MEG/EEG Module Trainees Kai Hwang Tina Rasmussen TA Gus Sudre Bronwyn Woods Instructor Bill Eddy, Ph.D. Anna Haridis Thanks to:

Slides:

Advertisements

Similar presentations

Early studies of the development of recognition memory in infants demonstrated a negative component over central leads (Nc) with greater amplitude in event-related.

Advertisements

The Event-Related Potential (ERP) Embedded in the EEG signal is the small electrical response due to specific events such as stimulus or task onsets, motor.

Temporal dynamics of spatial phase congruency sensitivity in human visual cortex Linda Henriksson 1,2, Aapo Hyvärinen 3 and Simo Vanni 1 1 Brain Research.

Electroencephalogram (EEG) and Event Related Potentials (ERP) Lucy J. Troup 28 th January 2008 CSU Symposium on Imaging.

Fast Method for Precise Phosphene Threshold Identification in Research Using Transcranial Magnetic Stimulation Abrahamyan, A., Clifford C.W.G., and Harris.

Detecting Conflict-Related Changes in the ACC Judy Savitskaya 1, Jack Grinband 1,3, Tor Wager 2, Vincent P. Ferrera 3, Joy Hirsch 1,3 1.Program for Imaging.

Exploring Magnetoencephalography (MEG) Data Acquisition and Analysis Techniques Rosalia F. Tungaraza, Ph.D. Anthony Kelly, B.A. Ajay Niranjan, M.D., MBA.

Brain-computer interfaces: classifying imaginary movements and effects of tDCS Iulia Comşa MRes Computational Neuroscience and Cognitive Robotics Supervisors:

Contrasts & Inference - EEG & MEG Outi Tuomainen & Rimona Weil mfd.

Electrophysiology. Electroencephalography Electrical potential is usually measured at many sites on the head surface More is sometimes better.

Pre-processing for EEG and MEG

HST 583 fMRI DATA ANALYSIS AND ACQUISITION Neural Signal Processing for Functional Neuroimaging Emery N. Brown Neuroscience Statistics Research Laboratory.

MEG Experiments Stimulation and Recording Setup, Overview on Data Analysis Educational Seminar Institute for Biomagnetism and Biosignalanalysis November.

MEG Experiments Stimulation and Recording Setup Educational Seminar Institute for Biomagnetism and Biosignalanalysis February 8th, 2005.

Electrophysiology.

Advances in Event-Related fMRI Design Douglas N. Greve.

Opportunity to Participate

Subdural Grid Intracranial electrodes typically cannot be used in human studies It is possible to record from the cortical surface Subdural grid on surface.

Experimental Design in fMRI

Electroencephalography The field generated by a patch of cortex can be modeled as a single equivalent dipolar current source with some orientation (assumed.

Electroencephalography and the Event-Related Potential

The Event-Related Potential (ERP) We have an ERP waveform for every electrode.

The Event-Related Potential (ERP) Embedded in the EEG signal is the small electrical response due to specific events such as stimulus or task onsets, motor.

Electroencephalography Electrical potential is usually measured at many sites on the head surface.

Brain Electrical Source Analysis This is most likely location of dipole Project “Forward Solution” Compare to actual data.

Four Main Approaches Experimental cognitive psychology Cognitive neuropsychology Computational cognitive science Cognitive neuroscience.

Introduction to Neural Networks Kenji Nakayama Kanazawa University, JAPAN 適応システム理論ガイダンス.

Closed and Open Electrical Fields

Attention Modulates Responses in the Human Lateral Geniculate Nucleus Nature Neuroscience, 2002, 5(11): Presented by Juan Mo.

Functional Brain Signal Processing: EEG & fMRI Lesson 4

You had it coming... Precursors of Performance Errors Tom Eichele, MD PhD Department of Biological and Medical Psychology University of Bergen Source:

NIMH MEG Core Facility Magnetoencephalography Imaging Resource, IRP, NIMH / NINDS Comparison of temporal and spatial resolution for various neuroimaging.

Automatic online control of motor adjustments -P NANDHA KUMAR.

Motivation Increase bandwidth of BCI. Reduce training time Use non invasive technique.

Expertise, Millisecond by Millisecond Tim Curran, University of Colorado Boulder 1.

Acknowledgement Work supported by NINDS (grant NS39845), NIMH (grants MH42900 and 19116) and the Human Frontier Science Program Methods Fullhead.

Signal Processing: EEG to ERP A Darren Parker Presentation.

1 Psychology 304: Brain and Behaviour Lecture 4. 2 Research Methods and The Structure of the Nervous System 2. What are the primary divisions of the nervous.

Methods for Dummies: Study Design and Pre-processing Tim West & Vanessa Meitanis Hans Berger- Inventor of EEG.

Date of download: 6/28/2016 Copyright © 2016 American Medical Association. All rights reserved. From: Teamwork Matters: Coordinated Neuronal Activity in.

Electrophysiology. Neurons are Electrical Remember that Neurons have electrically charged membranes they also rapidly discharge and recharge those membranes.

Methods for Dummies M/EEG Analysis: Contrasts, Inferences and Source Localisation Diana Omigie Stjepana Kovac.

