This is project for fulfilment of the course "Brain Computer Interfaces: Fundamentals and Application".

Project Aims & Objectives:

The goal of this project is to provide electroencephalography (EEG) approaches for emotion recognition. EEG signals are collected from the brain’s scalp and analyzed in response to a variety of stimuli representing the three main emotions. Human emotions are varied and complex but can be generalized into positive, neutral, and negative categories.

Project Aims & Objectives:

For gradient-based optimization techniques, the Rosenbrock function is a common test problem. The function is unimodal, with a narrow, parabolic valley containing the global minimum. Convergence to the minimum is challenging, despite the fact that this valley is easy to locate. In this project different numerical optimization algorithms are implemented to locate the global minimum. Besides, other optimization algorithms such as "trust-constr" and "COBYLA" are employed to solve some constrained-optimization problems in the repository.