EEG,ERPs,

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EEG,ERPs, relation to single neuronal activity.

• (For 312, Prosem.)

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2 parameters determine what an electrode records

• 1. Electrode tip diameter
• A) .1 to 3 microns (mu or µ) gives you
• action potentials (spikes) or psps.
• B) gt 50 mu EEG and its derivatives, such as
  event-related potentials (erps), event-related
  spectral disturbances (ersps).

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and the other parameter

• 2. Biological amplifier filter settings
• A) High pass passes frequencies gt 1KHz gives
  you spikes, multiple unit activity or hash,
  multiple neurons near one electrode (usually 5-50
  mu). Nobody liked hash for along time until Nikos
  Logothetis (more later).
• B) Low pass (0-100 Hz usually 0-30) gives you
  EEG, ERPs, ERSPs PSPs

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The point

• The Bio Amplifier subtracts one input from the
  other.
• Referential (Signal Reference) is standard
  EEG/ERP set up. It maximizes signals because
  signal is distant from 0 the quiet
  reference. (No such thing, really, but reference
  ltltltltltlt signal is ok, usually.)
• Differential (Signal 1 Signal 2)maximizes
  difference between electrodes. Usually used in
  animals where one can look surface to depth of
  cortex, yielding a huge signal. In humans, we are
  restricted to scalp, usually. Though for EOG (eye
  artifact channel), electrodes above and below eye
  can be hooked up differentially to great
  advantage. (Explain, John, Meixner).

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EEG and derivativesmeaning all these are
obtained from EEG hook-up

• 1. Spontaneous EEG Love to show you picture but
  cant draw in ppt. So take a look at
  http//en.wikipedia.org/wiki/Electroencephalograph
  y
• I just want you to look at voltages varying as a
  f (time) thats EEG. Its always there, in (on)
  your brain. The 2 major variables describing it
  are amplitude (height/magnitude) and frequency or
  how many times per sec the signal crosses 0
  microvolts (uV) and goes from plus to minus.

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Classical EEG frequencies and their meanings

• Alpha (8-13 Hz). Associated with unfocused
  relaxation, not sleep. Sinusoidal-synchronous
  high amplitude.
• Beta (14-30 Hz) seen in intense arousalas the
  exam is handed out. Or during dreaming sleep,
  called paradoxical sleep. Whats the paradox?
  Hardest to wake you up, but looks like youre up
  and taking exam, or anticipating torture. Low
  amplitude, unsynchronous (random-ish)
  frequencies.
• Theta (5-7 Hz) seen as you fall asleep,
  associated with hyponogogic images. Synchronous.
• Delta (0- 4 Hz) seen in deep, dream-free sleep,
  or in pathological stateover a lesion, a cyst
  with pus. But, despite what some fools believe,
  also seen in waking, as in CNV (described below).
  High amplitude.

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Examples
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Other EEG Phenomena

• Gamma (30-100 Hz40-50 average). Recently
  studied. Associated with higher cognitive
  phenomena, like learning, binding. When it is
  necessary that many cortical areas communicate
  (bind), the observed carrier wave among them is
  gamma. During a memory involving sight and sound,
  for example, visual and auditory cortices must
  talk to each other. Gamma theorized to carry the
  information.

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Other EEG Phenomena

• Petit Mal epileptic activity (absence seizuresee
  my imitation or see movie The Andromeda Strain)
  is a 3 Hz repeating cycle of spikey looking
  EEG waves followed by domesspike and dome, spike
  and dome, spike and dome, 3 times a second,
  accompanied by rhythmic blinking, also at 3 Hz,
  and the apparent inattentiveness (absence) of the
  sufferer.
• This isnt Grand Mal epilepsy, characterized by
  falling down, foaming at mouth, spasms of
  muscles, biting the tongue, and huge, wild EEG
  spikes, and occasional death.

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Other EEG Phenomena

• Sensory-motor rhythm (SMR), which is synchronous
  12-14 Hz (like high alpha) activity seen over
  somatic sensory and motor cortices. The lack of
  normal amounts of SMR is also associated with
  epilepsy.

