Time Synchronization Between Parietal–Frontocentral Connectivity with MRCP and Gait in Post-Stroke Bipedal Tasks

Engineering, Electrical Engineering, Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging,
stroke   brain-computer interface   functional connectivity   motor execution   motor preparation   bipedal classification   rehabilitation  

Time Synchronization of Motor-Related Cortical Potentials and Parieto-Frontocentral Connectivity in Bilateral Tasks of Stroke Patients Background In stroke rehabilitation research, functional connectivity (FC), motor-related cortical potentials (MRCP), and gait activities are common metrics related to rehabilitation outcomes. Although these have be...

Multi-Feature Attention Convolutional Neural Network for Motor Imagery Decoding

Engineering, Electrical Engineering, Information Science, Automation, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurology, Life Sciences, Bioengineering,
multi-feature attention   convolutional neural networks   motor imagery   brain-computer interface   lightweight model  

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...

EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding

EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding

Engineering, Electrical Engineering, Information Science, Artificial Intelligence, Medicine, Neurology,
brain-computer interface   motor imagery   attention collapse   temporal convolution network   transfer learning  

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...

Wavelet-Based Temporal-Spectral-Attention Correlation Coefficient for Motor Imagery EEG Classification

Engineering, Electrical Engineering, Information Science, Computer Science, Artificial Intelligence, Medicine, Basic Medical Sciences, Neurology, Medical Imaging, Life Sciences, Bioengineering,
brain-computer interface   motor imagery   EEG channel attention   temporal-spectral attention   wavelet transform   correlation coefficient  

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...

Neuronal Functional Connectivity is Impaired in a Layer-dependent Manner Near Chronically Implanted Intracortical Microelectrodes in C57BL/6 Wildtype Mice

Information Science, Electronic Information, Artificial Intelligence, Medicine, Basic Medical Sciences, Neurology, Life Sciences, Biophysics,
brain-computer interface   brain circuit   LFP synchronization   visual cortex   neuroinflammation  

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...

Preparatory Movement State Enhances Premovement EEG Representations for Brain-Computer Interfaces

Information Science, Electronic Information, Computer Science, Automation, Medicine, Neurology, Life Sciences, Bioengineering, Biophysics,
brain-computer interface   preparatory movement state   premovement intention   encoding paradigm   neural representation  

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...

A User-Friendly Visual Brain-Computer Interface Based on High-Frequency Steady-State Visual Evoked Fields Recorded by OPM-MEG

A User-Friendly Visual Brain-Computer Interface Based on High-Frequency Steady-State Visual Evoked Fields Recorded by OPM-MEG

Engineering, Electrical Engineering, Information Science, Electronic Information, Computer Science, Automation, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Cell Biology, Bioengineering, Biophysics,
brain-computer interface   high frequency   steady-state visual evoked field   magnetoencephalography   optically pumped magnetometer  

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-...

A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis

A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology,
deep learning   speech synthesis   neural decoding   brain-computer interface   speech parameters  

Major Breakthrough in Neuroscience Research: Deep Learning Technique Achieves Decoding of Natural Speech from Brain Signals A cross-disciplinary research team at New York University recently achieved a major breakthrough in the fields of neuroscience and artificial intelligence. They developed a novel deep learning-based framework that can directly...