Comparative Analysis of VGG19, ResNet50, and GoogLeNet Inception Models for BCI

Comparative Analysis of VGG19, ResNet50, and GoogLeNet Inception Models for BCI

Kolla Bala Seswitha, Bonam Rekha Reddy, Sanumuri Venkata Sahithi, Madala Lakshmi Priya

1,2,3,4Department of Computer Science and Engineering,

Koneru Lakshmaiah Education Foundation,

Vaddeswaram, Guntur, AP, India

ABSTRACT:

A brain-computer interface (BCI) is a system that allows an individual to communicate with a computer or other external device using brain activity alone. Electroencephalography (EEG) is a common approach for detecting brain activity in BCI systems, as it provides a non-invasive measure of brain activity with high temporal resolution. Deep learning methods, such as convolutional neural networks (CNNs), have been applied to the analysis of EEG data for BCI applications, and have shown promising results in terms of accuracy and reliability. In this literature survey, we reviewed the use of CNNs for the classification of EEG data in real time to identify specific brain commands. Challenges to the development of an effective BCI using EEG and deep learning methods include the variability of EEG signals across individuals and the high dimensionality of the EEG data. Further research is needed to address these challenges and improve the accuracy and reliability of BCI systems using deep learning methods.