Electroencephalography, commonly called 'EEG', estimates through the application of electrodes, the electrical activity of the brain (which is the sum of the electrical activity of each neuron). In recent years, with the goal of making more reliable the EEG, many researchers have turned their interest in the development of tools, methods and software. This thesis describes some best procedures for the experimental design, data visualization and descriptive or inferential statistical analysis. The application of statistical models to single or multiple subjects study-design are also described, including parametric and non-parametric approaches. Methods for processing multivariate data (PCA, ICA, clustering) were described. Re-sampling methods (bootstrap) using many randomly software-generated samples were also described. The aim of this work is to provide, with statistical concepts and examples, information on the qualitative and quantitative approaches related to the electroencephalographic signals. The work consists into three parts: INTRODUTION TO ELECTROENCEPHALOGRAPHY (GENERAL CHARACTERISTICS); DATA MINING AND STATISTICAL ANALYSIS; EXPERIMENTAL STUDY DESIGNS. The six works included in the section called “EXPERIMENTAL STUDY DESIGNS” analyze EEG alterations in the protocols: Electrocortical activity in dancers and non-dancers listening to different music genre and during imaginative dance motor activity; Electrocortical activity during monosynaptic reflex in athletes; Monitoring of electrocortical activity for evaluation of seasickness; Electrocortical activity in different body positions; Electrocortical activity in athletes and non-athletes during body balance tasks; Electrocortical responses in volunteers with and without specific experience watching movies including the execution of complex motor gestures. In the section called “OTHER INTERESTING THINGS” were included one work that analyze EMG (electromyography) alterations in pathological and healthy subjects in the protocol: Comparison between clinical diagnostic criteria of sleep bruxism and those provided by a validated portable holter. The described procedures can be used for clinical trials, although the studies proposed in this work do not refer to samples from pathological subjects. With its multi-specialist approach, through many theoretical and practical feedback, this work will be useful for specializing in neuroscience, statistics, engineering or physiology.
Table of Contents
1. INTRODUTION TO ELECTROENCEPHALOGRAPHY (GENERAL ASPECTS)
1.1 FUNDAMENTALS OF EEG MEASUREMENT
1.1.1 Activity of the brain
1.1.2 Electroencephalography (EEG)
1.1.3 Quantitative electroencephalography (qEEG)
1.1.4 Frequency and amplitude of the signal
1.1.5 International 10-20 system
2. DATA MINING AND STATISTICAL ANALYSIS
2.1 PRE-PROCESSING PROCEDURES
2.1.1 EEGLAB: statistical software for electro-physiological data analysis
2.1.2 Importing channel location: information about the electrodes placement
2.1.3 Filtering data to minimizing the introduction of artifacts
2.1.4 Extracting data epochs and removing baseline values
2.2 CHANNEL DATA ANALYSIS
2.2.1 Channel data scroll: visualization, normalization and channel rejection procedure
2.2.2 Channel spectra and associated topographical maps
2.2.3 ERP and associated topographical maps
2.2.4 Time/frequency decomposition
2.2.5 Cross-coherences computation
2.2.6 Channel summary
2.2.7 Rejecting artifacts in continuous and epoch data
2.3 COMPONENT DATA ANALYSIS
2.3.1 Independent Component Analysis
2.3.2 ICA Algorithms
2.3.3 Component data scroll
