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Event related potentials: methods of recording and analysis

Event related potentials: methods of recording and analysis. By Kropotov Juri D. Definition. Event related potentials (ERPs) are scalp recorded voltage fluctuations that are time-locked to an event.

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Event related potentials: methods of recording and analysis

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  1. Event related potentials: methods of recording and analysis By KropotovJuri D.

  2. Definition Event related potentials (ERPs) are scalp recorded voltage fluctuations that are time-locked to an event. The event can be a stimulus presentation followed by assessment operations (such as estimation of color or shape of the visual stimulus), by executive operations (such as selection of appropriate response), as well as by affective or memory operations. The event can also be a motor or other type of subject’s response. 19 channel EEG recorded in a healthy subject performing a cued GO/NOGO task. The first stimulus presentation in a trial is marked by red vertical line. Time marks (seconds) are on the top.

  3. Signal-to-noise ratio • The ERP amplitude is usually smaller than the amplitude of background EEG so that it’s quite difficult by a naked eye to separate the ERP waveform from the ongoing EEG oscillations. In other words the ERP/EEG ratio (or the signal-to-noise ratio) in a single trial is rather small. To increase the ratio, EEG fragments are averaged over a large number of trials. The signal-to-noise ratio of the averaged ERP increases as the squire root of the number of trials. ERP is obtained by averaging the raw EEG fragments time locked to the stimulus. Red line – visual stimulus presentation. A: EEG fragments measured at O1 at a healthy subject in three trials with visual stimulus presentation (no response was required). B: ERPs at O1 in the same subject in response to visual stimulus presentation obtained by averaging different number of trials (10, 25, 50 and 200 trials). C: amplitude and time scales are the same for A and B

  4. Pyramidal neuron as a dipole • The modern neuroscience views an ERP waveform as a result of summation of excitatory and inhibitory postsynaptic potentials that occur simultaneously in large numbers of aligned cortical neurons. • These postsynaptic potentials are in turn the result of changes of neuronal membrane potentials in response to opening or closing of ion channels due to neurotransmitters binding with receptors on the postsynaptic neuronal membrane. • The release of inhibitory or excitatory neurotransmitters is initiated by spikes coming via axons to the neuron from corresponding excitatory and inhibitory neurons A. Excitatory and inhibitory post synaptic potentials. Left – neuron with excitatory (red) and inhibitory (blue) axonal inputs. Right - micro intra-neuronal electrodes and their recordings. The excitatory spikes are transformed into excitatory post synaptic potentials (EPSP), the inhibitory spikes are transformed into inhibitory post synaptic potentials (IPSP). When two excitatory post synaptic potentials occur in a small time frame, the neuron fires. B. Electrical dipole simulates postsynaptic potential at the apical dendrite of the pyramidal neuron. The excitatory post-synaptic potential produces opposite potential changes measured at extra-neuronal electrodes.

  5. Feedforward, feedback and lateral connections in the visual cortex • The visual input to the occipital cortex from the lateral geniculate body of the thalamus constitutes only a small fraction of the whole synaptic connections in this area. • Most of the synapses are formed by long distance connections from multiple sources (Douglas, Martin, 2007) so that information processing in the primary visual cortex is shaped by feed forward, feedback projections in the hierarchically organized sensory cortical areas as well as by lateral projections between parallels pathways.

  6. Volume conducted dipole potentials at the scalp • A given single current dipole generates the specific distribution of positive and negative voltages recorded on the scalp. The volume conduction results in a widespread, smeared voltage topography over the whole scalp with a maximum that is not necessarily occurred right over the activated cortical patch. By the laws of physics, the integral of the potential over the whole head is zero. Top: Radial primary current flow at the cortical convexity in the right central cortex. Bottom: Tangential primary current flow in a cortical fissure. Although the location of the active brain region is the same, the different orientations of the dipole lead to a completely different potential distribution.

  7. Temporal overlap of ERP components • The postsynaptic potentials last ten or hundreds millisecond. • Consequently, even a short burst of spikes generates an ERP component that may last up to hundred milliseconds. • That means that ERP components associated with different psychological operations overlap in time. • One way of separating the sources generating ERPs is given by Independent Component Analysis (ICA). Top: topographical potential maps for the single dipole sources localized in three spatially distinct cortical areas. Bottom: maps for the simulated ERPs at consecutive 40 ms bins. The activation of the three sources overlap in time. (from Richards, 2004).

  8. Independent component analysis (ICA) • ICA is a method for solving the blind source separation problem: to recover N independent source signals s = {s1(t); s2(t), …, sN(t) }from N linear mixtures, x = {x1(t); x2(t),…, xN(t)} obtained as the result of multiplying s by an unknown square mixing matrix A. • x=As • In the case of ERPs, the source signals s are activation patterns of cortical networks that process independent pieces of information and that are linearly summed at the surface of the head as x= As. Hypothetically these activation patterns sare associated with distinct psychological operations. • The goal of ICA is to recover a version u of the original sources, identical to s except for scaling and source order. To achieve this goal it is necessary to find a square matrix W that linearly inverts the mixing process. • u=Wx • The key assumption used in ICA is that sources siare statistically independent. Statistical independence requires that all second-order and higher-order correlations are zero.

  9. ICA for collection of EEG fragments in GO/NOGO task A: 700 ms fragments of EEG are selected for trials of NOGO condition. A matrix of these single-trial EEG data, x, is used to train an `unmixing' matrix W: u= Wx . B: only 4 independent components (ICs) obtained by ICA (Infomax algorithm) are shown. Each IC consists of a time dynamics (bottom) and a fixed scalp topography (left). Vertically stacking thin color coded horizontal bars correspond to the IC in all selected trials. The crossed IC is generated by eye blinks. Three other components account for evoked activity. C: Artifact-free EEG signals can be obtained by mixing and projecting back onto scalp the selected non-artifactualICs. This is done by multiplying the selected activation waveforms by the inverse mixing matrix W-1 in which the corresponding columns are zeroed. D: comparison of artifact corrected and non-corrected ERP wave-forms.

  10. ICA (Infomax) reliably decomposes components from the simulated single-trial ERP data • An artificial EEG dataset are modeled by nine cerebral sources. The topographical maps of the sources are shown on the left. • The sources were temporally overlapping on most of the trials to give rise to EEG fragments. • The infomax algorithm of ICA was applied on these EEG fragments. • The topographical maps of the extracted ICs are shown on the right (from Richards, 2004).

  11. Group ICA vs single-trial ICA • One of the disadvantages of single trial ICA decomposition is that the obtained independent components include not only time-locked activation patterns but also components corresponding to spontaneous EEG generators. • To get rid of spontaneous EEG one can compute ERPs in which the spontaneous EEG is reduced dramatically as squared root of the number of the averaged trials. A collection of individual ERPs represents the input data for the group ICA. • In this case to obtain enough data for training the unmixing matrix one should collect a large number of individuals. For our GO/NOGO task we estimate this number larger than 100. • An example of application of ICA to a collection of individual ERPs is presented in the next slide.

  12. ICA for collection of individual ERPs The results of decomposition of the collection of ERP computed for 134 healthy subjects into independent components. A: brief presentation of a pure acoustic tone elicits a N1/P2/N3 complex recorded at Cz. Maps at different latencies are quite different indicating presence of several sources. B. The ICA reveals 5 independent components (two components symmetrical components for the left side are not shown. They are generated in different cortical areas and their time courses overlap with each other. The ICs correspond to the sources found by means of intracortical recordings.

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