Essentials of EEG/MEG
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This document provides an overview of essential methods for analyzing EEG/MEG signal data. It discusses (1) what EEG and MEG are and how they relate to brain activity, (2) common analytic steps like epoching, artifact rejection, averaging, and measuring amplitudes, (3) examples of ERP components and how to avoid overlap, (4) advanced approaches like source analysis, time-frequency analysis, and multiscale entropy analysis, and (5) the importance of EEG/MEG for studying human cognitive functions and brain mechanisms.
Essentials of EEG/MEG
- 1. Essentials and Analytic Methods of EEG/MEG signals 徐峻賢 中央研究院語言學研究所
- 2. by Steven Luck http://erpinfo.org/the-erp-bootcamp references of this presentation
- 3. Demo • EEG – https://www.youtube.com/watch?v=bdsfydAjeUQ • MEG – https://www.youtube.com/watch?v=kgInT8hbDu Q&index=9&list=PL16Q0tGI4WvIes7ezj0VoF90tTB G1F1o7
- 4. From EEG to ERP (Lee et al., 2007)
- 5. Main Issues • What is EEG/ MEG? • How to analyze EEG/MEG data? • Advanced approaches
- 6. Electroencephalography (EEG) • ElectroEncephaloGraphy: a recording (graphy) of electrical signal (electro) from the brain (encephalo).
- 7. • 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)
- 10. 10-20 system Figure adopted from Malmivuo & Plonsey, 1995
- 11. High-Density EEG Recording Figure adopted from Malmivuo & Plonsey, 1995
- 12. 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
- 13. Analytic steps Continuous waveforms with event marks Epochs aligned to the time- locking events Averaged waveforms for analysis •Epoching •Visual Inspection •Baseline •Artifact • Rejection • Correction •Filtering •Averaging
- 14. • Clean EEG data
- 15. • Noisy EEG data – line noise, EOG, EMG, etc.
- 16. Analytic steps Continuous waveforms with event marks Epochs aligned to the time- locking events Averaged waveforms for analysis •Epoching •Visual Inspection •Baseline •Artifact • Rejection • Correction •Filtering •Averaging
- 17. 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.
- 18. Filter
- 20. 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 Silent naming Homophone judgment
- 21. From Epoch to Grand Average Averaging
- 22. • EEG/MEG activity = event-related activity + random noise (mean = 0)
- 23. Exclusion Criteria • Behavioral Exclusion Criteria – Error rate • EEG/MEG Exclusion Criteria – The number of “clean” epochs – Is the “abnormal” pattern informative?
- 24. Analytic steps Continuous waveforms with event marks Epochs aligned to the time- locking events Averaged waveforms for analysis •Epoching •Visual Inspection •Baseline •Artifact • Rejection • Correction •Filtering •Averaging
- 26. MEG response to onsets of single words (visual) M100
- 27. MEG response to onsets of single words (visual) M170
- 28. MEG response to onsets of single words (visual) M250
- 29. MEG response to onsets of single words (visual) M350
- 30. Measuring Amplitudes Mean amplitude calculate the mean amplitude in a defined time-window Area amplitude mean amplitude × number of time point
- 31. (Hsu et al. 2014)
- 32. CAUTION • Components might overlap
- 33. Lateralized Readiness Potential Coles, 1989, Psychophysiology
- 34. Examples of components overlapping: semantic judgment task Instruction: 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
- 35. To avoid components overlapping: go/no-go task Instruction: press a key if the noun is an animal name do not press any key if the noun is not an animal name
- 36. • 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) Try your best to control these factors:
- 37. 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)
- 38. 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)
- 39. • Why EEG/MEG is important for studying human mind? – temporal dynamics of cognitive functions • source analysis – brain mechanisms • time-frequency analysis, etc.
- 40. 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
- 41. Source Analysis (2) • Dale et al. (2000) – L2 minimum norm solution – Dynamic statistical parametric mapping (dSPM)
- 42. Time-Frequency Analysis (Talon-Baudry & Bertrand, 1999, TICS)
- 43. Time-frequency activity of auditory-evoke responses of MEG
- 44. 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)
- 45. Multiscale entropy (MSE) analysis of EEG signals Complex EEG Regular EEG (Yang, et al, 2014)
- 46. A Final Remark Data are cheap (well… it depends), Facts are expensive, Insight is priceless. People are desperate to be inspired!