Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …

Background Data augmentation (DA) has recently been demonstrated to achieve
considerable performance gains for deep learning (DL)—increased accuracy and stability …

The research progress in multimodal learning has grown rapidly over the last decade in
several areas, especially in computer vision. The growing potential of multimodal data …

The digital world is expanding rapidly and advances in networking technologies such as 4G
long-term evolution (LTE), wireless broadband (WiBro), low-power wide area networks …

Multimodal classification research has been gaining popularity with new datasets in
domains such as satellite imagery, biometrics, and medicine. Prior research has shown the …

The increasing amount of data in electromyographic (EMG) signal research has greatly
increased the importance of develo** advanced data analysis and machine learning …

The immense progress in physiological signal acquisition and processing in health
monitoring allowed a better understanding of patient disease detection and diagnosis. With …

Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic
oscillations of neural activity, which is one of the core technologies of brain-computer …

In recent years, information available from multiple data modalities has become increasingly
common for industrial engineering and operations research applications. There have been a …