EEG records electrical activity from scalp electrodes with millisecond temporal resolution but limited spatial specificity. fMRI measures blood oxygen-level-dependent (BOLD) changes reflecting regional neural activity with good spatial but poor temporal resolution. Both require careful interpretation: EEG measures summated synaptic currents; fMRI indirectly reflects metabolic demand.
Analyze real EEG and fMRI datasets from open databases. Practice source localization for EEG.
fMRI shows where thoughts are—it shows where blood flows. EEG shows everything—it's mainly sensitive to synchronous local currents.
From your understanding of resting membrane potential and the organization of the central nervous system, you know that neurons generate electrical signals and that the brain is organized into functionally distinct regions. Neuroimaging methods allow us to observe brain activity in living humans without surgery, but each method captures a different shadow of the underlying neural reality — and understanding what each method actually measures is essential to interpreting results correctly.
Electroencephalography (EEG) places electrodes on the scalp to record voltage fluctuations generated by the brain. What the electrodes detect is not individual action potentials — those are too brief and too deep to reach the scalp — but rather the summed postsynaptic potentials of thousands of neurons firing in synchrony. When large populations of cortical pyramidal neurons receive excitatory input simultaneously, their aligned dendritic currents sum to produce electrical fields strong enough to be measured at the surface. EEG's great strength is temporal resolution: it captures changes on the order of milliseconds, making it ideal for studying the timing of cognitive processes, sleep stages, and seizure activity. Its weakness is spatial resolution — because electrical signals spread and distort as they pass through cerebrospinal fluid, skull, and scalp, pinpointing the exact source of an EEG signal (the "inverse problem") is mathematically underdetermined.
Functional magnetic resonance imaging (fMRI) takes the opposite approach. It exploits the fact that active neurons consume more oxygen and glucose, triggering local increases in blood flow and blood oxygenation. Oxygenated and deoxygenated hemoglobin have different magnetic properties, so an MRI scanner can detect the blood-oxygen-level-dependent (BOLD) signal — a proxy for regional metabolic demand. fMRI achieves spatial resolution of a few millimeters, allowing researchers to localize activity to specific brain structures. However, the hemodynamic response peaks about 5–6 seconds after neural activity occurs, so temporal resolution is poor compared to EEG. The BOLD signal also reflects aggregate metabolic activity rather than specific neural computations — a region "lighting up" on fMRI means increased blood flow, not necessarily increased firing of a particular neuron type.
In practice, EEG and fMRI are often complementary. EEG tells you when something happened in the brain with millisecond precision; fMRI tells you where it happened with millimeter precision. Researchers sometimes combine both in simultaneous EEG-fMRI recordings to get the best of both worlds, though this introduces technical challenges (the MRI's magnetic field distorts EEG signals). The critical lesson for interpreting any neuroimaging study is to remember that these tools measure indirect correlates of neural activity — electrical field summation for EEG, metabolic demand for fMRI — and that the inferential leap from signal to cognitive function requires careful experimental design and statistical rigor.