Analysis Of Electroencephalographic Signals Research Articles

Decoding influences of indoor temperature and light on neural activity: entropy analysis of electroencephalographic signals.

Understanding the neural responses to indoor characteristics like temperature and light is crucial for comprehending how the physical environment influences the human brain. Our study introduces an innovative approach using entropy analysis, specifically, approximate entropy (ApEn), applied to electroencephalographic (EEG) signals to investigate neural responses to temperature and light variations in indoor environments. By strategically placing electrodes over specific brain regions linked to temperature and light processing, we show how ApEn can be influenced by indoor factors. We also integrate heart indices from a multi-sensor bracelet to create a machine learning classifier for temperature conditions. Results showed that in anterior frontal and temporoparietal areas, neutral temperature conditions yield higher ApEn values. The anterior frontal area showed a trend of gradually decreasing ApEn values from neutral to warm conditions, with cold being in an intermediate position. There was a significant interaction between light and site factors, only evident in the temporoparietal region. Here, the neutral light condition had higher ApEn values compared to blue and red light conditions. Positive correlations between anterior frontal ApEn and thermal comfort scores suggest a link between entropy and perceived thermal comfort. Our quadratic SVM classifier, incorporating entropy and heart features, demonstrates strong performance (until 90% in terms of AUC, accuracy, sensitivity, and specificity) in classifying temperature sensations. This study offers insights into neural responses to indoor factors and presents a novel approach for temperature classification using EEG entropy and heart features.

Classification of Human Concentration Levels Based on Electroencephalography Signals

Concentration denotes the capability to direct one's attention to a specific subject matter. Presently, within the era characterized by an overwhelming abundance of information inundating human existence, distractions frequently impede human concentration, thereby influencing the depth of knowledge acquisition. Various elements contribute to the decline in human concentration, including diminished metabolic states, inadequate sleep, and engaging in multiple tasks simultaneously. The cognitive state of an individual during the process of thinking can be assessed through the analysis of electroencephalography signals. The primary objective of this investigation is to facilitate experts' interpretation of electroencephalography signal outcomes for categorizing concentration levels. The dataset utilized in this examination comprises unprocessed EEG data obtained from observing individuals in both relaxation and concentration states. After data preprocessing, feature extraction is executed, and classification is performed using the Support Vector Machine technique. The outcome of this study reveals an accuracy rate of 84%. These developments allow for continual monitoring of brain function, an enhanced comprehension of cerebral activities, and increased operational efficacy of end-effectors. The implications of these advancements on prospective research opportunities are evident in the potential for more accurate diagnosis of neurological disorders and the progression of sophisticated BCI applications designed to support healthcare and monitor cognitive states. The evolution of EEG technology is paving the way for novel research pathways in neuroscience and human-computer interaction.

Pilot Study on Analysis of Electroencephalography Signals from Children with FASD with the Implementation of Naive Bayesian Classifiers.

In this paper Naive Bayesian classifiers were applied for the purpose of differentiation between the EEG signals recorded from children with Fetal Alcohol Syndrome Disorders (FASD) and healthy ones. This work also provides a brief introduction to the FASD itself, explaining the social, economic and genetic reasons for the FASD occurrence. The obtained results were good and promising and indicate that EEG recordings can be a helpful tool for potential diagnostics of FASDs children affected with it, in particular those with invisible physical signs of these spectrum disorders.

Emotion Recognition by Correlating Facial Expressions and EEG Analysis

Emotion recognition is a fundamental task that any affective computing system must perform to adapt to the user’s current mood. The analysis of electroencephalography signals has gained notoriety in studying human emotions because of its non-invasive nature. This paper presents a two-stage deep learning model to recognize emotional states by correlating facial expressions and brain signals. Most of the works related to the analysis of emotional states are based on analyzing large segments of signals, generally as long as the evoked potential lasts, which could cause many other phenomena to be involved in the recognition process. Unlike with other phenomena, such as epilepsy, there is no clearly defined marker of when an event begins or ends. The novelty of the proposed model resides in the use of facial expressions as markers to improve the recognition process. This work uses a facial emotion recognition technique (FER) to create identifiers each time an emotional response is detected and uses them to extract segments of electroencephalography (EEG) records that a priori will be considered relevant for the analysis. The proposed model was tested on the DEAP dataset.

