Thesis Project Form
Title (tentative): Brain-Computer Interfaces for the improvement of perceptual learningThesis advisor(s): Chiappalone Michela, Jose del R. Millan (jose.millan@austin.utexas.edu) | E-mail: |
Address: Via Opera Pia 13, 16145 Genova | Phone: |
Description
Motivation and application domain
Perceptual learning is the experience-dependent enhancement of our ability to interpret sensory information. It improves our ability to make decisions when facing ambiguous sensory inputs (stimuli) through intense training. Brain-Computer Interface (BCI) -based approaches can provide non-pharmacological and non-invasive interventions for perceptual impairments in elderly and clinical populations, avoiding adverse effects and accelerating the process.
General objectives and main activities
The primary objective of this thesis is to design and refine an experimental pipeline for exploring the potential of BCIs in enhancing cognitive and learning processes, with a particular focus on perceptual learning. This will be achieved through the analysis of neurophysiological signals. The thesis will involve several key activities: (1) defining and designing a robust experimental pipeline aimed at assessing the effects of BCI training on perceptual learning, (2) conducting experiments utilizing the pipeline, (3) collecting relevant neurophysiological data, and (4) performing data analysis to interpret the findings and draw conclusions.
Training Objectives (technical/analytical tools, experimental methodologies)
The student will gain hands-on experience in conducting experiments, actively participating in every stage of the research process while working in a professional environment. The student will become proficient in utilizing BCI technologies, from data collection to analysis. Additionally, they will learn how to approach a research question and use advanced technological tools to address and explore that question, developing both technical and analytical skills in the process.
Place(s) where the thesis work will be carried out: The University of Texas at Austin, TX, USA
Additional information
Pre-requisite abilities/skills: Linear algebra, Signals and Systems, Programming in MATLAB or Python, Statistical methods, Biological systems
Maximum number of students: 1
Financial support/scholarship: Extra-EU scholarship