This repository hosts materials for the "A practical guide to EEG analysis tools used in the neuroscience of consciousness and cognition" tutorial, for ASSC26.
The slides for the course are below:
In the neuroscience of consciousness, electroencephalography (EEG) is an invaluable tool to non-invasively study human brain function. Indeed, the EEG allows the study of mechanisms that may hamper specific brain functions – for example, slow waves in the awake EEG are often linked to pathological dynamics. EEG is currently used as a clinical tool to monitor levels of consciousness during anesthesia and in patients with disorders of consciousness. In the process of using EEG to understand conscious states, methodological choices in data analysis figure heavily in the development of scientific theories and vice versa. With recent developments in methods for analyzing EEG data, it is essential that practitioners appropriately apply analysis tools.
In this tutorial, the presenters will provide a hands-on introduction to key methodological concepts and popular tools used in the analysis of EEG brain signals. Working from interactive notebooks, this tutorial is designed as an approachable guide to neural data analysis for both investigators and trainees. Attendees will also receive a brief introduction to each method’s statistical and theoretical underpinnings. We will provide an overview of EEG data analysis techniques that target biomarkers of consciousness, grouped into four categories:
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Frequency Domain Methods - including analyzing aperiodic neural activity: States of consciousness dramatically differ in overall signal power. This can be seen in the slope of the power spectrum, which reflects aperiodic (non-oscillatory) activity. Measuring aperiodic activity is important in order to detect band-limited oscillations, and as a feature of interest that relates to underlying spiking levels, and to the balance of excitation and inhibition. This tutorial will cover spectral parameterization (specparam, formerly fooof), a method for measuring aperiodic activity and neural oscillations.
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Time Domain Methods - including analyzing slow waves: EEG slow waves are a common feature of all unconscious states, and are thought to reflect up- and down-states in the spiking of cortical neurons. Recent research suggests that different levels of unconsciousness are differentiated by the entrainment of high-frequency activity to the slow wave in frontal vs posterior EEG. This tutorial will cover time-domain methods for decomposing the EEG signal into slow waves and high-frequency activity, and relating them to each other (Hilbert-based methods, State space oscillators, phase-amplitude coupling).
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Connectivity measures - including analyzing synchrony and global coherence: Measures of coupling between EEG recording sites often invoke the notion of synchrony as coherence or phase locking. These measures are sometimes used, in turn, to infer graph edges in network analyses. This tutorial will define and compare pairwise coherence, phase locking indices, and global coherence, as well as demonstrate some of their uses in the inference of functional connectivity.
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Complexity measures - including examining responses in TMS-EEG experiments: Over the past years, complexity-related measures have been shown to be reliable biomarkers of consciousness across several conditions. Among them, the perturbational complexity index has shown unprecedented sensitivity and specificity in detecting capacity for consciousness in healthy controls and patients with disorders of consciousness. This tutorial will cover measures of complexity (Perturbational Complexity Index, Lempel-Ziv complexity).
The interactive portion of the tutorial will be followed by a panel discussion and Q&A session regarding analytical approaches and their applications to consciousness science.
This tutorial has two components, slides and hands-on code examples.
If you want to try out the hands-on materials you will need a working Python installation (see below) and MATLAB.
The hands-on materials are organized as follows:
data:contains some example data files that can be used to explore the methodscomplexity:contains materials related to computing complexity measures on TMS-EEG dataconnectivity:contains materials related to applying functional connectivity measures to neural datafreq_domain:contains materials related to using frequency-domain spectral decompositiontime_domain:contains materials using state space models to capture time-domain rhythms and their interactions.
Part of the materials in this tutorial require the Python programming language, as well as Jupyter notebooks. Participants will need a working install of Python (>=3.6), with standard scientific packages (numpy, scipy, matplotlib), as well as some neuroscience specific modules.
To assist with getting an environment setup, we have provided an environment file which can be initialized as:
> git clone ***
> cd ASSC26_EEGMethods
> conda env create -f environment.yml
> conda activate ASSC26
> jupyter lab
The above assumes you have the conda package manager, which can be downloaded as part of the Anaconda distribution.
Some of the tutorial requires a version of MATLAB compatible with .mlx files.
This Tutorial was created by:
- David W. Zhou, PhD, Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Emily P. Stephen, PhD, Dept. of Mathematics and Statistics, Boston University, Boston, MA, USA
- Thomas Donoghue, PhD, Dept. of Biomedical Engineering, Columbia University, New York City, NY, USA
- Matteo Fecchio, PhD, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
Some of these materials are adapted from the short course "Advanced topics in the analysis of neural electrophysiology data: decomposing rhythmic and broadband components", also available here.