2.3.4 Component spectra and associated topographical maps
2.3.5 Time/frequency decomposition
2.3.6 Computing cross-coherences
2.3.7 Component summary
2.3.8 Rejecting based on independent data components
2.4 MULTIPLE SUBJECT DATA PROCESSING
2.4.1 Channel statistics
2.4.2 Component statistics
2.4.3 Clustering procedure
2.4.4 Preparing to cluster with PCA method
2.4.5 Clustering
2.4.6 Component clusters visualization
2.5 STATISTICAL PROCEDURES
2.5.1 Parametric and non-parametric statistics
2.5.2 Paired/unpaired samples
2.5.3 Re-sampling methods
2.5.4 Multivariate methods (PCA vs ICA)
2.5.5 Correcting for multiple comparisons
3. EXPERIMENTAL STUDY DESIGNS
3.1 ELECTROCORTICAL ACTIVITY IN DANCERS AND NON-DANCERS LISTENING TO DIFFERENT MUSIC GENRE AND DURING IMAGINATIVE DANCE MOTOR ACTIVITY
3.1.1 Abstract
3.1.2 Introduction
3.1.3 Materials and Methods
3.1.4 Statistical analysis
3.1.5 Results
3.1.6 Discussions and conclusions
3.2 ELECTROCORTICAL ACTIVITY DURING MONOSYNAPTIC REFLEX IN ATHLETES
3.2.1 Abstract
3.2.2 Introdution
3.2.3 Materials and methods
3.2.4 Statistical analysis
3.2.5 Results
3.2.6 Discussion and conclusions
3.3 MONITORING OF ELECTROCORTICAL ACTIVITY FOR EVALUATION OF SEASICKNESS
3.3.1 Abstract
3.3.2 Introdution
3.3.3 Materials and methods
3.3.4 Statistical analysis
3.3.5 Results
3.3.6 Discussions and conclusions
3.4 ELECTROCORTICAL ACTIVITY IN DIFFERENT BODY POSITIONS
3.4.1 Abstract
3.4.2 Introdution
3.4.3 Materials and methods
3.4.4 Statistical analysis
3.4.5 Results
3.4.6 Discussions and conclusions
3.5 ELECTROCORTICAL ACTIVITY IN ATHLETES AND NON-ATHLETES DURING BODY BALANCE TASKS
3.5.1 Abstract
3.5.2 Introdution
3.5.3 Materials and methods
3.5.4 Statistical analysis
3.5.5 Results
3.6 ELECTROCORTICAL RESPONSES IN VOLUNTEERS WITH AND WITHOUT SPECIFIC EXPERIENCE WATCHING MOVIES INCLUDING THE EXECUTION OF COMPLEX MOTOR GESTURES
3.6.1 Abstract
3.6.2 Introduction
3.6.3 Materials and methods
3.6.4 Statistical Analysis
3.6.5 Results
3.6.6 Discussions and conclusions
4. OTHER INTERESTING THINGS
4.1 COMPARISON BETWEEN CLINICAL DIAGNOSTIC CRITERIA OF SLEEP BRUXISM AND THOSE PROVIDED BY A VALIDATED PORTABLE HOLTER
4.1.1 Abstract
4.1.2 Introduction
4.1.3 Materials and Methods
4.1.4 Statistical Analysis
4.1.5 Results
4.1.6 Discussion and conclusions
Objectives and Topics
This work aims to advance the reliability of electroencephalography (EEG) through the development and application of sophisticated tools and statistical methods. It provides a comprehensive framework for experimental design, data visualization, and both descriptive and inferential statistical analysis, including multivariate approaches like PCA and ICA, to better understand electrocortical signals in various physiological and complex scenarios.
- Fundamentals of EEG measurement and signal characteristics.
- Advanced data mining and statistical procedures for multi-subject datasets.
- Experimental designs analyzing electrocortical activity during dance, athletic reflexes, and motion sickness.
- Methods for processing multivariate EEG data, including artifact rejection and component clustering.
- Clinical application of instrumental diagnostic criteria for sleep bruxism.
Excerpt from the Book
1.1.2 Electroencephalography (EEG)
Electroencephalography is the recording of brain electrical activity by sensors. The electrodes are arranged on the surface of the head using a suitable amplifying equipment. The EEG signal reflects the electrical events of the skull. These events include cerebral post-synaptic potentials, action potentials, electrical signals of the skin, muscles, blood vessels and eyes.
