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...
Research Background and Objectives In recent years, Brain-Computer Interface (BCI) systems have been widely applied in the fields of neuroengineering and neuroscience. Electroencephalogram (EEG), as a data tool reflecting the activities of different neuronal groups in the central nervous system, has become a core research topic in these fields. How...
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...
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...
Paper Title: heuristics in risky decision-making relate to preferential representation of information Research Background When making choices, individuals not only differ from each other but also deviate from normative theoretical recommendations. One explanation for this difference is that individuals have unique information representation prefere...
Application of Magnetoencephalography (MEG) to Analyze Changes in Whole-Brain Oscillatory Microstate Patterns Across the Lifespan: Cambridge Centre for Aging and Neuroscience Cohort Study Research Background With the increasing seriousness of the aging population problem, understanding the neurophysiological changes during the aging process becomes...
Analysis of Cortical Neurophysiological Characteristics of ALS and Its Potential as a Biomarker Background Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that affects adults, characterized by a gradual loss of the integrity of the brain, spinal cord, and peripheral motor system. Although clinical and genetic studies have reveale...
Background Introduction A notable characteristic of human language processing is our ability to combine stored lexical elements, i.e., words, as needed to flexibly generate or alter current meanings. At the core of this process is how we construct semantic representations in real time. Although research on the generation of syntactic structures has...
Phase-Dependent Language Perception in Neural Oscillations: An Interdisciplinary Study Report Background During speech perception, the phase of neural oscillations plays a crucial role in the separation of neural representations and perceptual decisions. However, the specific phase-encoding mechanisms remain unclear. This study aims to reveal how p...