Development of single-channel electroencephalography signal analysis model for real-time drowsiness detection : SEEGDD.

Drowsiness detection is essential in some critical tasks such as vehicle driving, crane operating, mining blasting, and so on, which can help minimize the risks of inattentiveness. Electroencephalography (EEG) based drowsiness detection methods have been shown to be effective. However, due to the non-stationary nature of EEG signals, techniques such as signal transformation and sub-band extraction are increasingly being used to automatically classify awake and drowsy states. Most of these procedures require high computation time. In this paper, analytical and single-feature computation are used to propose a single-channel EEG-based drowsiness detection method to overcome this. Physionet sleep dataset and the simulated virtual driving dataset were used to test the proposed model. When compared to existing work, the proposed approach yields better results.

Sleep Problems in Narcolepsy and the Role of Hypocretin/Orexin Deficiency.

Since its description in the 19th century, narcolepsy type 1 (NT1) has been considered as a model sleep disorder, and after the discovery of rapid eye movement (REM) sleep onset in the disorder, a gateway to understanding REM sleep. The discovery that NT1 is caused by hypocretin/orexin deficiency, together with neurochemical studies of this system, has helped to establish how this neuropeptide regulates the organization of sleep and wake in humans. Current analyses suggest that the main functions of the hypocretin/orexin system are (1) maintenance of wakefulness in the face of moderate sleep deprivation; (2) passive wake promotion, especially in the evening, driven by the circadian clock; (3) inhibition of REM sleep, with possible differential modulating effects on various subcomponents of the sleep-stage, explaining REM sleep dissociation events in NT1. Narcolepsy is also associated with an inability to consolidate sleep, a more complex phenotype that may result from secondary changes or be central to the role of hypocretin in coordinating the activity of other sleep- and wake-promoting systems. Novel technologies, such as the use of deep learning analysis of electroencephalographic signals, is revealing a complex pattern of sleep abnormalities in human narcolepsy that can be used diagnostically. The availability of novel devices measuring sleep 24 h per day also holds promise to provide new insights into how brain electrical activity and muscle tone are regulated by hypocretin.

Wavelet Analysis of Electroencephalography Signals of Visual Emotion Induction in Schizophrenia Patients

Wavelet Analysis of Electroencephalography Signals of Visual Emotion Induction in Schizophrenia Patients

Analysis of Electroencephalography Signals on the Contents of Cognitive Function Game: Attention and Memory

In the current study, contents of cognitive function game were developed, and variations in Electroencephalography signals were measured and compared before performing the game tasks and during performing the game. The study sought to assess how much the game contents activated brain to see if they were suitable for cognitive functional training. For Electroencephalography signal analysis, power spectral analysis was implemented to classify signals according to frequency. To test signal variation according to the degree of brain activation before and after performing the game, variation comparison and paired t-test were conducted. Results showed that there was reduction in α wave signaling which implied that the subjects concentrate on the content, and increase in β wave signifying that they were engaged in cognitive activities such as remembering and assessing. Consequentially, the produced contents in this study are expected to be useful for cognitive functional training.

Asleep or awake? That is the question… A review of techniques available for monitoring the depth of anaesthesia

Introduction: The problem of intraoperative awareness concerns about 0.1%–0.2% of patients .The perfect method to evaluate the depth of sleep should be objective, so that the response is quick and precise – to increase or decrease the depth of anaesthesia. More scales originated in order to detect cases of intraoperative awareness. Also, new equipment was built so that anaesthesiologists could properly monitor the depth of anaesthesia. Aim: The aim is to describe methods and devices monitoring the depth of anaesthesia. Material and methods: This work was based on the available literature and the experience of the authors. Results and discussion: Recently a few devices were constructed, all of which can be divided into passive and active systems. Passive systems assess the collected data, while active ones first stimulate and then receive and process data. Passive systems use computer analysis of electroencephalographic signal, and some of them additionally evaluate alterations of frontal electromyogram. According to some, monitors currently available on the market show around 80% effectiveness in preventing intraoperative awareness. Other researchers showed that evidence of their effect on intraoperative awareness is limited. Conclusions: It seems that when it comes to the effect of anaesthetic agents on such a precise organ as the brain, there is still much to discover. As long as we do not fully know what awareness is and what mechanisms influence the state of staying awake and of anaesthetic sleep, and on which levels it happens, we will not be able to prevent intraoperative awareness effectively.

