Graph neural networks (GNNs) have attracted extensive research attention in recent years
due to their capability to progress with graph data and have been widely used in practical …

Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to
widely influence research, clinical and recreational use. Non-invasive BCI approaches are …

Recovering missed modality is popular in incomplete multimodal learning because it usually
benefits downstream tasks. However, the existing methods often directly estimate missed …

Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as
emotion recognition, motor imagery and neurological diseases and disorders. A wide range …

We propose a foundation model named Brant for modeling intracranial recordings, which
learns powerful representations of intracranial neural signals by pre-training, providing a …

With deep learning (DL) development, EEG-based emotion recognition has attracted
increasing attention. Diverse DL algorithms emerge and intelligently decode human emotion …

Electroencephalography (EEG) signals, known for convenient non-invasive acquisition but
low signal-to-noise ratio, have recently gained substantial attention due to the potential to …

Emotion recognition is essential in the diagnosis and rehabilitation of various mental
diseases. In the last decade, electroencephalogram (EEG)-based emotion recognition has …

The electroencephalogram (EEG) signal has become a highly effective decoding target for
emotion recognition and has garnered significant attention from researchers. Its spatial …

Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is
challenging yet essential for decoding brain activities using brain-computer interfaces …