In education, it is critical to monitor students’ attention and measure the extents which students participate differences in their levels abilities. The overall goal of this study was increase quality distance education. particular, order craft an approach that will effectively augment online learning using objective measures brain activity, we propose a brain–computer interface (BCI) system a...

As a novel biometric characteristic, the electroencephalogram (EEG) is used for authentication. To solve challenge of efficiently growing number classifications in traditional classification networks and to increase practicality engineering, this paper proposes an authentication approach EEG data based on attention mechanism triplet loss function. The method begins by feeding signals into deep ...

Electroencephalography (EEG) is a useful way to implicitly monitor the user's perceptual state during multimedia consumption. One of primary challenges for practical use EEG-based monitoring achieve satisfactory level accuracy in EEG classification. Connectivity between different brain regions an important property classification EEG. However, how define connectivity structure given task still ...

This paper presents the proposal of a method to recognize emotional states through EEG analysis. The novelty this work lies in its feature improvement strategy, based on multiclass genetic programming with multidimensional populations (M3GP), which builds features by implementing an evolutionary technique that selects, combines, deletes, and constructs most suitable ease classification process ...

Deep learning with convolutional neural networks (ConvNets) has dramatically improved the capabilities of computer vision applications just through considering raw data without any prior feature extraction. Nowadays, there is a rising curiosity in interpreting and analyzing electroencephalography (EEG) dynamics ConvNets. Our study focused on ConvNets different structures, efficiency multiple ma...