Analysis of the Emotions’ Brainwaves

Currently in Peru, patients with degenerative diseases, such as Amyotrophic Lateral Sclerosis (ALS) have lost of communication ability. Many researchers’ papers that establish basic communication system for these patients. It is also essential to know their feelings or state of mind through their emotions, in this study, we present an analysis of electroencephalographic signals (EEG) applied to emotions such as fear, tenderness, happiness and surprised; it was used linear discriminant analysis (LDA) to get the identification and classification of the 4 emotions with a success rate of 63.36% on average.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics.

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the “ENVIHAB” facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.

An Amplitude Modulation of Cerebral Rhythms based Method in a Motor Task BCI Paradigm

Quantitative evaluation based on amplitude modulation analysis of electroencephalographic signals is proposed for a brain computer interface paradigm. The method allows characterization of the interaction effects of different frequency bands in the electroencephalographic rhythms during motor tasks. A new index was proposed and computed to be a measure of the amplitude modulation. Built on this index, features vector are established for training different classification algorithms. Signals recorded from 50 subjects revealed important differences in amplitude modulations between motor tasks. Most notably, Theta modulation of the Theta and Alpha rhythms proved to be reliable discriminant features between different mental tasks.

Application of the Stockwell Transform to Electroencephalographic Signal Analysis during Gait Cycle.

The analysis of electroencephalographic signals in frequency is usually not performed by transforms that can extract the instantaneous characteristics of the signal. However, the non-steady state nature of these low voltage electrical signals makes them suitable for this kind of analysis. In this paper a novel tool based on Stockwell transform is tested, and compared with techniques such as Hilbert-Huang transform and Fast Fourier Transform, for several healthy individuals and patients that suffer from lower limb disability. Methods are compared with the Weighted Discriminator, a recently developed comparison index. The tool developed can improve the rehabilitation process associated with lower limb exoskeletons with the help of a Brain-Machine Interface.

Self-Grounded Vision: Hand Ownership Modulates Visual Location through Cortical β and γ Oscillations.

Vision is influenced by the body: in the Taylor illusion, the size of an afterimage projected on one's hand changes according to tactile and proprioceptive signals conveying hand position. Here, we report a new phenomenon revealing that the perception of afterimages depends not only on bodily signals, but also on the sense of self. Relying on the rubber hand illusion, we manipulated hand ownership, so that participants embodied a rubber hand placed next to their own hand. We found that visual afterimages projected on the participant's hand drifted laterally, only when the rubber hand was embodied. Electroencephalography revealed spectral dissociations between somatic and visual effects, and higher γ connectivity along the dorsal visual pathways when the rubber hand was embodied.

집중 시각자극 유발전위의 순차적 비선형 RQA 분석

The analysis of electroencephalographic signal associated with the attention is essential for the understanding of human cognition. In this paper, the characteristic differences between the attention and inattention status in the brain were inspected by nonlinear analysis. The recurrence quantification analysis was applied to the relatively small number of samples of evoked potential having time varying characteristics, where the recurrence plot (RP), the color recurrence plot (CRP), and mean and time-sequential trend parameters were extracted. The dimension and the time delay in phase transformation can be determined by the paired set of extracted parameters. It is observed from RP, CRP, and parameters that the brain dynamics in attention is more complex than that in the inattention, as well as the synchronized brain response is stable in the mean sense but locally time varying. It is feasible that the non-linear analysis method can be useful for the analysis of complex brain dynamics associated during visual attentional task.

Cross-conditional entropy and coherence analysis of pharmaco-EEG changes induced by alprazolam

Quantitative analysis of electroencephalographic signals (EEG) and their interpretation constitute a helpful tool in the assessment of the bioavailability of psychoactive drugs in the brain. Furthermore, psychotropic drug groups have typical signatures which relate biochemical mechanisms with specific EEG changes. To analyze the pharmacological effect of a dose of alprazolam on the connectivity of the brain during wakefulness by means of linear and nonlinear approaches. EEG signals were recorded after alprazolam administration in a placebo-controlled crossover clinical trial. Nonlinear couplings assessed by means of corrected cross-conditional entropy were compared to linear couplings measured with the classical magnitude squared coherence. Linear variables evidenced a statistically significant drug-induced decrease, whereas nonlinear variables showed significant increases. All changes were highly correlated to drug plasma concentrations. The spatial distribution of the observed connectivity changes clearly differed from a previous study: changes before and after the maximum drug effect were mainly observed over the anterior half of the scalp. Additionally, a new variable with very low computational cost was defined to evaluate nonlinear coupling. This is particularly interesting when all pairs of EEG channels are assessed as in this study. Results showed that alprazolam induced changes in terms of uncoupling between regions of the scalp, with opposite trends depending on the variables: decrease in linear ones and increase in nonlinear features. Maps provided consistent information about the way brain changed in terms of connectivity being definitely necessary to evaluate separately linear and nonlinear interactions.

