This systematic literature review explores the intersection of neuroscience and deep
learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to …

Emotion recognition from EEG signals is a major field of research in cognitive computing.
The major challenges involved in the task are extracting meaningful features from the …

Traditional convolutional neural networks (CNNs) often suffer from high memory
consumption and redundancy in their kernel representations, leading to overfitting problems …

In the field of brain–computer interfaces (BCIs), the potential for leveraging deep learning
techniques for representing electroencephalogram (EEG) signals has gained substantial …

Multi-modal fusion is a hot topic in field of multi-modal learning. Most of the previous multi-
modal fusion tasks are based on the complete modality. Existing researches on missing …

Accurately characterizing brain activity requires detailed feature analysis in the temporal,
spatial, and spectral domains. While previous research has proposed various spatial and …

Objective. Brain–computer interface (BCI) is leveraged by artificial intelligence in EEG signal
decoding, which makes it possible to become a new means of human-machine interaction …

In recent years, human physical activity recognition has increasingly attracted attention from
different research fields such as healthcare, computer-human interaction, lifestyle …

In many modern machine learning models, attention mechanisms play a crucial role in
processing data and identifying significant parts of the inputs, whether these are text or …

Decoding brain activity from non-invasive motor imagery electroencephalograph (MI-EEG)
has garnered significant attentions for brain-computer interface (BCI) and brain disorders …