Network-based alterations in task-induced functional connectivity in bipolar disorder: An functional near-infrared spectroscopy study.

Background: Functional near-infrared spectroscopy (fNIRS) is increasingly used to examine cognitive abilities and support the diagnosis of bipolar disorder (BD). Despite its growing application, research on functional connectivity in BD remains limited, and task-induced functional connectivity remains under-investigated. This study aims to differentiate patients with BD from healthy controls (HCs) based on fNIRS-derived functional connectivity.

Toward the TCN-based Real-Time BCI System for Target Detection.

This study focuses on developing a real-time Brain-Computer Interface (BCI) system, specifically designed for military applications, to enhance target detection in rapid serial visual presentation (RSVP) tasks. The proposed BCI system utilizes electroencephalogram (EEG) signals based on dry electrodes, known for their exceptional temporal resolution, to identify swiftly specific target symbols within sequences of visual stimuli. Leveraging deep learning techniques, particularly Temporal Convolutional Networks (TCN), this study demonstrates the accuracy and efficiency improvement in target detection for RSVP tasks.

Electromyographic Activities of the Rotator Cuff Muscles During Walking, Eating, and Washing.

Objective: The aim of this study was to evaluate the activity of rotator cuff (RC) muscles during activities of daily living.

Design: Motion analysis was conducted with 14 volunteers. Activation of RC (subscapularis, supraspinatus, and infraspinatus) was assessed using electromyography (EMG).