Selected work

Representative Papers

Consumer-grade EEG-based Eye Tracking

Mar 18, 2025

Progress in consumer-grade electroencephalography–eye-tracking (EEG-ET) research has been hindered by the scarcity of high-quality, time-synchronized multimodal data. Method: This work introduces and publicly releases the first large-scale, multi-paradigm, standardized benchmark dataset for consumer-grade EEG-ET synchronization. It comprises 113 participants, 116 sessions, and 11.75 hours of high-fidelity synchronized recordings—acquired using low-cost commercial EEG systems (e.g., OpenBCI) and webcam-based eye trackers—spanning four oculomotor paradigms: saccades, smooth pursuit, fixation, and free viewing. All data undergo rigorous temporal alignment, bandpass filtering, and missing-value imputation; accompanying open-source preprocessing and analysis code is provided. Contribution/Results: The dataset substantially lowers hardware barriers for EEG-ET research, enhances reproducibility, and provides critical empirical support for gaze decoding under challenging conditions—including low-light environments and camera-free settings.

1 citationsRead paper

Integrating Energy Efficiency into Software Development: Developer Perspectives and Requirements

Jul 24, 2026

This study addresses the insufficient attention to energy consumption in contemporary software development and the lack of energy-efficiency tools aligned with developers’ practical needs. Through semi-structured interviews and Mayring’s qualitative content analysis, integrated with the Technology Acceptance Model (TAM), the research systematically investigates developers’ awareness of software energy efficiency, the barriers they encounter in practice, and their requirements for AI-assisted tools. From the developer perspective, the study identifies core design principles for effective energy-efficiency tooling: delivering actionable energy-saving recommendations, enabling low-intrusion integration into existing development workflows, and ensuring transparent disclosure of data usage and quantified energy savings. These findings offer a user-centered design pathway for green software engineering, substantially enhancing both the perceived usefulness and adoption likelihood of energy-efficiency tools among developers.

0 citationsRead paper

EEG-EyeTrack: A Benchmark for Time Series and Functional Data Analysis with Open Challenges and Baselines

Apr 02, 2025

This work addresses the functional mapping problem from electroencephalography (EEG) signals to eye-movement trajectories. We introduce EEG-EyeTrack, the first publicly available benchmark tailored for functional data analysis (FDA), incorporating data acquired from both consumer-grade and research-grade hardware. We formalize an FDA-specific task definition and evaluation paradigm, and propose a B-spline-based functional representation. Our framework integrates functional principal component analysis (FPCA), time-warping-aware regression, and functional neural networks (FNNs). Compared to conventional time-series models, our FDA-based baselines reduce the mean angular error in gaze trajectory reconstruction to 8.3° on consumer-grade EEG devices—marking a substantial improvement in decoding accuracy. This work fills a critical gap by establishing the first FDA-oriented benchmark for neurobehavioral decoding, and provides a reproducible, scalable methodological framework for EEG-driven functional gaze modeling.

0 citationsRead paper

Recent publications

Latest Papers

Integrating Energy Efficiency into Software Development: Developer Perspectives and Requirements

Jul 24, 2026

This study addresses the insufficient attention to energy consumption in contemporary software development and the lack of energy-efficiency tools aligned with developers’ practical needs. Through semi-structured interviews and Mayring’s qualitative content analysis, integrated with the Technology Acceptance Model (TAM), the research systematically investigates developers’ awareness of software energy efficiency, the barriers they encounter in practice, and their requirements for AI-assisted tools. From the developer perspective, the study identifies core design principles for effective energy-efficiency tooling: delivering actionable energy-saving recommendations, enabling low-intrusion integration into existing development workflows, and ensuring transparent disclosure of data usage and quantified energy savings. These findings offer a user-centered design pathway for green software engineering, substantially enhancing both the perceived usefulness and adoption likelihood of energy-efficiency tools among developers.

0 citationsRead paper

EEG-EyeTrack: A Benchmark for Time Series and Functional Data Analysis with Open Challenges and Baselines

Apr 02, 2025

This work addresses the functional mapping problem from electroencephalography (EEG) signals to eye-movement trajectories. We introduce EEG-EyeTrack, the first publicly available benchmark tailored for functional data analysis (FDA), incorporating data acquired from both consumer-grade and research-grade hardware. We formalize an FDA-specific task definition and evaluation paradigm, and propose a B-spline-based functional representation. Our framework integrates functional principal component analysis (FPCA), time-warping-aware regression, and functional neural networks (FNNs). Compared to conventional time-series models, our FDA-based baselines reduce the mean angular error in gaze trajectory reconstruction to 8.3° on consumer-grade EEG devices—marking a substantial improvement in decoding accuracy. This work fills a critical gap by establishing the first FDA-oriented benchmark for neurobehavioral decoding, and provides a reproducible, scalable methodological framework for EEG-driven functional gaze modeling.

0 citationsRead paper

Consumer-grade EEG-based Eye Tracking

Mar 18, 2025

Progress in consumer-grade electroencephalography–eye-tracking (EEG-ET) research has been hindered by the scarcity of high-quality, time-synchronized multimodal data. Method: This work introduces and publicly releases the first large-scale, multi-paradigm, standardized benchmark dataset for consumer-grade EEG-ET synchronization. It comprises 113 participants, 116 sessions, and 11.75 hours of high-fidelity synchronized recordings—acquired using low-cost commercial EEG systems (e.g., OpenBCI) and webcam-based eye trackers—spanning four oculomotor paradigms: saccades, smooth pursuit, fixation, and free viewing. All data undergo rigorous temporal alignment, bandpass filtering, and missing-value imputation; accompanying open-source preprocessing and analysis code is provided. Contribution/Results: The dataset substantially lowers hardware barriers for EEG-ET research, enhances reproducibility, and provides critical empirical support for gaze decoding under challenging conditions—including low-light environments and camera-free settings.

1 citationsRead paper