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Electrophysiology. Electroencephalography. Electrical potential is usually measured at many sites on the head surface More is sometimes better. Magnetoencephalography. MEG systems use many sensors to accomplish source analysis
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Electroencephalography • Electrical potential is usually measured at many sites on the head surface • More is sometimes better
Magnetoencephalography • MEG systems use many sensors to accomplish source analysis • MEG and EEG are complementary because they are sensitive to orthogonal current flows • MEG is very expensive
EEG/MEG • EEG changes with various states and in response to stimuli
EEG/MEG • Any complex waveform can be decomposed into component frequencies • E.g. • White light decomposes into the visible spectrum • Musical chords decompose into individual notes
EEG/MEG • EEG is characterized by various patterns of oscillations • These oscillations superpose in the raw data 4 Hz 4 Hz + 8 Hz + 15 Hz + 21 Hz = 8 Hz 15 Hz 21 Hz
How can we visualize these oscillations? • The amount of energy at any frequency is expressed as % power change relative to pre-stimulus baseline • Power can change over time 48 Hz % change From Pre-stimulus 24 Hz 16 Hz Frequency 8 Hz 4 Hz +200 +400 +600 0 (onset) Time
Where in the brain are these oscillations coming from? • We can select and collapse any time/frequency window and plot relative power across all sensors Win Lose
Where in the brain are these oscillations coming from? • Can we do better than 2D plots on a flattened head? • we (often) want to know what cortical structures might have generated the signal of interest • One approach to finding those signal sources is Beamformer
Beamforming • Beamforming is a signal processing technique used in a variety of applications: • Sonar • Radar • Radio telescopes • Cellular transmision
Beamformer • Applying the Beamformer approach yields EEG or MEG data with fMRI-like imaging R L
The Event-Related Potential (ERP) • Embedded in the EEG signal is the small electrical response due to specific events such as stimulus or task onsets, motor actions, etc.
The Event-Related Potential (ERP) • Embedded in the EEG signal is the small electrical response due to specific events such as stimulus or task onsets, motor actions, etc. • Averaging all such events together isolates this event-related potential
The Event-Related Potential (ERP) • We have an ERP waveform for every electrode
The Event-Related Potential (ERP) • We have an ERP waveform for every electrode • Sometimes that isn’t very useful
The Event-Related Potential (ERP) • We have an ERP waveform for every electrode • Sometimes that isn’t very useful • Sometimes we want to know the overall pattern of potentials across the head surface • isopotential map
The Event-Related Potential (ERP) • We have an ERP waveform for every electrode • Sometimes that isn’t very useful • Sometimes we want to know the overall pattern of potentials across the head surface • isopotential map Sometimes that isn’t very useful - we want to know the generator source in 3D
Brain Electrical Source Analysis • Given this pattern on the scalp, can you guess where the current generator was?
Brain Electrical Source Analysis • Given this pattern on the scalp, can you guess where the current generator was? Duracell
Brain Electrical Source Analysis • Source Analysis models neural activity as one or more equivalent current dipoles inside a head-shaped volume with some set of electrical characteristics
Brain Electrical Source Analysis Project “Forward Solution” This is most likely location of dipole Compare to actual data
Brain Electrical Source Analysis • EEG data can now be coregistered with high-resolution MRI image
Intracranial and “single” Unit • Single or multiple electrodes are inserted into the brain • “chronic” implant may be left in place for long periods
Intracranial and “single” Unit • Single electrodes may pick up action potentials from a single cell • An electrode may pick up thecombined activity from several nearby cells • spike-sorting attempts to isolate individual cells
Intracranial and “single” Unit • Simultaneous recording from many electrodes allows recording of multiple cells
Intracranial and “single” Unit • Output of unit recordings is often depicted as a “spike train” and measured in spikes/second Stimulus on Spikes
Intracranial and “single” Unit • Output of unit recordings is often depicted as a “spike train” and measured in spikes/second • Spike rate is almost never zero, even without sensory input • in visual cortex this gives rise to “cortical grey” Stimulus on Spikes
Intracranial and “single” Unit • By carefully associating changes in spike rate with sensory stimuli or cognitive task, one can map the functional circuitry of one or more brain regions • What are the advantages and limitations of this approach?