Exploring Embodied Cognition and Brain Dynamics Under Multi-Tasks Target Detection in Immerse Projector-Based Augmented Reality (IPAR) Scenarios.

Embodied cognition explores the intricate interaction between the brain, body, and the surrounding environment. The advancement of mobile devices, such as immersive interactive computing and wireless electroencephalogram (EEG) devices, has presented new challenges and opportunities for studying embodied cognition. To address how mobile technology within immersive hybrid settings affects embodied cognition, we propose a target detection multitask incorporating mixed body movement interference and an environmental distraction light signal.

Time synchronization between parietal-frontocentral connectivity with MRCP and gait in post-stroke bipedal tasks.

Background: In post-stroke rehabilitation, functional connectivity (FC), motor-related cortical potential (MRCP), and gait activities are common measures related to recovery outcomes. However, the interrelationship between FC, MRCP, gait activities, and bipedal distinguishability have yet to be investigated.

Methods: Ten participants were equipped with EEG devices and inertial measurement units (IMUs) while performing lower limb motor preparation (MP) and motor execution (ME) tasks.

High-resolution EEG source localization in personalized segmentation-free head model with multi-dipole fitting.

. Electroencephalograms (EEGs) are often used to monitor brain activity. Several source localization methods have been proposed to estimate the location of brain activity corresponding to EEG readings.

Integration of MRI and somatosensory evoked potentials facilitate diagnosis of spinal cord compression.

This study aimed to integrate magnetic resonance imaging (MRI) and related somatosensory evoked potential (SSEP) features to assist in the diagnosis of spinal cord compression (SCC). MRI scans were graded from 0 to 3 according to the changes in the subarachnoid space and scan signals to confirm differences in SCC levels. The amplitude, latency, and time-frequency analysis (TFA) power of preoperative SSEP features were extracted and the changes were used as standard judgments to detect neurological function changes.

Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG Systems Review.

Wireless electroencephalography (EEG) systems have been attracting increasing attention in recent times. Both the number of articles discussing wireless EEG and their proportion relative to general EEG publications have increased over years. These trends indicate that wireless EEG systems could be more accessible to researchers and the research community has recognized the potential of wireless EEG systems.

Frontoparietal Dysconnection in Covert Bipedal Activity for Enhancing the Performance of the Motor Preparation-Based Brain-Computer Interface.

Motor-based brain-computer interfaces (BCIs) were developed from the brain signals during motor imagery (MI), motor preparation (MP), and motor execution (ME). Motor-based BCIs provide an active rehabilitation scheme for post-stroke patients. However, BCI based solely on MP was rarely investigated.

Immediate Plasticity of Parietal-Frontocentral Functional Connections in Music-Reality based Post-Stroke Rehabilitation.

Post-stroke neuronal plasticity was always viewed as a localized gain-of-functionality. The reorganization of neurons neighboring the lesioned brain tissues is able to compensate for the function of damaged neurons. However, it was also proposed that distant interconnected brain regions could be affected by stroke.

Intralobular and Interlobular Parietal Functional Network Correlated to MI-BCI Performance.

Brain-computer interface (BCI) brings hope to patients suffering from neuromuscular diseases, by allowing the control of external devices using neural signals from the central nervous system. However, a portion of individuals was unable to operate BCI with high efficacy. This research aimed to study the brain-wide functional connectivity differences that contributed to BCI performance, and investigate the relationship between task-related connectivity strength and BCI performance.

A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia From EEG Connectivity Patterns.

Objective: We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have been introduced to functional network classification only very recently for fMRI, and the proposed architectures essentially focused on a single type of connectivity measure.

Methods: We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ).