In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the
accuracy of the classification of motor movements. Machine learning (ML) algorithms such …

The ability to control external devices through thought is increasingly becoming a reality.
Human beings can use the electrical signals of their brain to interact or change the …

Recognition of motor imagery tasks (MI) from electroencephalographic (EEG) signals is
crucial for developing rehabilitation and motor assisted devices based on brain-computer …

In this paper, we address a method for motor imagery feature extraction for brain computer
interface (BCI). The wavelet coefficients were used to extract the features from the motor …

Electroencephalography (EEG) has been a staple method for identifying certain health
conditions in patients since its discovery. Due to the many different types of classifiers …

In this paper, we propose an automated computer platform for the purpose of classifying
Electroencephalography (EEG) signals associated with left and right hand movements using …

Electroencephalogram (EEG) is the most significant signal for brain-computer interfaces
(BCI). Nowadays, motor imagery (MI) movement based BCI is highly accepted method for …

Objective: Electroencephalography (EEG) has an influential role in neuroscience and
commercial applications. Most of the tools available for EEG signal analysis use machine …

This paper focuses on classification of motor imagery in Brain Computer Interface (BCI) by
using classifiers from machine learning technique. The BCI system consists of two main …

Considering the increase in life expectancy, people started to invest in technologies capable
of improving the quality of life. One of these technologies is the Brain-Machine Interface …