Scientific Data Report | K-emophone: A Mobile and Wearable Dataset with On-site Emotion, Stress, and Attention Labels Background With the proliferation of low-cost mobile and wearable sensors, numerous studies have leveraged these devices to track and analyze human mental health, productivity, and behavioral patterns. However, despite the developme...

Deep-learning-based Motor Imagery EEG Classification by Exploiting the Functional Connectivity of Cortical Source Imaging Research Background and Motivation A brain-computer interface (BCI) is a system that directly decodes and outputs brain activity information without relying on related neural pathways and muscles, thereby achieving communication...

MI-EEG Decoding Using a Temporal Dependency Learning Convolutional Neural Network (CNN) Based on Attention Mechanism Research Background and Problem Description Brain-Computer Interface (BCI) systems provide a new way of communicating with computers by real-time translation of brain signals. In recent years, BCI technology has played an important r...

Musculoskeletal models have been widely used in biomechanical analysis because they can estimate motion variables that are difficult to measure directly in living organisms, such as muscle forces and joint moments. Traditional physics-driven computational musculoskeletal models can explain the dynamic interactions between neural inputs to muscles, ...

Brain-Computer Interface (BCI) is a communication method that connects the nervous system to the external environment. Motor Imagery (MI) is the cornerstone of BCI research, referring to the internal rehearsal before physical execution. Non-invasive techniques such as Electroencephalography (EEG) can record neural activities with high temporal reso...

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) has emerged as an enhanced communication and control technology in recent years. In BCI based on electrophysiological characteristics (such as Electroencephalogram, EEG), Motor Imagery (MI) is an important branch that decodes users’ motor intentions for use in clinical rehabilitation, intelligent wheelchair control, c...

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...