A collaborative Brain-Computer Interface for improving group detection of visual targets in complex natural environments

D. Valeriani, R. Poli, C. Cinel
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引用次数: 18

Abstract

Detecting a target in a complex environment can be a difficult task, both for a single individual and a group, especially if the scene is very rich of structure and there are strict time constraints. In recent research, we have demonstrated that collaborative Brain-Computer Interfaces (cBCIs) can use neural signals and response times to estimate the decision confidence of participants and use this to improve group decisions in visual-matching and visual-search tasks with artificial stimuli. This paper extends that work in two ways. Firstly, we use a much harder target detection task where observers are presented with complex natural scenes where targets are very difficult to identify. Secondly, we complement the neural and behavioural information used in our previous cBCIs with physiological features representing eye movements and eye blinks of participants in the period preceding their decisions. Results obtained with 10 participants indicate that the proposed cBCI improves decision errors by up to 3.4% (depending on group size) over group decisions made by a majority vote. Furthermore, results show that providing the system with information about eye movements and blinks further significantly improves performance over our best previously reported method.
一种用于改善复杂自然环境中视觉目标群体检测的协同脑机接口
在一个复杂的环境中检测目标是一项艰巨的任务,无论是对一个人还是一个群体,特别是如果场景非常丰富的结构和有严格的时间限制。在最近的研究中,我们已经证明了协作脑机接口(cbci)可以使用神经信号和响应时间来估计参与者的决策信心,并利用它来改善具有人工刺激的视觉匹配和视觉搜索任务中的群体决策。本文从两个方面扩展了这一工作。首先,我们使用了一个更困难的目标检测任务,在这个任务中,观察者面对的是非常复杂的自然场景,目标很难识别。其次,我们补充了之前cbci中使用的神经和行为信息,其中包括参与者在做出决定之前的一段时间内的眼动和眨眼的生理特征。10名参与者获得的结果表明,与多数投票做出的群体决策相比,拟议的cBCI可将决策错误提高3.4%(取决于群体规模)。此外,结果表明,为系统提供有关眼球运动和眨眼的信息,比我们之前报道的最佳方法进一步显著提高了性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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