Developing efficient algorithms harnessing the power of current quantum processors has
sparked the emergence of techniques that combine soft computing with quantum computing …

Driver fatigue is a major cause of traffic accidents, and electroencephalography (EEG)
based driver fatigue classification is widely regarded as a future direction. In practical …

Brain-Computer Interfaces connect the brain to external control devices, necessitating the
accurate translation of brain signals such as from electroencephalography (EEG) into …

Motor imagery electroencephalogram signals play a crucial role in Brain-Computer Interface
design that offers a means of communication for individuals with motor disabilities. The non …

The proliferation of portable electroencephalogram (EEG) recording devices has made it
practically feasible to develop the motor imagery (MI) based brain–computer interfaces …

Brain-computer interfaces (BCIs) are used to understand brain functioning and develop
therapies for neurological and neurodegenerative disorders. Therefore, BCIs are crucial in …

This letter proposes a simple and effective EEG classification method using convolutional
neural networks (CNNs). The EEG signals are first converted to character sequences in …

Brain-Computer Interfaces (BCIs) rely on accurately decoding electroencephalography
(EEG) motor imagery (MI) signals for effective device control. Graph Neural Networks …

Brain-Computer Interface (BCI) is a technology that enables direct communication between
the brain and external devices. BCI systems often use Electroencephalography (EEG) to …

Decoding motor imagery electroencephalography (MI-EEG) signals presents significant
challenges due to the difficulty in capturing the complex functional connectivity between …