A framework for rapid visual image search using single-trial brain evoked responses
A framework for rapid visual image search using single-trial brain evoked responses
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DOI：
10.1016/j.neucom.2010.12.025
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发表时间：
2011-06-01
期刊：
影响因子：
6
通讯作者：
Hild, Kenneth E., II
中科院分区：
文献类型：
--
作者：
Huang, Yonghong;Erdogmus, Deniz;Hild, Kenneth E., II
我们报道了一种脑机接口的设计和性能，该接口基于在快速串行视觉呈现过程中获得的32通道脑电图（EEG）中人类动态脑反应特征，用于对已观看图像进行单次检测。该系统通过利用人类瞬间的感知判断来探索加速图像分析的可行性。我们提出了一种增量学习系统，该系统具有较少的内存存储和计算成本，用于单试验事件相关电位（ERP）检测，该系统使用交叉会话数据进行训练。我们证明了该方法在目标图像检测任务上的有效性。我们应用线性和非线性支持向量机（svm）和线性逻辑分类器（LLC）进行单试验ERP检测，使用从图像分析师和幼稚受试者收集的数据。对于我们的数据，非线性支持向量机的检测性能优于线性支持向量机和LLC。我们还表明，基于erp的目标检测系统比传统的图像查看范式快5倍。(C) 2011 Elsevier B.V.版权所有
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. (C) 2011 Elsevier B.V. All rights reserved.