Welcome to the repository for the Neuroscience Lab Course. This repository contains solutions and implementations for four assignments related to various aspects of neuroscience research, including visual search tasks, regression and ANOVA analysis, EEG preprocessing, and fMRI analysis.
- Visual Search Task
- Regression and ANOVA Analysis
- EEG Preprocessing and Analysis
- fMRI Preprocessing and Analysis
Description: This assignment involves simulating a visual search experiment using Psychtoolbox. Participants search for high-value objects among low-value distractors in a series of trials.
Key Components:
- Implementation of the search phase, including fixation, object selection, and reward delivery.
- Random assignment of fractals and trial conditions.
- Recording and saving experimental data.
- Video recording of the experiment showing various trial situations.
Description: This assignment focuses on analyzing search time data using regression and ANOVA. It includes checking model assumptions, performing stepwise regression, and analyzing the impact of different variables.
Key Components:
- Loading and analyzing data with multiple linear regression.
- Checking assumptions with Q-Q plots, variance, and residual independence.
- Performing ANOVA with different models and post-hoc comparisons.
- Adding subject as a factor for additional ANOVA analysis.
Description: This assignment involves preprocessing EEG data and analyzing ICA components and ERP results. The preprocessing steps include removing noise, re-referencing, and assessing the impact on ICA components.
Key Components:
- Preprocessing steps including noise removal, ICA, and re-referencing.
- Plotting and comparing ICA components before and after preprocessing.
- Extracting and analyzing ERP data for different stimulus conditions.
Description: This assignment covers preprocessing of fMRI data, including structural and functional image alignment, regressor construction, and GLM analysis.
Key Components:
- Skull removal and structural image processing.
- Functional image preprocessing and alignment with structural images.
- Construction of regressors for different experimental conditions.
- GLM analysis and correction for false positives.