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EEG Derivatives

• Event-Related Potentials(ERPs)
• If as the spontaneous EEG is coming along, one
  presents a discrete stimulus to the subjectlike
  a light flash or tone pip-- the EEG breaks up
  into a series of much larger peaks and troughs.
  This series of waves is the ERP, related to the
  stimulus event, whatever it was. The number of
  peaks and troughs depends on the complexity of
  the stimulus. For simple stimuli, there may be
  just 2-4. For complex psychological stimulilike
  names of people there may be 5-8.

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EEG Derivatives-2

• If you go to http//mitpress.mit.edu/catalog/item/
  default.asp?ttype2tid10677
• .and download that sample chapter and go to Fig.
  1.1B, youll see an example of a continuous EEG
  wave where there is a stimulus presented about
  every second. Note the ERPs towering over the
  background EEG when O is presented (see
  caption).
• I also provided a handout where you can see EEG
  breaking into an ERP.

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EEG Derivatives-3

• To clearly see the O-evoked ERP components
  (called P300), you average all the O waves into 1
  bin, and all the X waves into another.
• An example of the average ERPs is in next slide

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Actually , this is from my work. It shows a big
ERP component (arrow) called P300 which follows
psychologically meaningful stimuli, like your
birth date, and the other flatter average is to
other, irrelevant dates.
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Note other down-going and up-going waves in the
ERP.

• Each is called a component. The earliest
  wavesnot easily visible here, because I heavily
  filter them outrepresent the sensory information
  reaching specific sensory cortex from lateral
  pathways. Each wave/component represents the
  discharge of a certain synaptic organization. The
  next set of waves represents the sensory
  information mediated via medial reticular
  pathways. Both kinds of components are
  exogenous ERPs, because they represent
  external, sensory info. (Why do reticular
  components come later?) These exogenous ERPs are
  also called (stimulus-) evoked potentials .
  People used that term to describe all time-locked
  EEG events, but then when the endogenous (see
  below) and motor potentials were encountered,
  Herbert Vaughn coined the more general ERP term.

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Endogenous ERPs

• These are the latest (in msec) set of waves or
  components in the ERP, and are of most interest
  to PSYCHOphysiologists, because they represent
  psychological reactions to externally presented
  but meaningful stimuli.
• There are not that many, maybe 5 or 6 discovered
  to date, though folks argue about the number.
  Here are a few

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Endogenous ERPs

• P300 this positive wave comes following rarely
  (lt 40 of time) presented, meaningful stimuli.
  P300 was first discovered by Sutton et al. in
  1965, who presented high and low tones in a
  random series, about 2-3 sec apart, in the ratio
  82 and told Ss to internally count the rare
  tones. Whether high or low was rare, the P300
  always followed the rare, counted tone. (Why was
  this a critical demonstration?)
• The stimuli were simple tone pips so indeed the
  latencies were 250-300 ms But complex stimuli can
  lead to 400-800 ms latencies. I.e. P300 LATENCY
  REPRESENTS STIMULUS COMPLEXITY, and processing
  time..

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More P300

• The amplitude of P300 is directly proportional to
  rareness, and directly proportional to
  meaningfulness.
• You can make a stimulus meaningful by assigning a
  task to itlike counting it. Some stimuli are
  intrinsically meaningfullike self-referring
  information names, birthdays, phone numbersand
  crime details which led to our P300-based lie
  detector, more on which later.

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More P300

• The scalp distribution of P300 (or any ERP) helps
  to define it and may represent cognitive
  phenomena P300 is usually largest over Pz and
  smallest over Fz, but there are many ways that
  can be so

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Many curves where FzltCzltPz
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ERP-defining attributes

• 1. Polarity (P300 is positive, which for
  traditional reasons, we plot down-going).
• 2. Latency (300-800 ms for P300)
• 3. Scalp distribution (Pz gt Cz gtFz for P300)
• 4. Antecedent conditions or experimental
  manipulations (for P300, rareness (oddball
  stimuli and meaningfulness).

