Gerson Adam D, Parra Lucas C, Sajda Paul
Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
IEEE Trans Neural Syst Rehabil Eng. 2006 Jun;14(2):174-9. doi: 10.1109/TNSRE.2006.875550.
We describe a real-time electroencephalography (EEG)-based brain-computer interface system for triaging imagery presented using rapid serial visual presentation. A target image in a sequence of nontarget distractor images elicits in the EEG a stereotypical spatiotemporal response, which can be detected. A pattern classifier uses this response to reprioritize the image sequence, placing detected targets in the front of an image stack. We use single-trial analysis based on linear discrimination to recover spatial components that reflect differences in EEG activity evoked by target versus nontarget images. We find an optimal set of spatial weights for 59 EEG sensors within a sliding 50-ms time window. Using this simple classifier allows us to process EEG in real time. The detection accuracy across five subjects is on average 92%, i.e., in a sequence of 2500 images, resorting images based on detector output results in 92% of target images being moved from a random position in the sequence to one of the first 250 images (first 10% of the sequence). The approach leverages the highly robust and invariant object recognition capabilities of the human visual system, using single-trial EEG analysis to efficiently detect neural signatures correlated with the recognition event.
我们描述了一种基于实时脑电图（EEG）的脑机接口系统，用于对使用快速序列视觉呈现的图像进行分类。在一系列非目标干扰图像中的目标图像会在脑电图中引发一种刻板的时空响应，这种响应可以被检测到。模式分类器利用这种响应重新对图像序列进行优先级排序，将检测到的目标置于图像堆栈的前端。我们使用基于线性判别分析的单试验分析来恢复反映目标图像与非目标图像诱发的脑电图活动差异的空间成分。我们在一个滑动的50毫秒时间窗口内为59个脑电图传感器找到了一组最优的空间权重。使用这种简单的分类器使我们能够实时处理脑电图。五名受试者的检测准确率平均为92%，即在2500幅图像的序列中，根据检测器输出对图像进行重新排序，结果92%的目标图像从序列中的随机位置移动到前250幅图像之一（序列的前10%）。该方法利用了人类视觉系统高度稳健且不变的目标识别能力，通过单试验脑电图分析有效地检测与识别事件相关的神经信号。
