Bayesian Estimation of Group Event-Related Potential Components: Testing a Model for Synthetic and Real Datasets

Information Science, Electronic Information, Artificial Intelligence, Medicine, Neurology, Life Sciences, Biophysics,
electroencephalogram   event-related potentials   hidden sources   bayesian estimation  

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...

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...

Topology of Surface Electromyogram Signals: Hand Gesture Decoding on Riemannian Manifolds

Engineering, Electrical Engineering, Information Science, Electronic Information, Computer Science, Medicine, Neurology, Life Sciences, Biophysics,
surface electromyogram   hand gesture decoding   Riemannian manifold   electrode array   multivariate timeseries  

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...

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-...