Analysis of Scale-Invariance in EEG microstates due to Acoustic Stimuli
Author
Term
4. semester
Education
Publication year
2022
Submitted on
2022-06-03
Pages
102
Abstract
This thesis analyzes and assesses changes in the self-similarity of EEG microstate sequences due to acoustic stimuli. By embedding the microstates into a random walk, an estimate of the Hurst exponent is obtained by means of estimation. The topic of estimating the Hurst exponent played a significant role in this thesis. Two methods were introduced for the purpose of estimating the Hurst exponent; one was an established method based on the wavelet transform; the was through the implementation of a convolutional neural network. Using the two methods, an ANOVA was performed on EEG data recordings of a listening task to assess whether the type of sound was significant. Ultimately, the results of the analysis proved inconclusive, and further work is needed on the topic.
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