EEG/ERP Analysis with Python and MNE: An Introductory Course

Explore brain frequencies from delta to beta, explain frequency and amplitude concepts, and link alpha and mu rhythms to idling and attention, with notes on hippocampus and memory consolidation.

Explore frequency analysis of EEG using Fourier transform to reveal theta, alpha, and beta power spectra and visualize them with topographic plots for ADHD biomarkers.

Explore time-domain analysis of EEG and ERP, with stimulus-driven triggers, baseline intervals, and the oddball paradigm revealing ERP components such as P and N.

Blend frequency and time in EEG analysis to track how brain rhythms synchronize and desynchronize after a stimulus in an oddball task, using time-frequency representations.

Identify and remove EEG noise sources, including eye movement artifacts with frontal topographies and horizontal movement patterns, muscle noise in frontal/temporal regions, heartbeat, electrode contact issues, and fatigue-related alpha.

Explore EEG/ERP analysis with Python and MNE as you observe artifacts in sync with Haydn's Surprise Symphony, visualized in EEG signals and power spectra.

Explore low pass, high pass, and band pass filters for EEG data to reduce noise and isolate brain activity, including the alpha frequency band, with Python analysis.

Install Anaconda to get Python ready for EEG/ERP analysis, following the step-by-step download and installer guidance for Windows, then proceed to coding basics in the next video.

Learn Python basics for EEG ERP analysis, from setting up an Anaconda and Jupyter environment to writing and running code. Master variables, strings, lists, concatenation, printing, types, and data checks.

Explore Python dictionaries for EEG data, storing sampling rate and experimental conditions, and master accessing keys, values, and items, plus indexing and slicing techniques.

Explore how Python functions power EEG/ERP analysis, from creating arrays and using numpy functions like max, mean, and linspace, to indexing 2D EEG data by channel.

Learn how control statements in Python, including if statements and for loops, enable efficient EEG analysis by looping over channels and generating plots.

Explore plotting with python’s matplotlib to visualize EEG and ERP data, including sine waves, sampling rate effects, axis labeling, subplots, legends, and figure sizing.

Install the mne Python package with pip to set up your Anaconda and Jupyter environment, then import EEG data, apply filters, handle bad channels, and use ICA for noise removal.

Import and inspect EEG data in Python using MNE; learn to import EDF files, visualize raw EEG, and identify artifacts for cleaning via filtering and ICA.

Apply a low-pass filter with MNE to remove high-frequency noise from EEG data, use a 0–30 Hz (or 0.1–40 Hz) range, and compare raw versus filtered signals.

Save your work at every step to protect data integrity and enable collaboration, saving raw and filtered EEG data with clear file naming and fif extensions in MNE Python workflows.

Apply filtering to EEG data, then use ICA to identify and remove artifacts such as eye blinks and eye movements, after setting a 1020 montage.

Learn how to manually remove remaining EEG artifacts through visual inspection after filtering and ICA, by annotating bad muscle and bad sensor segments and saving a clean file.

Import eeg data into Python by loading a single-channel fc text file with NumPy, plot it, and convert samples to seconds at 256 Hz; the next video covers fft analysis.

Demonstrates how to compute the Fourier transform with numpy's fft, extract positive frequencies, and plot the time-domain signal and amplitude spectrum from 1–40 Hz to reveal the alpha peak.

Explore frequency analysis in EEG with MNE by computing and plotting the power spectral density for 19 channels using the multitaper method, 0.5–30 Hz, and the sampling rate.

Learn to create custom frequency topographic maps in EEG analysis using Python and MNE. Visualize theta, alpha, and beta activity with PSD-based topographies and electrode positions.

Analyze the time course of brain responses to standard and oddball stimuli, from baseline through P3, illustrating conscious awareness and decision making.

Explore the P300 component in ERP, examining amplitude, latency, and topography, and its role in updating working memory during oddball experiments, cognitive processing, and consciousness detection.

Explore the N170 ERP component linked to facial processing, its timing around 160–170 ms, right-hemisphere amplitude, and how faces—real or Arcimboldo-inspired—affect brain responses in EEG studies.

Explore how language processing elicits the N400, a semantic brain response modulated by context, word frequency, and lexical features. Examine P600 and P3 as markers of syntactic and language processing.

Explore aging and development effects on ERPs, including P3, N400, and N170 latency and amplitude. Discuss age-appropriate paradigms and preview analyzing ERPs with Python and MNE.

Explore event-related potentials and time-frequency insights from trial-based EEG data in Python, comparing brain responses to happy face versus black circle stimuli across 1000 trials per condition.

Explore single-trial eeg data in Python by visualizing 1000 trials per condition (happy face and non-face), plotting time series, marking stimulus at 250 ms, and preparing for ERP averaging.

Compute mean ERPs in Python by averaging across trials to examine the time window around stimulus onset, comparing happy face and non-face conditions and plotting results.

Learn to handle continuous EEG data with event files, attach labels to EEG, and plot ERPs and time frequencies across four stimulus conditions.

Attach labels to EEG data and define epochs to study ERPs and time frequency patterns in python with MNE, using a 1020 montage, bandpass filtering, and labeled events.

Segment EEG data into epochs around events using MNE, with a -200 ms to 2 s window and -200 ms baseline, creating condition epochs for ERP analysis.

Compute and visualize erps in MNE by separating eeg epochs into condition one and condition two, then average across trials for a chosen channel and plot the results.

Explore time-frequency analysis of EEG with Python and MNE by computing Morlet wavelet based TFR, plotting power dynamics across 4–30 Hz, comparing condition one and two.

Conclude your journey through EEG and ERP analysis with Python and MNE, mastering preprocessing, artifact removal, frequency and time frequency analysis, and interpreting ERP components like P3 and N170.

Explore how AI and ChatGPT can enhance your Python learning and electrophysiological data analysis, with prompts to guide interactive coding, feedback, and deeper concept exploration.

Explore frequency analysis by comparing the SDS of electrodes C3 and C4 in the alpha range, using real EEG data and PSD visuals.