Some EEG waves are associated with certain states of consciousness and brain-specific pathological conditions (like epilepsy). In some cases, researchers are more interested in the EEG analysis related to certain psychological events. These EEG associated with events (external or internal) are called “event related potentials” (ERPs). Since the EEG amplitude of signal decreases as it spreads from its point of origin, a comparison of the signals recorded from different points of the scalp can sometimes indicate the origin of any particular waves. This explains how the EEG activity is recorded in many points of the scalp. One of the most common potential event related is the sensory evoked potential, the modification of the EEG caused by momentary presence of a sensorial stimulus.
The EEG signal has two components: the response to stimulus and the contemporary background activity. Obviously, the signal is an important part of each trace recorded, while the background activity isn’t so important. The problem in sensory evoked potentials recording of is that the background is usually great as to obscure the event related signal. The analysis of evoked potentials takes into account the peaks or waves present in the average EEG.
Summary of Chapters
1. INTRODUTION TO ELECTROENCEPHALOGRAPHY (GENERAL ASPECTS): Provides the foundational knowledge of brain activity and EEG measurement, including signal characteristics and the international 10-20 electrode system.
2. DATA MINING AND STATISTICAL ANALYSIS: Details the methodologies for pre-processing, channel and component data analysis, and multiple subject statistical procedures, including PCA and ICA.
3. EXPERIMENTAL STUDY DESIGNS: Presents diverse research protocols applying the aforementioned methodologies to areas such as dance motor activity, athletic reflexes, seasickness, and body posture.
4. OTHER INTERESTING THINGS: Investigates the comparison between clinical diagnostic criteria and instrumental portable holter monitoring for the diagnosis of sleep bruxism.
Keywords
Electroencephalography, EEG, qEEG, Data Mining, Statistical Analysis, Independent Component Analysis, ICA, Principal Component Analysis, PCA, Experimental Design, Electrocortical Activity, Motor Imagery, Motion Sickness, Sleep Bruxism, Signal Processing
Frequently Asked Questions
What is the primary focus of this thesis?
The work focuses on advancing electroencephalography (EEG) by describing best procedures for experimental design, signal processing, and statistical analysis, particularly for complex multi-subject studies.
What are the central themes discussed in the experimental section?
The central themes include electrocortical responses during physical tasks such as dance motor imagery, monosynaptic reflexes in athletes, the evaluation of seasickness in real marine environments, and the effects of body posture on EEG signals.
What is the main research objective regarding EEG methodology?
The primary goal is to provide statistical concepts and practical examples to improve the reliability and analysis of electroencephalographic signals through qualitative and quantitative approaches.
Which scientific methods are primarily utilized in the data analysis?
The thesis utilizes advanced statistical models, including parametric and non-parametric statistics, re-sampling methods (bootstrap), and multivariate blind source separation techniques like Principal Component Analysis (PCA) and Independent Component Analysis (ICA).
What does the final chapter cover regarding clinical applications?
The final chapter assesses the efficacy of international clinical diagnostic criteria for sleep bruxism by comparing them against objective data from a validated portable holter device.
Which keywords define this work?
The work is characterized by terms such as EEG, qEEG, Data Mining, ICA, PCA, Signal Processing, Electrocortical Activity, and Sleep Bruxism.
How does the author propose to differentiate experienced dancers from novices?
The author identifies differences in attentional effort reflected in EEG power spectrum and alpha band activity during imaginative dance motor activity, indicating distinct neural motor program development.
What specific finding does the research present regarding seasickness?
The research concludes that postural strategies and adjustments, such as moving to a supine position with eyes closed, can significantly alter the electrocortical signal associated with motion sickness and potentially mitigate symptoms.
How is the "mirror neuron" theory applied to the study of watching motor gestures?
The study analyzes whether experts show distinct cortical activation (specifically in the motor cortex) compared to non-experts when viewing videos of technical gestures they are trained to perform, testing the "mirror neuron" hypothesis of observation-execution correspondence.
- Quote paper
- Doctor Giovanni Cugliari (Author), Marco Ivaldi (Author), 2015, Multivariate Statistical Analysis in Neuroscience, Munich, GRIN Verlag, https://www.grin.com/document/299430
