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Electroencephalography & Event-related potentials ( EEG & ERP) علی یونسی پژوهشکده علوم شناختی اردیبهشت 91.

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Presentation on theme: "Electroencephalography & Event-related potentials ( EEG & ERP) علی یونسی پژوهشکده علوم شناختی اردیبهشت 91."— Presentation transcript:

1 Electroencephalography & Event-related potentials ( EEG & ERP) علی یونسی پژوهشکده علوم شناختی اردیبهشت 91

2 مصرف کننده شیشه ( متآمفتامین ) مصرف کننده قبلی شیشه فرد سالم

3 خلاصه  مقدمه ای بر نوار مغزی و سیگنال ناشی از محرک  منشا فعالیت الکتریکی مغز  ریتمهای مغزی  حالات مغز  کاربردهای EEG  کاربردهای شناختی  یادگیری  حافظه  توجه  تفسیر رویا  کاربردهای بالینی  تشنج  اختلالات حافظه  پایش  پایش تشنج  پایش در طول بیهوشی  پایش در حین عمل اندارترکتومی

4  کاربردهای شناختی ERP  BCI  ورودیهای حسی  خروجیهای حرکتی  تفسیرهای عملکردی  توجه  یادگیری  کاربردهای بالینی ERP  Schizophrenia  Mood disorders  Alcohol dependence and substance abuse  Dementia  Traumatic brain injury  Normal development  Childhood disorders  ADHD  Learning Disorders  تحقیقات در حال انجام  سوء مصرف شیشه  خواب

5 Research and Application  Psychological Research  Neurological Research  Medical Research  Educational Research and Application  Therapeutic Application  Occupational Application

6 تعداد مقالات

7 الکتریکی – Electroencephalography (EEG) –Electrocorticogram (ECoG) –Local field potential or single neuron روشهای دیگر –Magnetoencephalography (MEG) –Positron emission tomography (PET) –Magnetic resonance imaging (fMRI) –Infrared (IR) imaging پوست بافت نرم جمجمه دورا کورتکس 5 mm

8 8 When a neuron is active, its voltage may change by 100 mV or more. Electrical activity in a single neuron. How do EEGs work? Neural communication produces electrical activity.

9 Large amplitudes tend to entrain many neurons Inhibition Minimum Maximum Time Cell 2 Cell 1 Cell 3 Cell 1 Cell 2 Cell 3 Excitation (Pyramidal cells) Maximum Minimum Basics: Inhibition, Amplitude and Timing

10 Cortical Basis of Scalp EEG Baillet et al., IEEE Sig. Proc. Mag., Nov 2001, p. 16.

11 Six Layer Cortex Mountcastle, Brain, 120: , 1997.

12 Head Tissue Layers

13 EEG Electrodes Sliver Electrodes Electrodes Cap

14 14 This activity may be detectable to electrodes on the scalp. How do EEGs work? Conventional electrode caps from EGI, Neuroscan, and Electro-Cap.

15 برتریهای نسبی به دیگر روشها  Hardware costs are significantly lower than those of all other techniques  EEG sensors can be used in more places than fMRI, SPECT, PET, MRS, or MEG,  EEG has higher temporal resolution - milliseconds, rather than seconds - it can, in fact, take as many as 2000 samples per second (Only MEG rivals these speeds)  EEG is relatively tolerant of subject movement  EEG is silent  EEG does not aggravate claustrophobia  EEG does not involve exposure to high-intensity (>1 Tesla) magnetic fields  ERP studies can be conducted with relatively simple paradigms, compared with block-design of fMRI studies  Extremely uninvasive

16 نقصان نسبت به دیگر روشها  Significantly lower spatial resolution. fMRI, for example, can directly display areas of the brain that are active, while EEG requires intense interpretation just to hypothesize what areas are activated by a particular response.  EEG determines neural activity that occurs below the upper layers of the brain (the cortex) very poorly. Unlike PET and MRS, cannot identify specific locations in the brain at which various neurotransmitters, drugs, etc. can be found.  Often takes a long time to connect a subject to EEG, as it requires precise placement of dozens of electrodes around the head and the use of various gels, saline solutions, and/or pastes to keep them in place.  Signal-to-noise ratio is very poor, so sophisticated data analysis and relatively large numbers of subjects are needed to extract useful information from EEG

17 چند نکته حیاتی  a given electrode on the scalp does not record solely the neuronal activity directly underlying it. Rather, every electrode picks up signals from different sources that can eventually be quite distal  Fluctuation of the voltage at the reference electrode will lead to changes of the potential at the active electrode even if the voltage at that point was actually stable. There is no point that is electrically silent and could be considered as true zero potential.

