163 citations · 281 across the 7 of their papers we have counts for
7 papers
Privacy-Preserving Brain-Computer Interfaces: A Systematic Review
K. Xia, W. Duch, Y. Sun +8
A brain-computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, ed…
Integrating Evidence into the Design of XAI and AI-based Decision Support Systems: A Means-End Framework for End-users in Construction
Peter E. D. Love, Jane Matthews, Weili Fang +1
Explainable Artificial Intelligence seeks to make the reasoning processes of AI models transparent and interpretable, particularly in complex decision making environments. In the c…
Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework
Peter ED Love, Jane Matthews, Weili Fang +3
Explainable artificial intelligence is an emerging and evolving concept. Its impact on construction, though yet to be realised, will be profound in the foreseeable future. Still, X…
Explainable Artificial Intelligence: Precepts, Methods, and Opportunities for Research in Construction
Peter ED Love, Weili Fang, Jane Matthews +3
Explainable artificial intelligence has received limited attention in construction despite its growing importance in various other industrial sectors. In this paper, we provide a n…
Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review
Dongrui Wu, Jiaxin Xu, Weili Fang +5
Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, ad…
Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)
Ziang Liu, Xue Jiang, Hanbin Luo +3
Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine learning model can be trained from the same number of labeled samples. Most…
Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs
Zihan Liu, Lubin Meng, Xiao Zhang +2
Multiple convolutional neural network (CNN) classifiers have been proposed for electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, CNN models have been foun…