Modelling sleep-related data using hidden Markov models
Project Description
We will apply methods similar to Eye Movement analysis with Hidden Markov Models (EMHMM, http://visal.cs.cityu.edu.hk/research/emhmm/) to sleep-related data including actigraphy data or EEG data to model and quantify individual differences in circadian rhythms or sleep stages.
Supervisor
HSIAO, Janet Hui-wen
Quota
2
Course type
UROP1000
UROP1100
Applicant's Roles
The applicant will perform relevant data analysis using EMHMM, which may involve programming or developing new functions. The applicant will also assist in report preparation.
Applicant's Learning Objectives
- to learn about the EMHMM approach and apply it to new data type.
- to learn to model and analyse individual difference in circadian rhythms or sleep stages using actigraphy or EEG data.
- to learn to interpret and present the results in a report.
- to learn to model and analyse individual difference in circadian rhythms or sleep stages using actigraphy or EEG data.
- to learn to interpret and present the results in a report.
Complexity of the project
Challenging