18 EEG Recording From Normal Adult Male

19 19/37 Midline Fronto- central Centro- parietal Anterior Posterior

20 20 PCs 1-8

21 10 /20 % system of EEG electrode placement

22

23 Different types of brain waves in normal EEG

24 Alpha wave  rhythmic, 8-13 Hz  mostly on occipital lobe  μ V  normal,  relaxed awake rhythm with eyes closed

25 Beta wave  irregular, Hz  mostly on temporal and frontal lobe  mental activity  excitement

26 Theta wave  rhythmic, 4-7 Hz  Drowsy, sleep

27 Delta wave  slow, < 3.5 Hz  in adults  normal sleep rhythm

28

29 Different types of brain waves in normal EEG RhythmFrequency (Hz) Amplitude (uV) Recording & Location Alpha(α) 8 – 1350 – 100Adults, rest, eyes closed. Occipital region Beta(β) Adult, mental activity Frontal region Theta(θ) 5 – 7Above 50Children, drowsy adult, emotional distress Occipital Delta(δ) 2 – 4Above 50Children in sleep D T A B

30 EEG brain waves in the Sleep Cycle:

31 استراحت؟

32 Fourier Domain

33 EEG recording in man  Eyes opened condition.  Examples of different waves.

34 The cocktail party problem - find Z A z1z1 z2z2 zNzN XTXT ZTZT X T = AZ T x1x1 x2x2 xNxN

35 Blind Source Separation Blind Source Separation deals with the « separation » of a mixture of sources, with a little prior information about the mixing process and the sources signals Sensors S1S2SNS1S2SN Sources Environment Source Separation Algorithm W x2x2 xNxN x1x1 Ŝ 1 Ŝ2Ŝ2 ŜNŜN Observations x = A sŜ = W x

36 Nyquist Theorem  The highest frequency which can be accurately represented is one- half of the sampling rate.  The sampling rate here is below the Nyquist frequency, so the result of sampling is nothing like the input: aliasing.  For practical purposes the sampling rate should be 10 higher than the highest frequency in the signal.

37 Two dimensional example

38

39

40 LORETA  One such method is known as "LORETA", which provides an estimate of the current distribution throughout the entire 3- dimensional space within the brain.  An example of a LORETA solution, mapped onto a normalized brain space, is provided below. 04/06/11SPAN Harry Howard - Tulane University 40

41 Brain Wave Activity  Delta – sleep state (1-3 Hz)  Theta – between sleep and awake (4-7 Hz)  Alpha – relaxed state (8-12 Hz)  Low Beta – focused concentration (SMR-Sensory Motor Rhythms) (12-15 Hz)  Mid-range Beta – alert state (15-18 Hz)  High Beta – very alert, vigilant (Above 18)  Gamma – Hyper vigilant (Above 40)

42 Default Mode Netowrk

43 Default-mode brain  The default network is a network of brain regions that are active when the individual is not focused on the outside world and the brain is at wakeful rest.  Working memory tasks differentially deactivate the PCC.  signal increase and spatial decrease in the PCC and a signal decrease but spatial increase in the ACC with increasing working memory load

44 (1) Oscillations: Timing and spatial organization of information processes. Oscillations provide mechanisms that allow the emergence of spatially and temporally organized firing patterns in neural networks. (2) Slow frequency oscillations: Conscious control of information processing. Slow frequency oscillations in the theta and alpha range (of about 4 – 13.5 Hz) are associated with the top-down control of two large processing systems, a working memory system and a a complex knowledge system, allowing semantic orientation in a constantly changing environment. Theta and alpha oscillations exhibit a variety of different synchronization processes (e.g., amplitude increase, phase coupling, event-related phase reorganization) that reflect different types of control processes and different aspects of the timing of cognitive processes. Oscillations and the control of information processing

45 ms poststim µV Fz Cz Pz Oz Theta-waves single subject “B“ A popo p -1p+1 change in direction Example of an evoked, ‘traveling’ theta wave, one subject, negative polarity is in blue

46 کاربردها ترجمه سیگنال به یک حرکت استفاده از مغز به عنوان یک پردازشگر سریع بررسی عملکرد حسی و شناختی فرد

47 Alzheimer’s disease and mild cognitive impairment  Semantic classification task: low frequency functional connectivity between anterior (MPFC) and posterior (PCC/retrosplenial cortex) regions : negatively associated with age  eyes-closed resting state in Alzheimer patients ( N =24; 9 males; mean age 76.3 years) and non-demented subjects with subjective memory complaints ( N =19; 9 males)  The mean level of EEG synchronization was lower in Alzheimer patients in the upper alpha (10–13 Hz) and beta (13–30 Hz) band.

48 Schizophrenia  low-frequency and alpha-band power abnormalities (perhaps thalamic and frontal lobe dysfunction)  augmented low-frequency power :  more negative symptoms  larger third ventricles  larger frontal horns of the lateral ventricles  increased cortical sulci widths  greater ocular motor dysfunction

49 Autism  In the θ (3–6 Hz) frequency range  within left hemisphere frontal and temporal regions  8–10 Hz:  globally reduced coherence within frontal regions and between frontal and all other scalp regions.  The ASD : greater relative power between 3 and 6 Hz and 13–17 Hz and significantly less relative power between 9 and 10 Hz.

