Search Results for author:
Found 17 papers, 3 papers with code
Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension
With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning.
Using EEG Signals to Assess Workload during Memory Retrieval in a Real-world Scenario
Objective: The Electroencephalogram (EEG) is gaining popularity as a physiological measure for neuroergonomics in human factor studies because it is objective, less prone to bias, and capable of assessing the dynamics of cognitive states.
Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning
Significance: This study demonstrated the capability of the LST-based transfer learning to leverage existing data across subjects and/or devices with an in-depth investigation of its rationale and behavior in various circumstances.
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks
Test samples with the backdoor key will then be classified into the target class specified by the attacker.
Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellers
An electroencephalogram (EEG) based brain-computer interface (BCI) speller allows a user to input text to a computer by thought.
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications
Recent technological advances such as wearable sensing devices, real-time data streaming, machine learning, and deep learning approaches have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications.
Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Events
Electroencephalography (EEG) data are difficult to obtain due to complex experimental setups and reduced comfort with prolonged wearing.
Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing
The parallel trend of deep-learning has led to a huge leap in performance towards solving various vision-based research problems such as object detection.
Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown its robustness in facilitating high-efficiency communication.
Multi-modal Approach for Affective Computing
Using multi-modal AMIGOS dataset, this study compares the performance of human emotion classification using multiple computational approaches applied to face videos and various bio-sensing modalities.
Human-Computer Interaction
EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features
Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance.
Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)
Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noises, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression.
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Particularly, the proposed algorithm ensured that the BCI classification and the drowsiness estimation had little degradation even when data were compressed by 80%, making it very suitable for continuous wireless telemonitoring of multichannel signals.
Compressed Sensing for Energy-Efficient Wireless Telemonitoring: Challenges and Opportunities
As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make possible the design of low-power devices.
Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware
Compressed sensing (CS), as an emerging data compression methodology, is promising in catering to these constraints.
Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG via Block Sparse Bayesian Learning
The design of a telemonitoring system via a wireless body-area network with low energy consumption for ambulatory use is highly desirable.
