nature.com/articles/s4159…
In this project, we curated a list of 98 (!!) identified ERP components in the literature, and collected and summarized all the papers we could find on each.
Here are a couple example summaries, for the mismatch negativity, and the P600:
Feb 1, 2022 • 5 tweets • 2 min read
This 1938 paper by Grass & Gibbs is (I think) the first paper to compute Fourier transforms of EEG data!
They developed a device to compute frequency domain representations of EEG data (pictured) and analyzed over 300 EEGs (!!), arguing for the utility of this kind of approach.
Don't let the figure fool you - the method is very simple!
Just record EEG, project a shadowgraph (huh?), make a belt (okay...), go through a slit of light (what?), apply a filter (hey, I know that one!), add a galvanometer, and then easy (clearly magic) - you get amplitudes!
Sep 9, 2021 • 11 tweets • 5 min read
📜New Preprint!🎉
In an 'automated literature analysis' we collect & analyze >20,000 papers on event-related potentials (ERPs), building profiles & analyzing patterns across the literature (w @bradleyvoytek).
There are a LOT of papers on event-related potentials, such that dealing with the scale of the literature on can be difficult. We wanted to address this, in part because ERPs studies continue to be wildly popular, including in new applications (BCIs, education, etc).
Sep 7, 2021 • 8 tweets • 5 min read
Another underappreciated EEG pioneer is Pauline A. Davis (1896-1942). Amongst other work, Pauline reported the first evoked potentials (ERPs) recorded in humans, using auditory stimuli. I think this paper might be the first single-author EEG paper by a woman!
As well as relating evoked potentials to alpha, a really cool thing about this paper is that by using sequences of sounds, Pauline Davis notices tracking and anticipatory effects (sorta like oddball tasks) and talks about brain as doing prediction!
As someone interested in statistical analyses of EEG data & the history of the field, I think an important (and under-appreciated) researcher is Dr. Mary Brazier (1904-1995) who did both pioneering experiments & analyses, and rigorously documented the history of the field.
The 1952 paper "Cross- & auto-correlation studies of EEG" is amazing early work on statistical analyses, discussing ideas on phase-coherence, "noise", and oscillation properties including sinusoidality, temporal consistency, etc. Ahead of it's time!
TIL that Andrey Markov, of "Markov Chain" fame, was also known as 'Andrey the Furious' for his impassioned social commentary & anti-authoritarian writings.
The Markov Chain was developed to disprove a “mathematical proof of freewill“ which had been proposed to defend the church.
The debate between Nekrasov, arguing that patterns in social variables proves freewill due, and Markov, who argued this could be explained as random processes is fascinating, including how Markov expanded the law of large numbers (LLN) to non-independent "chained variables":
Mar 24, 2021 • 14 tweets • 6 min read
New Preprint 🎉
"Methodological considerations for studying neural oscillations"
With Natalie Schaworonkow (@nschawor) and Bradley Voytek (@bradleyvoytek), we review key methodological issues and concerns for analyzing oscillatory neural activity.
🧵: psyarxiv.com/hvd67/
We use simulated data to demonstrate and describe 7 key issues we think should always be considered for measuring neural oscillations.
We try and review and pull together recommendations, citing and combining topics from across the current literature.
In the name of transparency, let's have a look at these "transparency audits". Is it legit? Who's running it? Who's advising, and how? What are they doing?
tl-dr: it looks to be a rogue operation with an in-name only advisory board, and some questionable / shady tactics.
Thousands of students have taken this course at UC San Diego, and now we're making the materials more openly available.
Apr 14, 2020 • 12 tweets • 4 min read
I've spent my PhD learning what turns out to be work-from-home friendly science. I also incessantly keep notes & links.
So, here are lists of all the resources I know for learning data analysis, open-source development, open-access data and open-science practices, etc:
Quick note / disclaimer: this is somewhat tuned to cognitive neuroscience / electrophysiology - but a lot of it is also pretty general.