Essentials of EEG/MEG

Essentials and Analytic Methodsof EEG/MEG signals

徐峻賢中央研究院語言學研究所

by Steven Luckhttp://erpinfo.org/the-erp-bootcamp

references of this presentation

Demo

• EEG– https://www.youtube.com/watch?v=bdsfydAjeUQ

• MEG– https

://www.youtube.com/watch?v=kgInT8hbDuQ&index=9&list=PL16Q0tGI4WvIes7ezj0VoF90tTBG1F1o7

From EEG to ERP

(Lee et al., 2007)

Main Issues

• What is EEG/ MEG?• How to analyze EEG/MEG data?• Advanced approaches

Electroencephalography (EEG)

• ElectroEncephaloGraphy: a recording (graphy) of electrical signal (electro) from the brain (encephalo).

• Action potentials generally make little or no contribution to scalp EEG

• EEG/ERPs reflect mainly the summed PSPs – (EPSPs and IPSPs at that moment) of large populations of pyramidal neurons

• for an ERP, be active in a consistent temporal relationship with the stimulus

(EPSPs and IPSPs)

http://www.ctf.com/Pages/page33.html

Magnetoencephalography (MEG)

EEGMEG

10-20 system

Figure adopted from Malmivuo & Plonsey, 1995

High-Density EEG Recording

Figure adopted from Malmivuo & Plonsey, 1995

Advantages of using ERPs• Lots of data– channels x times x trials• e.g., 62 x 1000 x 1000 (one participants)

– channels x times x trials x frequency• e.g., 157 x 1000 x 1000 x 100 (one participants)

• Freedom from Extraneous Task Demands• Modality Neutral

Analytic stepsContinuous waveforms with event

marks

Epochs aligned to the time-locking

events

Averaged waveforms for analysis

•Epoching•Visual Inspection

•Baseline•Artifact

• Rejection• Correction

•Filtering•Averaging

• Clean EEG data

• Noisy EEG data– line noise, EOG, EMG, etc.

Analytic stepsContinuous waveforms with event

marks

Epochs aligned to the time-locking

events

Averaged waveforms for analysis

•Epoching•Visual Inspection

•Baseline•Artifact

• Rejection• Correction

•Filtering•Averaging

Baseline correction

• Use pre-stimulus interval as baseline– e.g., 100-200 ms before stimulus onset– The amplitude in this period is unaffected by the stimulus.

• CAUTION: any noise in the baseline will add noise to your measures.

Filter

Avoiding artifacts from participants and the experimental procedure• Lexical decision

– with children…..hum…?• go/ no-go semantic judgment

• naming task– simultaneously recording EEG??– homophone judgment

Length of epoch

Silent naming

Homophone judgment

From Epoch to Grand Average

Averaging

• EEG/MEG activity = event-related activity

+random

noise (mean = 0)

Exclusion Criteria

• Behavioral Exclusion Criteria– Error rate

• EEG/MEG Exclusion Criteria– The number of “clean” epochs– Is the “abnormal” pattern informative?

Analytic stepsContinuous waveforms with event

marks

Epochs aligned to the time-locking

events

Averaged waveforms for analysis

•Epoching•Visual Inspection

•Baseline•Artifact

• Rejection• Correction

•Filtering•Averaging

ERP waveforms

http://erpinfo.org/the-erp-bootcamp

MEG response to onsets of single words (visual)

M100

MEG response to onsets of single words (visual)

M170

MEG response to onsets of single words (visual)

M250

MEG response to onsets of single words (visual)

M350

Measuring Amplitudes

Mean amplitudecalculate the mean amplitude in a defined time-window

Area amplitudemean amplitude × number of time point

(Hsu et al. 2014)

CAUTION• Components might overlap

Lateralized Readiness PotentialColes, 1989, Psychophysiology

Examples of components overlapping: semantic judgment taskInstruction: press the left key if the noun is an animal name

press the right key if the noun is not an animal name

P200

N400

P3 + LRP

To avoid components overlapping: go/no-go taskInstruction: press a key if the noun is an animal name

do not press any key if the noun is not an animal name

• Sometimes adopting the factorial design might not allow to have enough trials for estimating ERPs.

• e.g., psycholinguistic studies

frequency 詞頻 : e.g., 村 vs. 皴regularity ( 發音 ) 規則性 : e.g., 楓 vs. 埋orthography-to-phonology consistency 表音一致性 : e.g., 搖 vs. 梳imageability, concreteness 文字指稱的概念特性 : e.g., 蜂 vs. 風grammatical class 語法類型 : e.g., 跑 vs. 紙semantic ambiguity (e.g.: bank, 黃牛 )…etc.

Try your best to control these factors:

Single-trial regression analysis with MEG data

• Random Variable– Subjects, Items

• Fixed Variables– trial numbers (the rank of trials in the list)– number of strokes– phonetic combinability– semantic combinability– frequency– noun-to-verb ratio– semantic ambiguity

physical level

lexical level

orthographic level

semantic level

(Hsu, Lee and Marantz, 2011)

The contributions of bilateral occipital-temporal regions in the reading of Chinese words

• The semantic combinability effects in RH M170 reflects the decomposition of characters.

• Effect of visual complexity in LH M170 suggests that LH fusiform gyrus is a general mechanism for visual word recognition.

(Hsu, Lee and Marantz, 2011)

• Why EEG/MEG is important for studying human mind?

– temporal dynamics of cognitive functions• source analysis

– brain mechanisms• time-frequency analysis, etc.

Source Analysis (1)

Multilayer model:

skull and scalp taken into account,

conductivities needed

Homogeneous model:

skull taken as an insulator,

result independent of conductivity

Head model for EEG Head model for MEG

Source Analysis (2)

• Dale et al. (2000)– L2 minimum norm solution– Dynamic statistical parametric mapping (dSPM)

Time-Frequency Analysis

(Talon-Baudry & Bertrand, 1999, TICS)

Time-frequency activity of auditory-evoke responses of MEG

Two mechanisms of of acoustic change detection (Hsu et al. 2014):1: memory updating (theta bands and T1/ T3 contrasts)2: functional inhibition (alpha bands and T2/ T3 contrasts)

Multiscale entropy (MSE) analysis of EEG signals

Complex EEG

Regular EEG

(Yang, et al, 2014)

A Final Remark

Data are cheap (well… it depends),Facts are expensive,Insight is priceless.

People are desperate to be inspired!