50 برانگیختگی ناشی از محرک P3 یکی از بخشهای خاص این سیگنال برانگیخته است : میزان توجه سختی پردازش تصویر سن فرد برانگیختگی بدون محرک هدف برانگیختگی با محرک هدف

51 The mismatch negativity (MMN) MMN can be recorded in infants and young children

52

53 64 electrodes 256 Hz sample rate 1-45 Hz filtering 1000ms epoch EEG Acquisition

54 بررسیهای ادراکی و شناختی Visual ERP (VEP)  ادراکی ( نمایش محرکهای ساده )  روشنایی  رنگ  حرکت ....  شناختی ( محرکهای پیچیده )  حافظه  توجه  تشخیص شیی ...

55 بیماریهایی که در آنها تغییرات در ERP گزارش شده است  Mood disorders  Alcohol dependence and substance abuse  Dementia  آلزایمر  Traumatic brain injury  Normal development  Childhood disorders  ADHD  Learning Disorders  مولتیپل اسکلروزیس  پارکینسون

56 Coma  reappearance of the MMN is a valid predictor of recovery from coma.  Based on  six N100 studies (N = 548 patients)  five MMN studies (N = 470)  six P300 studies (N = 313) the N100, MMN, or P300, when present, significantly predicted awakening, P300 and MMN being significantly better predictors than N100.

57 Schizophrenia  reduced P300 amplitude in patients with schizophrenia (first reported 35 years ago)  The patients with schizophrenia showed smaller MMN amplitude 60 patients 53 unaffected family members 44 healthy controls

58 Mood disorders  deviant P300 is less consistent in mood disorders  patient subtypes or to the severity of depression  Bipolar disorder show more consistent P300 deviations (both latency and amplitude)

59 Alcohol dependence  the P300 amplitude reduction  degree of reduction in P300 in alcoholics was highly correlated with the number of alcohol-dependent individuals in the family

60 Dementia  between subcortical and cortical dementias  P300 latency distinguish dementia from depression-associated pseudodementia  Discrimination between patients with early Alzheimer’s disease and healthy individuals

61 Traumatic brain injury  Reduction in the amplitude of visual P300 in survivors of traumatic brain injury in approximately half of the studies  Most frequent effect of traumatic brain injury: reduction in the amplitude of auditory P300

62 Traumatic brain injury

63 Developmental changes in ERPs  three-stimulus oddball (frequent standard, rare target, rare nontarget novel) frontal P3a amplitude elicited by rare novel stimuli tends to increase between the ages of 8 and 20 years.  For routine initial examination, the passive oddball may be the most useful task across a wide age range

64 Attention-deficit/hyperactivity disorder  small P300 amplitudes with normal latency  a decrement in P300 at posterior electrode sites in conjunction with an augmentation at frontal sites

65 Other disorders of childhood  central auditory processing difficulties : failed to generate ERPs or substantially increased latencies and smaller amplitudes  In 11-year-old children with oppositional-defiant disorder: smaller P300 amplitudes to both cues and targets in a CPT

66 High-risk children  Visual mental rotation task: P300 amplitude was smaller for young boys at high- compared to lowrisk for alcoholism (similar to individuals with alcohol dependence)  Lower P300 amplitudes also were observed in the pre-adolescent sons of alcoholic men

67 Dyslexia  the magnitude of the MMN reduction is correlated with the severity of dyslexia  MMN can also be employed to assess the effectiveness of dyslexia rehabilitation programs

68 کوکایین الکل ارزیابی ERP در اعتیاد

69 Attentional Bias to Drug- and Stress-Related Pictorial Cues in Cocaine Addiction Comorbid with PTSD

70 Auditory target processing in methadone substituted opiate addicts: The effect of nicotine in controls

71 تصویر صورت تصویر خنثی

72 انواع تصاویر در مطالعه ادراکی

73 برانگیختگی ناشی از محرک ادراکی میزان میلین قطر نورون شناختی حافظه توجه احساسات

74 How to analyze  EEGLab  Brainstorm

75 EEGLAB documentation EEGLAB Home Pagehttp://sccn.ucsd.edu/eeglab/http://sccn.ucsd.edu/eeglab/ EEGLAB Tutorial Indexhttp://sccn.ucsd.edu/eeglab/eeglabtut.htmlhttp://sccn.ucsd.edu/eeglab/eeglabtut.html Workshop Home Pagehttp://sccn.ucsd.edu/eeglab/workshop/http://sccn.ucsd.edu/eeglab/workshop/ pages of tutorial (including “how to” for plugins) WEB or PDF

76 نتیجه گیری کاربردهای بالقوه برانگیختگی ناشی از محرک در مطالعات اعتیاد : تشخیص اعتیاد؟ تشخیص نوع ماده / مواد مصرفی؟ تشخیص مدت زمان مصرف و دوز مصرفی؟ پایش درمان اعتیاد؟ سیستمهای بازخورد عصبی و زیستی؟

77 نتیجه گیری  نکات حائز اهمیت  رضایت بیمار  رعایت نکات اخلاقی  خطاهای تشخیصی  گرانی هزینه پردازش  شدت سیگنال به نویز  راه اندازی نسبتا دشوار


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