相关论文: Predicting Workload in Virtual Flight Simulations …
Real-time adjustments to task difficulty during flight training are crucial for optimizing performance and managing pilot workload. This study evaluated the functionality of a pre-trained brain-computer interface (BCI) that adapts training…
The detection of pilots' mental states is important due to the potential for their abnormal mental states to result in catastrophic accidents. This study introduces the feasibility of employing deep learning techniques to classify different…
High-risk operational tasks such as those in aviation require training environments that are realistic and capable of inducing high levels of workload. Virtual Reality (VR) offers a simulated 3D environment for immersive, safe and valid…
Accurately monitoring cognitive load in real time is critical for Brain-Computer Interfaces (BCIs) that adapt to user engagement and support personalized learning. Electroencephalography (EEG) offers a non-invasive, cost-effective modality…
Brain-Computer Interfaces (BCIs) enable converting the brain electrical activity of an interface user to the user commands. BCI research studies demonstrated encouraging results in different areas such as neurorehabilitation, control of…
Understanding the interaction of neural and cardiac systems during cognitive activity is critical to advancing physiological computing. Although EEG has been the gold standard for assessing mental workload, its limited portability restricts…
Brain computer interfaces enable real-time monitoring of cognitive load, but their effectiveness in dynamic navigation contexts is not well established. Using an existing VR navigation dataset, we examined whether EEG signals can classify…
Electroencephalography (EEG) offers non-invasive, real-time mental workload assessment, which is crucial in high-stakes domains like aviation and medicine and for advancing brain-computer interface (BCI) technologies. This study introduces…
New mental tasks were investigated for suitability in Brain-Computer Interface (BCI). Electroencephalography (EEG) signals were collected and analyzed to identify these mental tasks. MS Windows-based software was developed for investigating…
With the growing need to effectively support workforce upskilling in the manufacturing sector, virtual reality is gaining popularity as a scalable training solution. However, most current systems are designed as static, step-by-step…
Brain-Computer Interfaces (BCIs) have traditionally been studied in clinical and laboratory contexts, but the rise of consumer-grade devices now allows exploration of their use in daily activities. Virtual reality (VR) provides a…
Brain-computer interfaces (BCIs) often suffer from limited robustness and poor long-term adaptability. Model performance rapidly degrades when user attention fluctuates, brain states shift over time, or irregular artifacts appear during…
The detection of pilots' mental states is critical, as abnormal mental states have the potential to cause catastrophic accidents. This study demonstrates the feasibility of using deep learning techniques to classify different fatigue…
Brain-Computer Interface (BCI) is a powerful communication tool between users and systems, which enhances the capability of the human brain in communicating and interacting with the environment directly. Advances in neuroscience and…
Brain-computer interfaces (BCIs) allow direct communication between the brain and electronics without the need for speech or physical movement. Such interfaces can be particularly beneficial in applications requiring rapid response times,…
Brain-computer interface (BCI) systems have potential as assistive technologies for individuals with severe motor impairments. Nevertheless, individuals must first participate in many training sessions to obtain adequate data for optimizing…
This article proposes a novel framework that utilizes an over-the-air Brain-Computer Interface (BCI) to learn Metaverse users' expectations. By interpreting users' brain activities, our framework can optimize physical resources and enhance…
The extraction of brain functioning features is a crucial step in the definition of brain-computer interfaces (BCIs). In the last decade, functional connectivity (FC) estimators have been increasingly explored based on their ability to…
This study investigates brain connectivity and information flow during mental workload (MWL) by integrating electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals. Utilizing the N-back task to induce varying…
Deep learning, including convolutional neural networks (CNNs), has started finding applications in brain-computer interfaces (BCIs). However, so far most such approaches focused on BCI classification problems. This paper extends EEGNet, a…