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Another cognitive ERP N400

• This is the response to semantic incongruity
  (antecedent condition). The following 3 stimuli,
  pieces of one sentence, are presented one at a
  time every second
• Today
• I ate
• My breakfast
• would NOT evoke N400, because the third stimulus
  is congruent and unsurprising. You DO get N400 if
  the 3rd stimulus is
• My motorcycle. (Get it?!)

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Another cognitive ERP CNV (this was actually the
first discovered.)

• There are 2 stimuli presented, tones, say 1-3 sec
  apart. The first, S1, alerts the subject that
  another (S2) is coming which will require a
  response, lest an aversive event (shock) occur.
  The EEG will drift negatively after S1 until S2
  is presented, whereupon it resolves. Since the
  stimuli could be 2 sec apart (frequency is .5 Hz,
  figure it out.), you need to pass very low
  frequencies to see this ERP, called the
  C(ontingent)N(egative)V(ariation).
• BTWThere must be CNV-like (anticipatory) states
  during spontaneous life, so there must be delta
  activity in normal waking people.

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Other cognitive ERPs

• ERN or error-related negativity. This one occurs
  after you make an error, and realize it.
• RP or Recognition Potential, discovered by my old
  pal, Al Rudell just a few years ago A series of
  random stimuli with a Chinese character
  interspersed elicits an RP in Chinese, but not
  English speakers. A series of random stimuli with
  an English character interspersed elicits an RP
  in English, but not Chinese speakers. Some say,
  Oh this is just a P300 (especially P300
  enthusiasts), but they are wrong, because the RP,
  though positive, occurs too early (250 ms) and
  varies across the scalp with stimulus location in
  visual field, unlike P300.

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Other cognitive ERPs

• MMN Mismatch Negativity. This occurs when an
  occasional oddball stimulus occurs in a series of
  like stimuli. Again, people say isnt this like
  a P300?
• The answer is no, because you need to be awake
  and attentive to make a P300, but MMN occurs in
  sleep or distraction. Also, MMN has a different
  scalp distribution, latency (100-200 ms), and,
  obviously, polarity (negative).

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Motor ERPs

• There are also ERPs seen over motor areas which
  precede actual movements. These represent the
  summed synchronous synaptic activity of pyramidal
  neurons, issuing commands to lower motor neurons.

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Another recently discovered EEG derivative the
Event Related Spectral Perturbation or ERSP

• This simply means a change in the frequency of
  spontaneous EEG related to an event.
• The alpha blocking pattern associated with
  arousal or reticular activation is an example.
• Measuring these was only possible with the advent
  of recent, very fast computers, because a fast
  Fourier transform is necessary every few msec.

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Fourier Transform

• The usual EEG is a plot of voltage as a f(time),
  as you know. A Fourier Transform of a section of
  EEG ( an epoch of say 60 seconds) results in a
  plot of magnitude (or energy or power) as a
  f(frequency) in that epoch. Frequency Spectra are
  the resulting plots that show where in the EEG
  spectrum one finds maximum power in different
  psychological states

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Fast Fourier Transforms

• It used to take weeks to do an FT (not FFT) even
  with a calculator, as matrix inversion was
  involved. Even with a computer (circa 1960s) it
  took several days. Then 2 programmers came up
  with a shortcut, the FFT. With modern fast
  computers and tools like MATLAB, they can be done
  in just a few milliseconds, so that one can go
  back to the time domain and plot frequency as f
  (time)

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Why bother? Cant you just look at the EEG and
see alpha blocking in the usual way?

• Yes, the arousal response of alpha blocking is
  one of the very few EEG effects that are obvious
  from the usual display of EEG voltage as a
  f(time). Likewise for development of epileptic
  seizure activity. But subtle changes in cognitive
  or emotional state have very subtle EEG effects
  which you just cant eyeball.

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OK, but why cant you just average EEG from the
stimulus time, as with ERPs?

