Exploring how images are represented in human brain activity
Name
Kristiina Pokk
Abstract
This bachelor thesis explores how images are represented in human brain activity. Data used in this thesis were collected in the University Hospital of Lyon with experiments where human neural activity was recorded with intracranial electrodes during a simple visual task. The aim of this thesis is to analyse the data, more specifically the activity in the high-gamma frequency range, with unsupervised machine learning methods to find structure in it. In particular, similarity of neural responses (recorded by electrodes) to images and the differences in activity according to image categories.
Visualization and biclustering were used in this thesis. Out of the two analysis methods used, visualization was more successful. Among image categories used, images of faces stood out the best. The houses and scrambled images clustered to some extent as well. Visualizing electrodes resulted in pronounced clusters emerging, which were heterogeneous in their nature. Biclustering gave no noteworthy results.
Visualization and biclustering were used in this thesis. Out of the two analysis methods used, visualization was more successful. Among image categories used, images of faces stood out the best. The houses and scrambled images clustered to some extent as well. Visualizing electrodes resulted in pronounced clusters emerging, which were heterogeneous in their nature. Biclustering gave no noteworthy results.
Graduation Thesis language
English
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Raul Vicente, Jaan Aru
Defence year
2016