[Investigadores] Invitación conferencia EEG (12:00-13:00 viernes 14 abril 2023)

[Maria Ines Rivera]

Estimados MIembros CMM-DIM,

Se les invita a  la conferencia EEG, este viernes 14 abril, a las  12pm.

*Title:*
Recovering Arrhythmic EEG Transients from Their Stochastic Interference
*Conferencista:* Juan Carlos Letelier, Facultad de Ciencias, Universidad 
de Chile
*Date & Time:* Friday, April 14, 12pm

*Venue:* Room B06, Beauchef 851, Escuela de Ingeniería, Universidad de Chile

Hybrid modality, zoom link:
https://uchile.zoom.us/j/98591910073?pwd=Qk1MQnVDU2RtRjhQcmhXT3o2NXIxZz09

Organized by the Millennium Institute of Intelligent Healthcare (iHealth)

*Abstract*
Traditionally, the neuronal dynamics underlying electroencephalograms 
(EEG) have been understood as arising from rhythmic oscillators with 
varying degrees of synchronization. This dominant metaphor employs 
frequency domain EEG analysis to identify the most prominent populations 
of neuronal current sources in terms of their frequency and spectral 
power. However, emerging perspectives on EEG highlight its arrhythmic 
nature, which is primarily inferred from broadband EEG properties like 
the ubiquitous 1/f spectrum. In the present study, we use an arrhythmic 
superposition of pulses as a metaphor to explain the origin of EEG. This 
conceptualization has a fundamental problem because the interference 
produced by the superpositions of pulses generates colored Gaussian 
noise, masking the temporal profile of the generating pulse. We solved 
this problem by developing a mathematical method involving the 
derivative of the autocovariance function to recover excellent 
approximations of the underlying pulses, significantly extending the 
analysis of this type of stochastic processes. When the method is 
applied to spontaneous mouse EEG sampled at 5 kHz during the sleep-wake 
cycle, specific patterns --- called PSI-patterns --- characterizing NREM 
sleep, REM sleep, and wakefulness are revealed. Psi-patterns can be 
understood theoretically as power density in the time domain and 
correspond to combinations of generating pulses at different time 
scales. Remarkably, we report the first EEG wakefulness-specific 
feature, which corresponds to an ultra-fast (1 ms) transient component 
of the observed patterns. By shifting the paradigm of EEG genesis from 
oscillators to random pulse generators, our theoretical framework pushes 
the boundaries of traditional Fourier-based EEG analysis, paving the way 
for new insights into the arrhythmic components of neural dynamics.

Esperando contar con su participación, les saluda,

Ma. Inés Rivera

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