Brain-Computer Interfaces for Emotional Regulation in Patients with Various Disorders

TL;DR

This paper explores EEG-based brain-computer interfaces and a novel neural network algorithm to recognize and regulate emotional states, aiming to aid individuals with neurological and physiological disorders.

Contribution

It introduces a new neural network approach for classifying emotional states from EEG data, with promising accuracy for emotional regulation in disorder-affected individuals.

Findings

High accuracy in classifying emotional states from EEG data

Potential of BCIs to assist emotional regulation in neurological disorders

Use of novel data modification techniques for dataset augmentation

Abstract

Neurological and Physiological Disorders that impact emotional regulation each have their own unique characteristics which are important to understand in order to create a generalized solution to all of them. The purpose of this experiment is to explore the potential applications of EEG-based Brain-Computer Interfaces (BCIs) in enhancing emotional regulation for individuals with neurological and physiological disorders. The research focuses on the development of a novel neural network algorithm for understanding EEG data, with a particular emphasis on recognizing and regulating emotional states. The procedure involves the collection of EEG-based emotion data from open-Neuro. Using novel data modification techniques, information from the dataset can be altered to create a dataset that has neural patterns of patients with disorders whilst showing emotional change. The data analysis reveals promising results, as the algorithm is able to successfully classify emotional states with a high degree of accuracy. This suggests that EEG-based BCIs have the potential to be a valuable tool in aiding individuals with a range of neurological and physiological disorders in recognizing and regulating their emotions. To improve upon this work, data collection on patients with neurological disorders should be done to improve overall sample diversity.