• You can average ERPs because the stimulus
  generates a series of synaptic events always
  time-locked and phase locked to the evoking
  event. But its impossible to predict what the
  spontaneous EEG is doing (e.g., is it high or
  low?) when the perturbing event (stimulus) is
  presented, so lack of phase locking in
  spontaneous EEG prevents averaging from the
  stimulus, since out-of-phase signals will average
  to a straight line. Thats why we need the new
  technology to see ERSPs. And many new neural
  signs of cognitive and emotional events have been
  reported (especially in Europe) with the advent
  of the new methods.

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OK..New Topic!

• So..
• Shift gears!

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Are ERPs and EEG neural activities?

• Yes, of course they are, but prior to 1965, there
  was considerable doubt. The great invertebrate
  neurophysiologist T.H. Bullock used to visit our
  lab and joke to us Maybe that wiggle in the EEG
  simply represents a red blood cell passing near
  your electrode, first presenting positivity, then
  negativity from the flip side, as it passes your
  electrode.
• (Just as until 2000, there was the same doubt
  about fMRI, --Is it Neural?-- which is why you
  are assigned to read
• Heeger,D.J. et al. (2000) Spikes versus BOLD
  what does meuroimaging tell us about neuronal
  activity? Nature Neuroscience, Vol 3, pp
  631-633.)
• ..but back to EEG and ERPs

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Were there data?

• Mostly negative data people would record from
  one macro-electrode on cortical surface, and
  another micro-electrode just outside a nearby
  neuron. The spike and EEG records would be
  separately recorded on chart paper, and people
  would look (in vain) for hours to find a
  correspondence by which I mean a pattern-- such
  as you only get spikes when the EEG peaks occur,
  and nothing during the EEG troughs.

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New development The C.A.T. computer and Foxs
pussycat experiment with it.

• The Computer of Average Transients (CAT
  transient means temporary signal like an ERP)
  was adapted by Mary Brazier (a Fox mentor at
  UCLA) for ERP averaging. (The CAT was initially
  invented to average atomic wave phenomena.) Fox
  took one with him to Michigan, then Iowa.

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WOW! The CAT had 400 bytes (not MB or GB) of
memory!

• These bytes were in 100 byte segments, so you
  could have 4 average erpssay to 4 different
  stimuliseparately averaging in 4 separate
  channels. (Before the CAT, people would simply
  superimpose repeated sweeps into a fuzzy mess on
  a storage oscilloscope.)
• The CAT could make one other thing the post
  stimulus time histogram (PSTH)

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PSTH is a latency distribution, much like a
normal bell curve is a grade distribution.
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Latency distribution

• After, say, 1000 trials, the numbers in each bin
  in the memory string (called a buffer) would
  contain the number of times in 1000 sweeps that
  spikes occurred at that particular time.
• This is a decent approximation to the probability
  of a spike at, say, 200 msec (or whatever time
  bin), post stimulus. If a spike occurs on 500
  trials at 200 msec post-stimulus, the probability
  of a spike at 200 ms around .5.
• So in the PSTH, you have a sequence of spike
  probabilities for various times following the
  stimulus. It is indeed a probability
  distribution, a distribution of firing
  probabilities at various times(latencies) after
  the stimulus. The post stimulus time histogram
  (PSTH) is like any other histogram distribution.

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CAT software

• To decide whether one of the 4 CAT channels would
  accumulate average ERPs or PSTHs, the operator
  pulled the side case of the CAT away and either
  pulled out a 3 by 5 inch circuit board (for the
  PSTH) or left it in (for the average ERP, or
  AERP).
• (Some program!)

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The Fox-OBrien (1965) cat (the meow type)
preparation

• They prepared a cat, under general anesthesia,
  with scalp retracted, hole drilled in skull,
  through which a micro-electrode was passed until
  a single neuron was encountered. Then the cats
  eyes were opened and maintained with lubricant,
  and a light flashed every 2 seconds.