[Ran Manor and Amir B.Geva] Yehu Sapir Outlines Review

The General Linear Model (GLM): the marriage between linear systems and stats FFA.

Volume 60, Issue 4, Pages (November 2008)

Perceptual Echoes at 10 Hz in the Human Brain

Viewing Lip Forms Neuron

Delayed Striate Cortical Activation during Spatial Attention

Cycle 10: Brain-state dependence

Dynamic Causal Modelling for M/EEG

Jason Samaha, Bradley R. Postle Current Biology

Threshold Behavior in the Initiation of Hippocampal Population Bursts

Volume 26, Issue 13, Pages (July 2016)

EEG and MEG: Relevance to Neuroscience

Selective Entrainment of Theta Oscillations in the Dorsal Stream Causally Enhances Auditory Working Memory Performance Philippe Albouy, Aurélien Weiss,

MEG fundamentals.

Machine Learning for Visual Scene Classification with EEG Data

Georgia G. Gregoriou, Stephen J. Gotts, Robert Desimone Neuron

Basics of EEG recording

Vahe Poghosyan, Andreas A. Ioannides Neuron

Jude F. Mitchell, Kristy A. Sundberg, John H. Reynolds Neuron

John T. Serences, Geoffrey M. Boynton Neuron

3.0 PREPARING MEASUREMENTS

Tuning to Natural Stimulus Dynamics in Primary Auditory Cortex

Viewing Lip Forms Neuron

DCM Demo – Model Specification, Inversion and 2nd Level Inference

Honghui Zhang, Andrew J. Watrous, Ansh Patel, Joshua Jacobs Neuron

The functional architecture of attention

Volume 65, Issue 4, Pages (February 2010)

Volume 27, Issue 1, Pages e6 (April 2019)

Presentation transcript:

MEG/EEG Module Trainees Kai Hwang Tina Rasmussen TA Gus Sudre Bronwyn Woods Instructor Bill Eddy, Ph.D. Anna Haridis Thanks to:

Goals Learn the basics of EEG/MEG –Why? Excellent temporal resolution, direct and noninvasive measurement of neuronal activity –How? Used simple motor, somatosensory, visual paradigms to learn data collection, preprocessing and analysis Easy to run, predictable results

Tasks: motor, visual, somatosensory TaskParametersWe’ll present… Paced finger tapping: Index finger, middle finger, big toe. A visual cue to indicate tap right or left finger. ISI = 800ms Source localization result of right index, right middle finger tapping. Visual checkerboard: Checkerboard stimuli shifted randomly between 4 quadrants of visual fields. Slow: ISI = 1sec, Duration = 300ms. Fast: ISI = 100ms, Duration = 300ms. Source localization of 4 quadrants of visual field. The effect of averaging and artifact rejection on sensor waveform. Time frequency analysis in sensor space Electrical stimulation: Index finger, middle finger, big toe. ISI = 492 ms, Duration = 10 ms Nothing due to time constraint.

Simultaneous MEG/EEG measurement

Experiences: EEG head cap was very stiff. It did not fit well to the head for some subjects. MEG head position indicator (HPI) coils could not be well localized by the MEG scanner, when they were glued to the EEG head cap Solution to both problems: Glue the EEG electrodes and the HPI coils directly onto the subject’s scalp Simultaneous MEG/EEG measurement

Processing Steps Preprocessing –Spatial filtering (SSS) –Temporal filtering (0 – 40Hz) Off-line averaging –By trial type (different finger, visual quadrant) –Reject trials with artifacts (EOG, ECG, etc) Source localization –MNE, dSPM Time-frequency analysis –Fieldtrip (Matlab)

Can MEG distinguish motor regions of different fingers? Right IndexRight Middle Average of 191 trials Average of 119 trials

Can MEG localize 4 quadrants of visual space? Upper right Bottom left Upper left Bottom right Average of 240 trials 120ms after stim. onset

How many events are needed to obtain a stable average waveform? Top right quadrant

Artifact rejection for EEG signals

Temporal variation of EEG activity over the visual cortex 5 consecutive stimuli: First stimulus always at bottom left Position of next 4 varies randomly ISI = 100ms, Duration= 300ms. Electrode O2 (EEG059) is placed over the right occipital lobe.

What have we learned? Experiments: Data recording setup Simultaneous MEG/EEG experiments Signal processing/analysis: Signal cleaning (SSS, continuous HPI, filtering) Event-related potentials Source estimation/localization Time-frequency analysis Analysis tools: MNE (GUI, batch scripts) Shell scripting (bash and C shell) Matlab toolbox (FieldTrip)

Toe tapping

Somatosensory Stimulation Right Toe

Somatosensory Stimulation Right Middle

SSS or no SSS? Right index tapping. White = no SSS, yellow= SSS on average, blue SSS before averaging MEG1821

cHPI (motion correction) Noisy!

Time-frequency analysis: EEG signals Fast:

Temporal variation of EEG activity over the visual cortex 3 consecutive stimuli First stimulus always at top right Next 2 shifted randomly ISI = 100ms, Duration= 300ms.