The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
The Time Course of Visual Processing: From Early Perception to Decision-Making
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
Characterizing the performance limits of high speed image triage using Bayesian search theory
FAC'11 Proceedings of the 6th international conference on Foundations of augmented cognition: directing the future of adaptive systems
A subject transfer framework for EEG classification
Neurocomputing
Computer Methods and Programs in Biomedicine
Quantifying target spotting performances with complex geoscientific imagery using ERP P300 responses
International Journal of Human-Computer Studies
Hi-index | 0.01 |
We report the design and performance of a brain computer interface for single-trial detection of viewed images based on human dynamic brain response signatures in 32-channel electroencephalography (EEG) acquired during a rapid serial visual presentation. The system explores the feasibility of speeding up image analysis by tapping into split-second perceptual judgments of humans. We present an incremental learning system with less memory storage and computational cost for single-trial event-related potential (ERP) detection, which is trained using cross-session data. We demonstrate the efficacy of the method on the task of target image detection. We apply linear and nonlinear support vector machines (SVMs) and a linear logistic classifier (LLC) for single-trial ERP detection using data collected from image analysts and naive subjects. For our data the detection performance of the nonlinear SVM is better than the linear SVM and the LLC. We also show that our ERP-based target detection system is five-fold faster than the traditional image viewing paradigm.