Using time-frequency and connectivity-methods in EEG-analysis

Eräs tärkeimmistä aivojen tutkimusmenetelmistä on aivojen sähköisen toiminnan mittaaminen. Elektroenkefalografia (EEG) on tärkeä mittaväline aivojen valvetilan sekä dynaamisen tilan kuvaamisessa ja menetelmä on laajalti käytössä niin lääketieteen kuin psykologiankin tutkimuksessa. EEG-mittauksen vaiheet ovat koehenkilölle tehtävän EEG-rekisteröinti, raakasignaalin käsittely, signaalin analysointi sekä johtopäätösten tekeminen. Diplomityön ensimmäisessä osiossa esitetään EEG-tutkimuksen matemaattinen perusta. Osiossa kuvataan yleisimpiä aivosähkökäyrän esikäsittely- ja analyysivaiheita. Esikäsittelyvaiheista kuvataan tarkemmin suodatus, baselinekorjaus, virheiden käsittely ja uudelleenreferensointi. Analyyseistä käydään läpi keskiarvovasteanalyysi (ERP), Fourier-, aika-taajuus- ja kaksi erilaista konnektiivisuusanalyysiä. Sopivissa kohdin esitetään esimerkkejä. Diplomityön toisessa osassa kuvataan aivosähkökäyrän analysointiin kehitetyn Matlab-ohjelmiston, Eegtool:n, toiminta pääpiirteittäin. Eegtool koostuu kahdesta Matlab-ohjelmasta: eegtool-preprocessing ja eegtool-analysis. Eegtool-preprocessing-ohjelmalla voidaan suorittaa EEG-signaalin esikäsittely. Eegtool-analysis ohjelmalla voidaan suorittaa analyyseja esikäsitellyille tiedostoille. Lisäksi osiossa käsitellään EEG-signaalin käsittelyä matriisi- ja vektorimuodossa ja siihen liittyviä erityispiirteitä. Toinen osio soveltuu käyttöohjeeksi Eegtool-ohjelman käyttöön

A graphical user interface for infant ERP analysis

Recording of event-related potentials (ERPs) is one of the best-suited technologies for examining brain function in human infants. Yet the existing software packages are not optimized for the unique requirements of analyzing artifact-prone ERP data from infants. We developed a new graphical user interface that enables an efficient implementation of a two-stage approach to the analysis of infant ERPs. In the first stage, video records of infant behavior are synchronized with ERPs at the level of individual trials to reject epochs with noncompliant behavior and other artifacts. In the second stage, the interface calls MATLAB and EEGLAB (Delorme & Makeig, Journal of Neuroscience Methods 134(1):9–21, 2004) functions for further preprocessing of the ERP signal itself (i.e., filtering, artifact removal, interpolation, and rereferencing). Finally, methods are included for data visualization and analysis by using bootstrapped group averages. Analyses of simulated and real EEG data demonstrated that the proposed approach can be effectively used to establish task compliance, remove various types of artifacts, and perform representative visualizations and statistical comparisons of ERPs. The interface is available for download from http://www.uta.fi/med/icl/methods/eeg.html in a format that is widely applicable to ERP studies with special populations and open for further editing by users. Electronic supplementary material The online version of this article (doi:10.3758/s13428-013-0404-4) contains supplementary material, which is available to authorized users

Using time-frequency and connectivity-methods in EEG-analysis

Eräs tärkeimmistä aivojen tutkimusmenetelmistä on aivojen sähköisen toiminnan mittaaminen. Elektroenkefalografia (EEG) on tärkeä mittaväline aivojen valvetilan sekä dynaamisen tilan kuvaamisessa ja menetelmä on laajalti käytössä niin lääketieteen kuin psykologiankin tutkimuksessa. EEG-mittauksen vaiheet ovat koehenkilölle tehtävän EEG-rekisteröinti, raakasignaalin käsittely, signaalin analysointi sekä johtopäätösten tekeminen. Diplomityön ensimmäisessä osiossa esitetään EEG-tutkimuksen matemaattinen perusta. Osiossa kuvataan yleisimpiä aivosähkökäyrän esikäsittely- ja analyysivaiheita. Esikäsittelyvaiheista kuvataan tarkemmin suodatus, baselinekorjaus, virheiden käsittely ja uudelleenreferensointi. Analyyseistä käydään läpi keskiarvovasteanalyysi (ERP), Fourier-, aika-taajuus- ja kaksi erilaista konnektiivisuusanalyysiä. Sopivissa kohdin esitetään esimerkkejä. Diplomityön toisessa osassa kuvataan aivosähkökäyrän analysointiin kehitetyn Matlab-ohjelmiston, Eegtool:n, toiminta pääpiirteittäin. Eegtool koostuu kahdesta Matlab-ohjelmasta: eegtool-preprocessing ja eegtool-analysis. Eegtool-preprocessing-ohjelmalla voidaan suorittaa EEG-signaalin esikäsittely. Eegtool-analysis ohjelmalla voidaan suorittaa analyyseja esikäsitellyille tiedostoille. Lisäksi osiossa käsitellään EEG-signaalin käsittelyä matriisi- ja vektorimuodossa ja siihen liittyviä erityispiirteitä. Toinen osio soveltuu käyttöohjeeksi Eegtool-ohjelman käyttöön

