A5081643442- EEG and Brain-Computer Interfaces
- Neural dynamics and brain function
- Neuroscience and Neural Engineering
- Functional Brain Connectivity Studies
- Memory and Neural Mechanisms
- Neural Networks and Applications
- Sleep and Work-Related Fatigue
- Heart Rate Variability and Autonomic Control
- Neural and Behavioral Psychology Studies
- Neurobiology of Language and Bilingualism
- Advanced Optical Imaging Technologies
- Visual perception and processing mechanisms
- Action Observation and Synchronization
- Infrared Thermography in Medicine
- Optical Imaging and Spectroscopy Techniques
- Sleep and Wakefulness Research
- Non-Invasive Vital Sign Monitoring
- Gaze Tracking and Assistive Technology
- Fuzzy Logic and Control Systems
- Machine Learning in Healthcare
- Face Recognition and Perception
- Artificial Intelligence in Healthcare and Education
- Human-Automation Interaction and Safety
- Deception detection and forensic psychology
- Retinal Imaging and Analysis
University of Technology Sydney
2019-2025
Information Technology University
2023
Korea Institute of Science and Technology
2017
Abstract Spatial navigation is a complex cognitive process based on multiple senses that are integrated and processed by wide network of brain areas. Previous studies have revealed the retrosplenial (RSC) to be modulated in task-related manner during navigation. However, these restricted participants’ movement stationary setups, which might impacted heading computations due absence vestibular proprioceptive inputs. Here, we present evidence human RSC theta oscillation (4–8 Hz) an active...
Brain-computer interfaces (BCIs) allow users to communicate directly with external devices via their brain signals. Recently, BCIs, and wearable computers in particular, have been receiving more attention by government industry as an alternative means of interacting technology. Wearable can combine highly immersive virtual/augmented/mixed reality experiences for entertainment, health monitoring, utilitarian purposes, and, most importantly at present, research. With computers, researchers...
The Brain-Computer Interface (BCI) enables direct brain-to-device communication, with the Steady-State Visual Evoked Potential (SSVEP) paradigm favored for its stability and high accuracy across various fields. In SSVEP BCI systems, supervised learning models significantly enhance performance over unsupervised models, achieving higher in less time. However, prolonged data collection can cause user fatigue even trigger photosensitive epilepsy, creating a negative experience. Thus, reducing...
Brain-computer interfaces (BCIs) present a promising avenue by translating neural activity directly into text, eliminating the need for physical actions. However, existing non-invasive BCI systems have not successfully covered entire alphabet, limiting their practicality. In this paper, we propose novel EEG-based system with Curriculum-based Neural Spelling Framework, which recognizes all 26 alphabet letters decoding signals associated handwriting first, and then apply Generative AI (GenAI)...
Can ChatGPT diagnose Alzheimer's Disease (AD)? AD is a devastating neurodegenerative condition that affects approximately 1 in 9 individuals aged 65 and older, profoundly impairing memory cognitive function. This paper utilises 9300 electronic health records (EHRs) with data from Magnetic Resonance Imaging (MRI) tests to address an intriguing question: As general-purpose task solver, can accurately detect using EHRs? We present in-depth evaluation of black-box approach zero-shot multi-shot...
The availability of accurate and reliable dry sensors for electroencephalography (EEG) is vital to enable large-scale deployment brain–machine interfaces (BMIs). However, invariably show poorer performance compared the gold standard Ag/AgCl wet sensors. loss with even more evident when monitoring signal from hairy curved areas scalp, requiring use bulky uncomfortable acicular This work demonstrates three-dimensional micropatterned based on a subnanometer-thick epitaxial graphene detecting...
Previous studies showed that natural walking reduces the susceptibility to VR sickness. However, many users still experience sickness when wearing headsets allow free in room-scale spaces. This paper and postural instability while user walks an immersive virtual environment using electroencephalogram (EEG) headset a full-body motion capture system. The experiment induced by gradually increasing translation gain beyond user's detection threshold. A between-group comparison between...
Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control routinely such complex environments, we designed tracking collision prediction tasks to emulate their elementary tasks. The physiological response workload variations these was elucidated untangle impact experienced by operators. Electroencephalogram (EEG), eye activity, heart rate...
Anticipating human decisions while performing complex tasks remains a formidable challenge. This study proposes multimodal machine-learning approach that leverages image features and electroencephalography (EEG) data to predict response correctness in demanding visual searching task. Notably, we extract novel set of pertaining object relationships using the Segment Anything Model (SAM), which enhances prediction accuracy compared traditional features. Additionally, our effectively utilizes...
