Background The processing of brain signals for Motor imagery (MI) classification to have
better accuracy is a key issue in the Brain-Computer Interface (BCI). While conventional …

Deep transfer learning (DTL) has developed rapidly in the field of motor imagery (MI) on
brain-computer interface (BCI) in recent years. DTL utilizes deep neural networks with strong …

Brain-computer Interface (BCI) system based on motor imagery (MI) heavily relies on
electroencephalography (EEG) recognition with high accuracy. However, modeling and …

Brain-computer interfaces (BCIs) based on motor imagery (MI) face challenges due to the
complex nature of brain activity, nonstationary and high-dimensional properties, and …

Biometric systems are a continuously evolving and promising technological domain that can
be used in automatic systems for the unique and efficient identification and authentication of …

Brain-computer interface systems with Electroencephalogram (EEG), especially those use
motor-imagery (MI) signals, have demonstrated the ability to control electromechanical …

The concept of brain-controlled UAVs, pioneered by researchers at the University of
Minnesota, initiated a series of investigations. These early efforts laid the foundation for …

Due to their vital applications in many real-world situations, researchers are still presenting
bunches of methods for better analysis of motor imagery (MI) electroencephalograph (EEG) …

In automated systems, biometric systems can be used for efficient and unique identification
and authentication of individuals without requiring users to carry or remember any physical …

Brain-computer interface (BCI) is a direct communication between the brain and the
computer. The BCI system using an electroencephalogram (EEG), a non-invasive system, is …