Berentschot, Melle (2021) Manipulation of perception using HsMM-MVPA analysis on EEG data. Bachelor's Thesis, Artificial Intelligence.
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Abstract
With the arrival of the cognitive revolution (1950) and the invention of the electroencephalogram (EEG), people have been able to measure the electrical activity of the brain. Evidence was found supporting that cognitive processing stages can be derived from EEG data by using a hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA) by Anderson, Zhang, Borst, and Whalsh (2016). This claim of using HsMM-MVPA as a method to derive cognitive stages out of an EEG signal was supported with evidence found by Berberyan, van Maanen, van Rijn, and Borst (2021). Our research uses the same method in order to investigate whether there is a difference in the perceptual processing stage when using transparent stimuli, compared to Berberyan and colleagues using non-transparent stimuli for the same task. The method was replicated with the difference that we used transparent stimuli in order to accurately investigate whether the use of transparent stimuli made a difference. There was no significant difference found in our reaction times or cognitive stage durations compared to the results of Berberyan and colleagues. This implies that the transparent stimuli used for this experiment did not result in perception manipulation. However, as we did replicate the results of Berberyan and colleagues, it can be said that the validity of using HsMM-MVPA as a method to derive cognitive stages out of EEG data is supported by our findings.
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Supervisor name: | Borst, J.P. and Berberyan, H. |
| Degree programme: | Artificial Intelligence |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 18 Feb 2021 15:13 |
| Last Modified: | 18 Feb 2021 15:13 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/23983 |
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