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 …

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

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

The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has
substantially advanced human-computer interaction (HCI) technologies in the AI era …

Compared to other modalities, EEG-based emotion recognition can intuitively respond to the
emotional patterns in the human brain and, therefore, has become one of the most …

We introduce a wearable single-eye emotion recognition device and a real-time approach to
recognizing emotions from partial observations of an emotion that is robust to changes in …

Electroencephalogram (EEG) is a brain signal known for its high time resolution and
moderate signal-to-noise ratio. Whether natural images can be decoded from EEG has been …

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