Electroencephalography (EEG) based Brain-Computer Interfaces (BCIs) are vital for various
applications, yet achieving accurate EEG signal classification, particularly for Motor Imagery …

Brain-computer interface (BCI) is an alternative communication pathway between the human
brain and computer system without involving any muscles or actual motor neuron activities …

Deep learning methods, and in particular Convolutional Neural Networks (CNNs), have
shown breakthrough performance in a wide variety of classification applications, including …

In the evolving field of Brain–Computer Interfaces (BCIs), accurately classifying
Electroencephalography (EEG) signals for Motor Imagery (MI) tasks is challenging. We …

The classification accuracy has become a significant challenge and an important task in
sensory motor imagery (SMI) electroencephalogram (EEG) based Brain Computer interface …

Motor-imagery EEG signal classification is an important topic in the Brain-Computer
Interface domain. In this chapter, two distinct variants of filter bank models have been used …

Electroencephalogram (EEG) signal recorded throughout motor imaging (MI) tasks has been
wide applied in brain-computer interface (BCI) applications as a communication approach …

This study proposes a novel approach that com-bines stacking ensemble learning with
Graph Convolutional Neural Networks (GCNNs) to enhance the classification accuracy of …

This thesis delves into Motor Imagery Electroencephalography (MI-EEG) classification,
aiming to refine precision and deepen the understanding of the complex dynamics in motor …

BCI or brain–computer interface is a buzzing technology in the world. It can be understood
as a mechanism of commanding various hardware devices just by the use of signals …