Method for parallel construction of a committee of decision tree for processing the electroencephalography signals

A method for parallel construction of a classifier ensemble for solving the problem of localization of neuron sources within the brain on the basis of the analysis of electroencephalography signals is described. The idea of the proposed parallel numerical method consists in the consideration of the source parameters as attributes of decision tress constructed in parallel. The method is based on formation of a training data set from an experimental signal and construction of a classifier on the basis of the value of error of the potential, that is, the difference between the measured and model values of the potential. The efficiency of parallelization of the localization problem, namely, the data distribution between processors, and the distributed training of the ensembles of decision trees are considered. Analysis of the scalability of the problem of construction of a classifier ensemble with a increase in the number of processors in the course of solution of the problem of localization of a neuron source on multiprocessor computational complexes is presented. The parallel source localization algorithm is developed for architectures with either common or distributed memory. The algorithm is realized using the MPI technology; a hybrid model of parallel calculations using MPI and OpenMPI is also discussed.

Methods of electroencephalographic signal analysis for detection of small hidden changes.

The aim of this study was to select and evaluate methods sensitive to reveal small hidden changes in the electroencephalographic (EEG) signal. Two original methods were considered.Multifractal method of scaling analysis of the EEG signal based on the length distribution of low variability periods (LDLVP) was developed and adopted for EEG analysis. The LDLVP method provides a simple route to detecting the multifractal characteristics of a time-series and yields somewhat better temporal resolution than the traditional multifractal analysis.The method of modulation with further integration of energy of the recorded signal was applied for EEG analysis. This method uses integration of differences in energy of the EEG segments with and without stressor.Microwave exposure was used as an external stressor to cause hidden changes in the EEG. Both methods were evaluated on the same EEG database. Database consists of resting EEG recordings of 15 subjects without and with low-level microwave exposure (450 MHz modulated at 40 Hz, power density 0.16 mW/cm2). The significant differences between recordings with and without exposure were detected by the LDLVP method for 4 subjects (26.7%) and energy integration method for 2 subjects (13.3%).The results show that small changes in time variability or energy of the EEG signals hidden in visual inspection can be detected by the LDLVP and integration of differences methods.

Seizure anticipation: Are neurophenomenological approaches able to detect preictal symptoms?

Analysis of electroencephalographic signals and several brain imaging studies suggest that a preictal state precedes the onset of seizures. In this study, we used phenomenological strategies to detect modifications in patients’ experience before their seizures. We observed that patients with partial epilepsy feeling an aura ( n = 9) frequently experienced prodromes ( n = 6). Prodromes were subtle preictal symptoms, varying among patients and having common negative features. They were generally continuous before seizures and could last hours, whereas auras were sudden and intermittent. All patients were able to recognize facilitating factors. We also found that patients spontaneously develop cognitive countermeasures to avoid facilitating factors ( n = 6), to prevent a seizure ( n = 1) or to interrupt a seizure ( n = 5). Prodromes are not specific enough for clinical use, but could refine the behavioral strategies used in the treatment of epilepsy and the pathophysiology of the preictal state.

Chaotic map clustering algorithm for EEG analysis

The non-parametric chaotic map clustering algorithm has been applied to the analysis of electroencephalographic signals, in order to recognize the Huntington's disease, one of the most dangerous pathologies of the central nervous system. The performance of the method has been compared with those obtained through parametric algorithms, as K-means and deterministic annealing, and supervised multi-layer perceptron. While supervised neural networks need a training phase, performed by means of data tagged by the genetic test, and the parametric methods require a prior choice of the number of classes to find, the chaotic map clustering gives a natural evidence of the pathological class, without any training or supervision, thus providing a new efficient methodology for the recognition of patterns affected by the Huntington's disease.