Distracted driving is regarded as an integrated task requiring different regions of the brain to receive sensory data, coordinate information, make decisions, and synchronize movements. In this paper, we applied independent modulator analysis (IMA) method temporally electroencephalography (EEG) components understand how human executive control system coordinates simultaneously perform multiple tasks with distractions presented in modalities. The behavioral results showed that reaction time...
Distracted driving refers to multisensory integration and attention shifts between attentional different interferences from modalities, including visual auditory stimuli. Here, we compared the behavioral performance with interacting distractors during driving. Then, independent component analysis (ICA) event-related spectral perturbation (ERSP) were applied investigate neural oscillation changes. The results showed that response times (RTs) increased when appeared in Moreover, RTs longer...
Object recognition and object identification are multifaceted cognitive operations that require various brain regions to synthesize process information. Prior research has evidenced the activity of both visual temporal cortices during these tasks. Notwithstanding their similarities, recognized as separate functions. Drawing from two-stream hypothesis, our investigation aims understand whether channels within ventral dorsal streams contain pertinent information for effective model learning...
Multitasking has become omnipresent in daily activities and increased brain connectivity under high workload conditions been reported. Moreover, the effect of fatigue on neural activity shown participants performing cognitive tasks, but different is unclear. In this article, we investigated changes effective (EC) across network distinctive conditions. There were 133 electroencephalography (EEG) data sets collected from 16 over a five-month study, which high-risk, reduced, normal states...
Navigation is an essential skill that helps one to be aware of where they are in space and ambulate from a location others. Many cognitive processes involved navigation tasks, even the simplest scenario, such as landmarks encoding, map anchoring, goal-oriented planning, motor executing. Engaging multiple tasks simultaneously could lead higher load attenuated performance. In this study, we investigate participants while perform task. We demonstrated ability extract neural features complex...
Neuropathic pain is a debilitating secondary condition for many individuals with spinal cord injury. Spinal injury neuropathic often poorly responsive to existing pharmacological and nonpharmacological treatments. A growing body of evidence supports the potential brain-computer interface systems reduce via electroencephalographic neurofeedback. However, further studies are needed provide more definitive regarding effectiveness this intervention.The primary objective study evaluate multiday...
Abstract Spatial navigation is a complex cognitive process based on multiple senses that are integrated and processed by wide network of brain areas. Previous studies have revealed the retrosplenial (RSC) to be modulated in task-related manner during navigation. However, these restricted participants’ movement stationary setups, which might impacted heading computations due absence vestibular proprioceptive inputs. Here, we investigated neural dynamics RSC an active spatial task where...
Spatial navigation is a complex cognitive process based on vestibular, proprioceptive, and visualcues that are integrated processed by an extensive network of brain areas. The retrosplenial (RSC) integral part coordination translation between spatial reference frames. Previous studies have demonstrated the RSC active during tasks. specifics activity under various loads, however, still not characterized. This study investigated local information load conditions manipulated number turns in...
Decoding linguistic information from non-invasive brain signals using EEG has gained increasing research attention due to its vast applicational potential. Recently, a number of works have adopted generative-based framework decode electroencephalogram (EEG) into sentences by utilizing the power generative capacity pretrained large language models (LLMs). However, this approach several drawbacks that hinder further development applications for brain-computer interfaces (BCIs). Specifically,...
State-space models (SSMs) have garnered attention for effectively processing long data sequences, reducing the need to segment time series into shorter intervals model training and inference. Traditionally, SSMs capture only temporal dynamics of data, omitting equally critical spectral features. This study introduces EEG-SSM, a novel state-space model-based approach dementia classification using EEG data. Our features two primary innovations: EEG-SSM components. The component is designed...
The remarkable success of large language models (LLMs) across various multi-modality applications is well established. However, integrating with humans, or brain dynamics, remains relatively unexplored. In this paper, we introduce BELT-2, a pioneering multi-task model designed to enhance both encoding and decoding performance from EEG signals. To bolster the quality encoder, BELT-2 first work innovatively 1) adopt byte-pair (BPE)-level EEG-language alignment 2) integrate training in domain....
The rapid evolution of Brain-Computer Interfaces (BCIs) has significantly influenced the domain human-computer interaction, with Steady-State Visual Evoked Potentials (SSVEP) emerging as a notably robust paradigm. This study explores advanced classification techniques leveraging interpretable fuzzy transfer learning (iFuzzyTL) to enhance adaptability and performance SSVEP-based systems. Recent efforts have strengthened reduce calibration requirements through innovative approaches, which...