Widely applicable MATLAB routines for automated analysis of saccadic reaction times

Saccadic reaction time (SRT) is a widely used dependent variable in eye-tracking studies of human cognition and its disorders. SRTs are also frequently measured in studies with special populations, such as infants and young children, who are limited in their ability to follow verbal instructions and remain in a stable position over time. In this article, we describe a library of MATLAB routines (Mathworks, Natick, MA) that are designed to (1) enable completely automated implementation of SRT analysis for multiple data sets and (2) cope with the unique challenges of analyzing SRTs from eye-tracking data collected from poorly cooperating participants. The library includes preprocessing and SRT analysis routines. The preprocessing routines (i.e., moving median filter and interpolation) are designed to remove technical artifacts and missing samples from raw eye-tracking data. The SRTs are detected by a simple algorithm that identifies the last point of gaze in the area of interest, but, critically, the extracted SRTs are further subjected to a number of postanalysis verification checks to exclude values contaminated by artifacts. Example analyses of data from 5- to 11-month-old infants demonstrated that SRTs extracted with the proposed routines were in high agreement with SRTs obtained manually from video records, robust against potential sources of artifact, and exhibited moderate to high test-retest stability. We propose that the present library has wide utility in standardizing and automating SRT-based cognitive testing in various populations. The MATLAB routines are open source and can be downloaded from http://www.uta.fi/med/icl/methods.html

Widely applicable MATLAB routines for automated analysis of saccadic reaction times

Saccadic reaction time (SRT) is a widely used dependent variable in eye-tracking studies of human cognition and its disorders. SRTs are also frequently measured in studies with special populations, such as infants and young children, who are limited in their ability to follow verbal instructions and remain in a stable position over time. In this article, we describe a library of MATLAB routines (Mathworks, Natick, MA) that are designed to (1) enable completely automated implementation of SRT analysis for multiple data sets and (2) cope with the unique challenges of analyzing SRTs from eye-tracking data collected from poorly cooperating participants. The library includes preprocessing and SRT analysis routines. The preprocessing routines (i.e., moving median filter and interpolation) are designed to remove technical artifacts and missing samples from raw eye-tracking data. The SRTs are detected by a simple algorithm that identifies the last point of gaze in the area of interest, but, critically, the extracted SRTs are further subjected to a number of postanalysis verification checks to exclude values contaminated by artifacts. Example analyses of data from 5- to 11-month-old infants demonstrated that SRTs extracted with the proposed routines were in high agreement with SRTs obtained manually from video records, robust against potential sources of artifact, and exhibited moderate to high test-retest stability. We propose that the present library has wide utility in standardizing and automating SRT-based cognitive testing in various populations. The MATLAB routines are open source and can be downloaded from http://www.uta.fi/med/icl/methods.html

Developmental Precursors of Social Brain Networks: The Emergence of Attentional and Cortical Sensitivity to Facial Expressions in 5 to 7 Months Old Infants

<div><p>Biases in attention towards facial cues during infancy may have an important role in the development of social brain networks. The current study used a longitudinal design to examine the stability of infants' attentional biases towards facial expressions and to elucidate how these biases relate to emerging cortical sensitivity to facial expressions. Event-related potential (ERP) and attention disengagement data were acquired in response to the presentation of fearful, happy, neutral, and phase-scrambled face stimuli from the same infants at 5 and 7 months of age. The tendency to disengage from faces was highly consistent across both ages. However, the modulation of this behavior by fearful facial expressions was uncorrelated between 5 and 7 months. In the ERP data, fear-sensitive activity was observed over posterior scalp regions, starting at the latency of the N290 wave. The scalp distribution of this sensitivity to fear in ERPs was dissociable from the topography of face-sensitive modulation within the same latency range. While attentional bias scores were independent of co-registered ERPs, attention bias towards fearful faces at 5 months of age predicted the fear-sensitivity in ERPs at 7 months of age. The current results suggest that the attention bias towards fear could be involved in the developmental tuning of cortical networks for social signals of emotion.</p></div

The overlap paradigm.

<p>A face or a control stimulus was presented in the center of the screen after the participant fixated on an expanding red circle (fixation stimulus). A distractor was added to the right or to the left of the central stimulus after 1000 ms from face/control onset. The central stimulus was presented until the end of each trial, thus, overlapping in time with the distractor. The sequence of events and stimuli in the paradigm are shown with the duration of each event (top). The stimuli categories presented in the central location (neutral, happy, and fearful faces as well as phase-scrambled control stimuli) are shown in the bottom panel.</p

Association between the behavioral fear-bias at 5 months and the ERP fear-sensitivity at 7 months.

<p>A positive correlation between the behavioral fear-bias (increased probability of attentional dwell on fearful faces) and fear-sensitivity in N290 amplitude (increased positivity to fearful faces) was found (ρ = .22, p<.05, N = 61). Horizontal and vertical reference lines indicate median values.</p