Rapid face recognition based on single-trial event-related potential detection over multiple brains

Jiang, Lei; Wang, Yun; Cai, Bangyu; Wang, Yiwen; Chen, Weidong; Zheng, Xiaoxiang

doi:10.1109/ner.2015.7146571

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…The generation of such brain-activity patterns in response to faces of interest has made possible the development of brain-computer interfaces (BCIs) that can improve human performance in face recognition [5], [6], [7]. The accuracy and the speed with which we recognise faces can also be further enhanced with collaborative BCIs (cBCIs) which integrate information from multiple brains [8].…”

Section: Introduction a Motivationmentioning

confidence: 99%

Augmenting group performance in target-face recognition via collaborative brain-computer interfaces for surveillance applications

Valeriani

Cinel

Poli

2017

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)

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Abstract-This paper presents a hybrid collaborative braincomputer interface (cBCI) to improve group-based recognition of target faces in crowded scenes recorded from surveillance cameras. The cBCI uses a combination of neural features extracted from EEG and response times to estimate the decision confidence of the users. Group decisions are then obtained by weighing individual responses according to these confidence estimates. Results obtained with 10 participants indicate that the proposed cBCI improves decision errors by up to 7% over traditional group decisions based on majority. Moreover, the confidence estimates obtained by the cBCI are more accurate and robust than the confidence reported by the participants after each decision. These results show that cBCIs can be an effective means of human augmentation in realistic scenarios.

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Section: Introduction a Motivationmentioning

confidence: 99%

Augmenting group performance in target-face recognition via collaborative brain-computer interfaces for surveillance applications

Valeriani

Cinel

Poli

2017

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)

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“…Finally, users may have individual BCIs that predict their intentions, which a voting system integrates to compute the group's decision. Various studies (Wang and Jung, 2011; Matran-Fernandez et al, 2013; Stoica et al, 2013; Jiang et al, 2015) suggest that this voting method is often optimal for collaborative EEG-based classification, especially when the scores of the single classifiers (instead of the predicted class) are used for the integration (Cecotti and Rivet, 2014). Wang et al (2011) proposed a first collaborative framework for BCIs where an ensemble classifier was used to integrate the outputs of single BCIs.…”

Section: Applications Of Neuroscience Technologies For Human Augmementioning

confidence: 99%

Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects

Cinel

Valeriani

Poli

2019

Front. Hum. Neurosci.

124

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Recent advances in neuroscience have paved the way to innovative applications that cognitively augment and enhance humans in a variety of contexts. This paper aims at providing a snapshot of the current state of the art and a motivated forecast of the most likely developments in the next two decades. Firstly, we survey the main neuroscience technologies for both observing and influencing brain activity, which are necessary ingredients for human cognitive augmentation. We also compare and contrast such technologies, as their individual characteristics (e.g., spatio-temporal resolution, invasiveness, portability, energy requirements, and cost) influence their current and future role in human cognitive augmentation. Secondly, we chart the state of the art on neurotechnologies for human cognitive augmentation, keeping an eye both on the applications that already exist and those that are emerging or are likely to emerge in the next two decades. Particularly, we consider applications in the areas of communication, cognitive enhancement, memory, attention monitoring/enhancement, situation awareness and complex problem solving, and we look at what fraction of the population might benefit from such technologies and at the demands they impose in terms of user training. Thirdly, we briefly review the ethical issues associated with current neuroscience technologies. These are important because they may differentially influence both present and future research on (and adoption of) neurotechnologies for human cognitive augmentation: an inferior technology with no significant ethical issues may thrive while a superior technology causing widespread ethical concerns may end up being outlawed. Finally, based on the lessons learned in our analysis, using past trends and considering other related forecasts, we attempt to forecast the most likely future developments of neuroscience technology for human cognitive augmentation and provide informed recommendations for promising future research and exploitation avenues.

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“…This discovery made it possible to study the brain's naive responses to stimuli by averaging signals across multiple users, hence increasing the low signal-to-noise ratio that is typical in EEG-based BCIs. Moreover, the high time resolution provided by EEG systems allows researchers to use this technique also for active multimind BCIs, for example, in those based on event-related potentials (ERPs), which traditionally rely on multiple repetitions of a stimulus in single-user interfaces (Farwell and Donchin 1988;Jiang et al 2015;Kapeller et al 2014;Korczowski et al 2015;Matran-Fernandez and Poli 2015;Vidal 1973).…”

Section: History Of Multi-mind Bcismentioning

confidence: 99%

“…The simplest mode of combining evidence from multiple users is at the signal level. At this level, EEG signals of different users are averaged and either fed into a unique classifier directly, without extracting any feature (Cecotti and Rivet 2014a,b;Jiang et al 2015;Kapeller et al 2014;Korczowski et al 2015;Matran-Fernandez and Poli 2014;Matran-Fernandez et al 2013;Poli et al 2013a), or used to perform multi-user analyses, taking advantage of the increased signal-to-noise ratio that can be achieved by averaging trials from multiple users (De Vico Fallani et al 2010;Matran-Fernandez and Poli 2015).…”

Section: Implementing a Collective Brainmentioning

confidence: 99%

“…However, the results were obtained with very simple tasks. In recent years, multi-mind BCIs have been applied to progressively more complex and challenging decision-making tasks, including face recognition (Jiang et al 2015), detection of visual targets in slow (Yuan et al 2012) and rapid presentation of images (Matran-Fernandez et al 2013;Stoica et al 2013), and target localization within images Poli 2014, 2017). Studies from Matran-Fernandez and collaborators also found that pairing participants on the basis of their similarity in performance could further enhance the accuracy of the multi-mind BCI.…”

Section: Target Detection and Decision Makingmentioning

confidence: 99%

See 1 more Smart Citation

Past and Future of Multi-Mind Brain–Computer Interfaces

Valeriani¹,

Matran-Fernandez²

2018

Brain–Computer Interfaces Handbook

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The great improvements in brain-computer interface (BCI) performance that are brought upon by merging brain activity from multiple users have made this a popular strategy that allows even for human augmentation. These multi-mind BCIs have contributed in changing the role of BCIs from assistive technologies for people with disabilities into tools for human enhancement. This chapter reviews the history of multi-mind BCIs that have their root in the hyperscanning technique; the collaborative and competitive approaches; and the different ways that exist to integrate the brain signals from multiple people and optimally form groups to maximize performance. The main applications of multi-mind BCIs, including control of external devices, entertainment, and decision making, are also surveyed and discussed, in order to help the reader understand what are the most promising avenues and find the gaps that are worthy of future exploration. The chapter also provides a step-by-step tutorial to the design and implementation of a multi-mind BCI, with theoretical guidelines and a sample application.

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Rapid face recognition based on single-trial event-related potential detection over multiple brains

Cited by 7 publications

References 9 publications

Augmenting group performance in target-face recognition via collaborative brain-computer interfaces for surveillance applications

Augmenting group performance in target-face recognition via collaborative brain-computer interfaces for surveillance applications

Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects

Past and Future of Multi-Mind Brain–Computer Interfaces

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