p-values for 10000 simulated experiments comparing two conditions in 25 subjects for an increasing effect size. Starting out with a uniform distribution, the p-values shift to smaller and smaller values for increasing differences between conditions. pic.twitter.com/ynIsUr4JG3
— Natalie Schaworonkow (@nschawor) December 21, 2019
Inter-trial coherence measures phase-synchronization across trials. It is the circular sum of phases at a certain point in time (length of red arrow). It reaches its maximum value of 1 for perfectly phase-aligned signals and becomes 0 as the phase distribution becomes uniform. pic.twitter.com/jv2unSVC7O
— Natalie Schaworonkow (@nschawor) December 17, 2019
I wonder if this newly suggested mechanism for alpha blocking (https://t.co/GGF1kM0lI2) could be evaluated further in the time domain, as it may result in different waveform shape properties compared to other mechanisms. 🤔 Anecdotal example below for eyes open vs. closed. pic.twitter.com/lf0g9gKgjD
— Natalie Schaworonkow (@nschawor) December 11, 2019
the only paper books that I took with me to California. I guess you can figure out my very narrow interests. 🙂 Is there an equivalent of the left book for ECoG and LFP? (= a great intro with lots of visualizations)
A large portion of measured EEG signal is of non-neuronal origin. Signal separation techniques like independent component analysis can help to identify these. Here, noise components are slowly projected out, revealing the interesting bits, the alpha oscillations. pic.twitter.com/CjBI80pbUO
— Natalie Schaworonkow (@nschawor) December 4, 2019
Why does phase-amplitude coupling arise for non-sinusoidal signals? Amplitude of the band-pass filtered harmonic will be impacted in a systematic way by the base oscillation. Here: increased beta at the trough and decreased beta at the peak of the base alpha-oscillation. pic.twitter.com/f1ZFaeiRcb
— Natalie Schaworonkow (@nschawor) December 3, 2019
Phase-amplitude coupling for bursty non-sinusoidal signals, here: sensorimotor mu-rhythm. Mean beta-amplitude will differ across alpha-phase for high alpha-amplitude events. This is due to cycles with sharp troughs and long arcs (dark blue circles). pic.twitter.com/xRqm2Xw8fT
— Natalie Schaworonkow (@nschawor) November 27, 2019
A classic effect in the sensorimotor cortex: event-related desynchronization. Strong mu-rhythm oscillations attenuate as soon as the participant starts to contract a muscle. Where do they go? 🧐 pic.twitter.com/4TwU0gZnOz
— Natalie Schaworonkow (@nschawor) November 21, 2019
Highly arc-shaped mu-rhythm oscillations from human sensorimotor cortex, electrocorticography data. Band-pass filtering these oscillations seems quite drastic, since they have so many interesting features in the time domain. 🤔 pic.twitter.com/OP6CbSVjZn
— Natalie Schaworonkow (@nschawor) November 14, 2019
A delta function and white noise have the same power spectrum. But the phase spectrum is very different, with very strong alignment for the delta function and a uniform distribution for white noise. pic.twitter.com/TwmUuqPE1k
— Natalie Schaworonkow (@nschawor) November 4, 2019
Reaction times in response to a stimulus are generally much faster when there are no oscillatory bursts close to stimulus onset. (sensorimotor EEG mu-rhythm, simple stimulus-response task, the 50 fastest and slowest trials from a longer experiment) pic.twitter.com/3g5wzMt0xE
— Natalie Schaworonkow (@nschawor) October 23, 2019
Measured waveform shape in neuronal oscillations depends on signal-to-noise ratio. Only for high amplitude, peak-trough asymmetry can be reliably detected. Harmonic frequencies cancel fast... 3 subjects, EEG resting state data, channel: O1, each dot = 1 alpha-oscillation cycle. pic.twitter.com/y5dY67lVFs
— Natalie Schaworonkow (@nschawor) October 4, 2019
Resting state EEG alpha-oscillation waveform shape measures across channels, mean over 110 participants. Besides the known voltage peak maxima at occipital channels and decrease in cycle period over central channels, there is an interesting hemispheric rise-decay asymmetry. 🤔 pic.twitter.com/Da8rcgikdt
— Natalie Schaworonkow (@nschawor) September 18, 2019
Visual alpha oscillations when eyes are closed versus when eyes are open, recorded with EEG on the scalp surface (broadband filtered). A striking increase in power of oscillations, which is known since almost a century. Very beautiful waveforms! pic.twitter.com/N3PCXVxqBH
— Natalie Schaworonkow (@nschawor) July 31, 2019
Are neuronal oscillations different than just filtered 1/f-noise? Yes, for instance in their signal characteristics: they display intricate long-range temporal correlations in their band amplitudes; measured by a detrended fluctuation exponent (DFA) close to 1. pic.twitter.com/pohWRDWf2V
— Natalie Schaworonkow (@nschawor) July 8, 2019
And: interestingly, also visual alpha can be highly asymmetric (Fig. S1): pic.twitter.com/pavM5mm1Fb
— Natalie Schaworonkow (@nschawor) May 16, 2019
Our newly published study: https://t.co/LbQTNKmKXb We quantify waveform shape in a large EEG dataset and explore what happens when you mix non-sinusoidal signals with different delays. E.g.: Alpha and beta amplitudes will not necessarily be correlated!
— Natalie Schaworonkow (@nschawor) May 16, 2019
EEG oscillations as recorded over the sensorimotor cortex, filtered with a broadband and narrowband filter. (guilty pleasure: ordering slightly unnecessary material from @sparkfun) pic.twitter.com/aqQdS1Idhx
— Natalie Schaworonkow (@nschawor) April 30, 2019