Unraveling EEG correlates of unimanual finger movements: insights from non-repetitive flexion and extension tasks.

Background: The loss of finger control in individuals with neuromuscular disorders significantly impacts their quality of life. Electroencephalography (EEG)-based brain-computer interfaces that actuate neuroprostheses directly via decoded motor intentions can help restore lost finger mobility. However, the extent to which finger movements exhibit distinct and decodable EEG correlates remains unresolved.

Binocularly incongruent, multifrequency-coded SSVEP in VR: feasibility and characteristics.

Steady-state visual evoked potentials (SSVEPs) in response to flickering stimuli are popular in brain-computer interfacing but their implementation in virtual reality (VR) offers new opportunities also for clinical applications. While traditional SSVEP target selection relies on single-frequency stimulation of both eyes simultaneously, further called congruent stimulation, recent studies attempted to improve the information transfer rate by using dual-frequency-coded SSVEP where each eye is presented with a stimulus flickering at a different frequency, further called incongruent stimulation. However, few studies have investigated incongruent multifrequency-coded SSVEP (MultiIncong-SSVEP).

Unpacking the hazards: An analytic study of injury patterns and risk factors in urban instant delivery.

The rapid growth of urban instant delivery, facilitated by digital platforms and characterized by on-demand, short-term, task-based labor, has raised concerns about safety, particularly with the increasing frequency of instant delivery crashes (IDCs). This study addresses knowledge gaps in understanding injury patterns and risk factors associated with IDCs. Utilizing data extracted from judicial verdicts on IDC disputes in China, encompassing demographic, contextual, crash, and injury information, the research employs ordered logit regression to identify significant factors affecting injury patterns, the number of injuries per person (IPP), and injury severity.

Real-Time Navigation in Google Street View Using a Motor Imagery-Based BCI.

Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain-computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, the majority of BCI-based navigation studies adopt cue-based visual paradigms, and the evoked brain responses are encoded into navigation commands.

Chronic Study on Brainwave Authentication in a Real-Life Setting: An LSTM-Based Bagging Approach.

With the advent of the digital age, concern about how to secure authorized access to sensitive data is increasing. Besides traditional authentication methods, there is an interest in biometric traits such as fingerprints, the iris, facial characteristics, and, recently, brainwaves, primarily based on electroencephalography (EEG). Current work on EEG-based authentication focuses on acute recordings in laboratory settings using high-end equipment, typically equipped with 64 channels and operating at a high sampling rate.