With the joint advancement in areas such as pervasive neural data sensing, neural
computing, neuromodulation and artificial intelligence, neural interface has become a …

To achieve a high information transfer rate (ITR) in steady-state visual evoked potential
(SSVEP)-based brain-computer interfaces (BCIs), current decoding methods require …

Abstract The abstraction of Brain Computer Interface (BCI) is a communication and control
system that translated human mind thoughts into real-world interaction without any use of …

Depression recognition based on physiological signals such as functional near-infrared
spectroscopy (fNIRS) and electroencephalogram (EEG) has made considerable progress …

For patients with severe conditions such as stroke, passive exercise is commonly used in the
early stages of their rehabilitation training. This study aimed to assess the effect of passive …

Neurovascular coupling (NVC) provides important insights into the intricate activity of brain
functioning and may aid in the early diagnosis of brain diseases. Emerging evidences have …

Drug addiction (DA) is a long-term and relapsing brain disorder with limited effective
treatments. Electroencephalography (EEG) is a highly promising tool for investigating DA …

Accurate prediction of human locomotion intent benefits the seamless switching of lower
limb exoskeleton controllers in different terrains to assist humans in walking safely. In this …

Leveraging the multimodal brain signals collected from various electronic devices, such as
electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data, has …

This paper presents a novel Linear Mapping Field (LMF) to map time series into two-
dimensional images. The LMF extracts deeper features of fNIRS signals, which makes fNIRS …