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The Fox-OBrien (1965) cat (the meow type)
preparation

• The electrode output was divided into 2 channels,
  a high pass channel and a low pass channel.
• The former was interfaced to the CAT PSTH
  generator, the latter to an AERP generator

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The Fox-OBrien (1965) cat (the meow type)
preparation
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They took the new CAT out of the box, operated on
and set up the CAT connected to the cat

• Then, the went out to dinner, as the lights
  flashed in the cats face, every 2 sec, and the
  PSTH in one channel, and AERP in the other
  continued developing.

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Returning from dinner (1000 trials), they saw
the following on the screen
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They were shocked that the PSTH looked exactly
like the AERP. (The resemblance was better than
my drawing, check the required FoxOBrien paper
)

• O Brien (grad stud) said, Oh we must have done
  something wrong, so he hit re-set! Fox screamed,
  but then calmed down, knowing that any real
  phenomenon replicates. So they started all over,
  but this time sat there and watched the PSTH and
  AERP develop from a flat line to the same pair of
  correlated patterns you saw in previous slide.
  This was repeated with dozens more neurons, and
  in many other laboratories.

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What had they really shown?

• For the first time they showed that the
  moment-to-moment amplitude of the sensory evoked
  ERP was a good predictor of neuronal
  excitability, and so the ERP was indeed neural.
• How did they show this? Well they showed that the
  ERP voltage at any time following the stimulus
  was tightly correlated with the probability of a
  spike (from the PSTH) at that time. Of course the
  probability of a spike is a direct definition of
  excitability which is classically defined as
  closeness to firing threshold Clearly, the
  closer a neuron is to firing, the greater its
  excitability (Duhh!)

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Were there nay-sayers?

• Always. If you discover something important,
  others will snipe.
• In this case they said, well despite your
  filtering, some eeg leaked into the PSTH channel,
  and some spikes leaked into the wave channel.
• This is patently idiotic, but was easy to deal
  with. They just tapped on the electrode, killing
  the cell, and repeated the procedure.

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The result

• Of course there was no more PSTHthere were no
  spikes to leak from the dead cell, but the AERP
  replicated. Where did this signal come from?
  Other nearby neurons-- too far for the spikes to
  reach (thus, no PSTH) but the PSPs from these
  other cells made it fine all the way.
• I used to teach that the brain itself passes low
  frequencies better than it does high frequencies.
  (Also true of sound energywhich is why foghorns
  dont tweet, but are bassos in need of a good
  sub-woofer.) Very cute line, but proven wrong
  recently by Nikos Logothetis.

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This had a major implication

• .which was that ERPs are not spike
  envelopessums of spikes.
• No, ERPs are the complex sums of PSPs from the
  population of neurons in the neighborhood of the
  recording electrode. But a PSP is a perfectly
  respectable neural event.
• So when I asked before Are ERPs and EEG neural
  activities? the answer is clearly yes. (We
  didnt yet prove it for spontaneous EEG, but
  thats next.)

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The Fox-Norman 1968 experiment

• This study extended Fox-Obrien to the situation
  with spontaneous EEG, rather than evoked ERPs.
• No more CAT computer, but a real programmable
  one, the DEC PDP-8 that had.4KB (!) of memory.
  (Not MB, let alone GB, let alone TB).
• But with spontaneous EEG, there is no time 0,
  so what to do?

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That is.. Things just keep rollin along
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Well they re-used the Fox OBrienset-up with
key difference
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The 2 channels 2 distributions were developed as
follows

• (1.) Every millisecond they sampled the EEG
  channel and added the current value of the
  amplitude into gradually accumulating
  distribution The got a nice normal frequency
  (oftenness) distribution of amplitudes.

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They were SIMULTANEOUSLY collecting a second
distribution

• (2.) by looking at both the EEG channel AND the
  spike channel to see if there was a spike
  occuring. If they saw no spike, they did nothing.
  If they DID see a spike, then they incremented
  the counts of those amplitudes with simultaneous
  spikes. Is this distribution larger or smaller
  than the first?

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Suppose we talked about coin flips

• How does the frequency of head tosses compare to
  the frequency of all tosses, heads tails?
• (Duhhhhhh!)

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