Research Background Brain-Computer Interface (BCI) technology enables direct communication between the brain and external devices. It is widely used in fields such as human-computer interaction, motor rehabilitation, and healthcare. Common BCI paradigms include steady-state visual evoked potentials (SSVEP), P300, and motor imagery (MI). Among these...
Revealing the Neuromechanism of Interhemispheric Balance Restoration in Chronic Stroke Patients through EMG-driven Robot Hand Training: Insights from Dynamic Causal Modeling Stroke is a common cause of disability, with most stroke survivors suffering from upper limb paralysis. The consequences of upper limb functional impairment can persist for ove...
Brain-Computer Interface (BCI) technology has rapidly developed in recent years and is considered a cutting-edge technology that allows external devices to be controlled directly by the brain without the need for peripheral nerves and muscles. Particularly in the application of Motor Imagery Electroencephalography (MI-EEG), BCI technology has shown...
The IEEE “Transactions on Neural Systems and Rehabilitation Engineering” published a paper titled “Sleep Stage Classification Using Attention-Based Deep Learning for Single-Channel EEG” in Volume 29, 2021. The author of the article include Emadeldeen Edele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan. The main go...
Application of Attention-guided Graph Structure Learning Network for EEG-enabled Auditory Attention Detection Academic Background The “cocktail party effect” describes the human brain’s ability to selectively concentrate attention on one speaker while ignoring others in a multi-talker environment. However, for individuals with hearing impairments, ...
Feasibility of Intravascular Femoral Nerve Stimulation using a Stent Electrode Array In recent years, electrical stimulation of peripheral nerves has gained attention as a potential therapeutic approach for restoring impaired nerve function. Traditional electrode arrays typically require invasive surgical implantation, which imposes a significant b...
Frequency-Specific Reorganization of Brain Networks during Visuomotor Tasks Research Background Executing movements is a complex cognitive function that relies on the coordinated activation of spatially proximal and distal brain regions. Visuomotor integration tasks require processing and interpreting visual inputs to plan motor execution and adjus...
GCTNet: Graph Convolution Transformer Network for Detecting Major Depressive Disorder Based on EEG Signals Research Background Major Depressive Disorder (MDD) is a prevalent mental illness characterized by significant and persistent low mood, affecting over 350 million people worldwide. MDD is one of the leading causes of suicide, resulting in appr...
Topology of Surface Electromyography Signals: Decoding Hand Gestures Using Riemannian Manifolds This paper is authored by Harshavardhana T. Gowda (Department of Electrical and Computer Engineering, University of California, Davis) and Lee M. Miller (Center for Mind and Brain Sciences, Department of Neurophysiology and Behavior, Department of Otolar...
Visual Brain-Computer Interface Based on High-Frequency Steady-State Visual Evoked Fields Background Brain-Computer Interface (BCI) technology allows users to control machines by decoding specific brain activity signals. While invasive BCIs excel in capturing high-quality brain signals, their application is mainly limited to clinical settings. Non-...