In recent years, Contrastive Learning (CL) has become a predominant representation
learning paradigm for time series. Most existing methods manually build specific CL …

With the rise of embodied foundation models (EFMs), most notably small language models
(SLMs), adapting Transformers for the edge applications has become a very active field of …

In recent years, the field of electroencephalography (EEG) analysis has witnessed
remarkable advancements, driven by the integration of machine learning and artificial …

Invasive brain-computer interfaces with Electrocorticography (ECoG) have shown promise
for high-performance speech decoding in medical applications, but less damaging methods …

Rapid eye movement sleep is associated with distinct changes in various biomedical signals
that can be easily captured during sleep, lending themselves to automated sleep staging …

Human's perception of the visual world is shaped by the stereo processing of 3D
information. Understanding how the brain perceives and processes 3D visual stimuli in the …

This paper introduces the” Bio-Mechanical Poet”, an adaptive brain-computer interface that
integrates realtime electroencephalography (EEG) data with advanced generative artificial …

Currently, electroencephalogram (EEG) provides critical data to support the diagnosis of
epilepsy through the identification of seizure events. The review process is undertaken by …

With the development of data mining technology, the analysis of event-related potential
(ERP) data has evolved from statistical analysis of time-domain features to data-driven …

Accurate and efficient electroencephalography (EEG) analysis is essential for detecting
seizures and artifacts in long-term monitoring, with applications spanning hospital …