被动脑机接口对认知人脸加工单次试验可探测性的研究

Rebecca Pham Xuan, Lena M. Andreessen, T. Zander
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引用次数: 1

摘要

人脸的自动识别使机器能够从视觉上识别一个人,并获得非语言交流的机会,包括模仿。在实验室设置或受控现实环境中的不同方法提供了证据,证明自动人脸检测和识别在原则上是可行的,尽管在复杂的现实场景中的应用会带来不同类型的问题,目前还无法解决。具体来说,在自动驾驶中,如果汽车能够识别行人或其他司机的非语言交流,那将是有益的,因为这是日常交通中常见的交流方式。从观察中自动识别行人或其他司机是否通过模仿的微妙线索进行交流是一个尚未解决的问题,因为意图和其他认知因素很难从观察中得出。相比之下,交际者通常对自己是否在交际有清晰的认识,这些信息都表现在他们的心态中。本研究探讨了是否可以通过被动脑机接口(pBCI)来识别人脸的心理处理。然后,这可以用来支持汽车对行人面部模仿的自动解释,以识别非语言交流。此外,在部分自动驾驶中,专注的驾驶员可以作为传感器来提高汽车的上下文感知。这项工作提出了一项实验室研究,其中pBCI被校准以检测脑电图(EEG)中梭状回的反应,反映面部识别。研究人员向参与者展示了三种不同类别的图片:面孔、摘要和房屋,这些图片引发了用于校准pBCI的不同反应。在一次试验中,所得到的分类器可以区分对人脸的反应和其他刺激引起的反应,准确率超过70%。对分类方法和基础数据的进一步分析确定了脑电图中与梭状回面部识别相对应的激活模式。由此产生的pBCI方法是有希望的,因为它显示出优于随机的准确性,并且基于相关的和预期的大脑反应。未来的研究必须调查它能否从实验室转移到现实世界,以及如何将其应用于人工智能,就像用于自动驾驶一样。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigating the Single Trial Detectability of Cognitive Face Processing by a Passive Brain-Computer Interface
An automated recognition of faces enables machines to visually identify a person and to gain access to non-verbal communication, including mimicry. Different approaches in lab settings or controlled realistic environments provided evidence that automated face detection and recognition can work in principle, although applications in complex real-world scenarios pose a different kind of problem that could not be solved yet. Specifically, in autonomous driving—it would be beneficial if the car could identify non-verbal communication of pedestrians or other drivers, as it is a common way of communication in daily traffic. Automated identification from observation whether pedestrians or other drivers communicate through subtle cues in mimicry is an unsolved problem so far, as intent and other cognitive factors are hard to derive from observation. In contrast, communicating persons usually have clear understanding whether they communicate or not, and such information is represented in their mindsets. This work investigates whether the mental processing of faces can be identified through means of a Passive Brain-Computer Interface (pBCI). This then could be used to support the cars' autonomous interpretation of facial mimicry of pedestrians to identify non-verbal communication. Furthermore, the attentive driver can be utilized as a sensor to improve the context awareness of the car in partly automated driving. This work presents a laboratory study in which a pBCI is calibrated to detect responses of the fusiform gyrus in the electroencephalogram (EEG), reflecting face recognition. Participants were shown pictures from three different categories: faces, abstracts, and houses evoking different responses used to calibrate the pBCI. The resulting classifier could distinguish responses to faces from that evoked by other stimuli with accuracy above 70%, in a single trial. Further analysis of the classification approach and the underlying data identified activation patterns in the EEG that corresponds to face recognition in the fusiform gyrus. The resulting pBCI approach is promising as it shows better-than-random accuracy and is based on relevant and intended brain responses. Future research has to investigate whether it can be transferred from the laboratory to the real world and how it can be implemented into artificial intelligences, as used in autonomous driving.
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