures. A person with a seizure onset can lose consciousness which in turn can lead to fatal accidents. Electroencephalogram (EEG) is a recording of the electrical signals from the brain which is used to analyse the epileptic seizures. Physical visual examination of the EEG by trained neurologists is subjective and highly difficult due to the non-linear complex nature of the EEG. This opens a window for automatic detection of epileptic seizures using machine learning methods. In this work, we have used a standard database that consists of five different sets of EEG data including the epileptic EEG. Using this data, we have devised a novel 22 possible clinically significant cases with the combination of binary and multi class type of classification problem to automatically classify epileptic EEG. As the EEG is non-linear, we have devised 11 statistically significant non-linear entropy features to extract from this database. These features are fed to 10 different classifiers of various types for each of the 22 clinically significant cases and their classification accuracy is reported for 10-fold cross validation. Random Forest and Optimized Forest classifiers reported accuracies above 90% for all 22 cases considered in this study. Such vast possible clinically significant 22 cases from the combination of the data from the database considered has not been in the literature with the best of the knowledge of the authors. Comparing with the literature, several studies have presented one or few combinations of these 22 cases in this work. In comparison to similar works, the accuracies obtained by the classifiers were highly competitive. In addition, a novel integrated epilepsy detection index named EpilepIndex (IED) is able to differentiate between epileptic EEG and a normal EEG with 100% accuracy