Posts by Year – 2019

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

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

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

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

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

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

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

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

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

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

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

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

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

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

And: interestingly, also visual alpha can be highly asymmetric (Fig. S1): pic.twitter.com/pavM5mm1Fb

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!

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