Background: Brain-Computer Interface (BCI) is becoming more reliable, thanks to the
advantages of Artificial Intelligence (AI). Recently, hybrid Deep Learning (hDL), which …

Accurate binary classification of electroencephalography (EEG) signals is a challenging task
for the development of motor imagery (MI) brain–computer interface (BCI) systems. In this …

Visual perception is an important part of human life. In the context of facial recognition, it
allows us to distinguish between emotions and important facial features that distinguish one …

The classification of motor imagery-electroencephalogram (MI-EEG) based brain-computer
interface (BCI) can be used to decode neurological activities, which has been widely applied …

The main purpose of this paper is to provide information on how to create a convolutional
neural network (CNN) for extracting features from EEG signals. Our task was to understand …

The feature extraction technique plays a vital role in obtaining better classification accuracy.
In this paper, a novel framework is proposed, which develops two‐dimensional (2D) images …

针对脑电信号(electroencephalogram, EEG) 运动想象中单一特征无法多维表征信号中的信息
导致的分类准确率不高的问题, 提出一种基于样本熵和共空间模式特征融合的特征提取算法 …

This paper presents a comprehensive Systematic Literature Review (SLR) on the utilization
of adversarial learning techniques in Motor Imagery (MI) signal classification, a key …

Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze
group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This …

Riemannian Geometry-based features have been among the most promising
electroencephalography (EEG) classification methods in recent years. However, these …