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, ...

Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding Background Introduction Electroencephalogram (EEG) signals are widely used in brain-computer interface (BCI) tasks due to their non-invasive nature, portability, and low acquisition cost. However, EEG signals suffer from low signal-to-noise ratio, sensitivity to electr...

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...

Background Introduction The study of Event-Related Potentials (ERPs) provides important information about brain mechanisms, particularly in elucidating various psychological processes. In these studies, multi-channel electroencephalograms (EEGs) are typically recorded while subjects perform specific tasks, and the trials are categorized based on st...

Layer-Dependent Effects of Chronic Neural Electrode Implants on Neural Functional Connectivity in Mice Introduction This study explores the long-term effects of chronically implanted microelectrodes on neural functional connectivity within the brains of C57BL6 wild-type mice. Implanted intracerebral electrodes enable the recording and electrical st...

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...

EEG of Pre-movement Phase Aids Brain-Computer Interface (BCI) in Recognizing Movement Intentions Background and Research Objectives Brain-Computer Interface (BCI) is a technology that translates human intentions directly through neural signals to control devices, holding extensive application prospects [1]. BCI has the potential to revolutionize va...

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-...

Patients with Parkinson’s Disease (PD) often experience gait impairments, which severely affect their quality of life. Previous studies have suggested that β-frequency (15-30 Hz) oscillatory activity in the basal ganglia may be associated with gait impairments, but the exact dynamics of these oscillations during the gait process remain unclear. Add...