Neuroscience and Psychology: Electroencephalography (EEG)

Explore the brain’s cortex with its sulci and gyri and the four lobes—occipital, temporal, parietal, and frontal. Review the cerebellum, brain stem, and limbic system for balance, memory, and emotion.

Neurons (about 86 billion) communicate with action potentials and neurotransmitters across synapses to learn and adapt, while eeg records synchronized cortical pyramidal activity visible on the scalp.

Place scalp electrodes to record brain electrical activity using a non-invasive, passive EEG, which captures tiny signals amplified by an electroencephalograph and shown as a voltage time series.

Explore how EEG offers excellent time resolution with portable, affordable brain activity recording, and compare it to MEG, PET, and fMRI in terms of spatial resolution, invasiveness, and practicality.

See how synchronized postsynaptic potentials across pyramidal neurons create scalp electroencephalography (eeg) rhythms. Identify the delta, theta, alpha, beta, gamma bands and their links to sleep, meditation, attention, cognition.

Use EEG to diagnose brain disorders and epilepsy by detecting abnormal patterns and seizures; employ ambulatory EEG up to 72 hours to capture events and assess psychiatric comorbidity.

Explore how EEG links brain timing to cognitive tasks, showing ERP and the N170 peaking at 170 ms that differentiates face processing from other objects, revealing modular face networks.

Explore how EEG measures attention, distraction, stress, and cognitive load to track performance. See EEG support neuromarketing, brand testing, and interfaces that enable mind-controlled devices, including a quadriplegic driver.

Trace Caton's 1875 discovery of brain electrical activity, its variation with wakefulness, sleep, and stimuli, and Berger's 1924 human EEG recording establishing the technique.

Learn how EEG electrodes on the scalp record brain activity with wet gels or dry sensors, and how channel count and even distribution affect data quality and artifacts.

Explore EEG electrode naming by brain region codes (Fp, F, P, O, T, C) and midline z, left/right placement, eye movements and reference electrodes, and offline re-referencing.

Explore the international 10-20 system for EEG electrode placement, using nasion, inion, and pre-auricular landmarks with 10-20% and 1-2% increments, skin-friendly marks, and cap-based electrodes centered on Cz.

Capture neural voltages with electrodes, digitize at rates from 128 to 1000 Hz (samples per second), then amplify and forward the data to a recording computer.

Prioritize clean EEG data by recognizing artefacts from muscle activity and eye movements, including blinks. Minimize non-physiological interference with snug electrodes, shielding, and eye-tracking when possible.

Record clean EEG data by minimizing impedance between scalp and electrodes. Ask participants to arrive with washed, dry hair and no products, and monitor impedance indicators for quality.

Learn to distinguish frequency-based analysis from ERP analysis and to preprocess EEG data, including automated denoising, by filtering, removing bad channels, and re-referencing.

Utilize frequency-based analysis of EEG data across delta to gamma bands to assess cognitive-affective states and engagement, including frontal lateralisation and alpha blocking insights.

Explore a normal wakeful EEG segment, highlighting artefact-free epochs and key parameters (1 Hz low, 70 Hz high, 7 μv/mm, 30 mm/s), and the anterior-posterior bipolar montage with channels.

Perform a frequency-based analysis of the alpha rhythm in EEG: reactive 8–12 Hz activity, increasing with eye closure, measured with average reference; amplitude 40–50 microvolts, maximal at occipital regions.

Explore how event-related EEG uses time-locked events and embedded codes, with epoching and preprocessing, to extract ERPs from noisy continuous data.

Baseline correction re-centres epochs by subtracting the baseline mean, aligning all segments on the same scale. Then artefact rejection removes extreme values, and averaging trials and participants yields cleaner ERPs.

Explore how ERP analysis uses peaks and components such as N1, P1, N2, P2, N400, and P3, with amplitude, latency, and topographic distribution, requiring repeated, time-locked stimulus timing.

Conclude your study of electrophysiology in neuroscience and psychology, invite questions by email or on the Udemy course page, and encourage rating to support the course.