• Rasmus Vestergaard Lykke
4. semester, Mathematical Engineering, Master (Master Programme)
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.
Publication date2022
Number of pages102
ID: 472097161