Past and Future of Multi-Mind Brain–Computer Interfaces

Valeriani, Davide; Matran-Fernandez, Ana

doi:10.1201/9781351231954-36

Cited by 9 publications

(10 citation statements)

References 50 publications

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“…For the task of dynamic target detection, the dynamics of video background, the uncertainty of distractors, and the jitter of detection latency increase the detection difficulty, resulting in the limitations of single-mind BCI [ 10 , 11 ]. The cBCI can be considered as a good strategy to solve the problem, which will contribute to improving the stability and accuracy of comprehensive discrimination [ 12 , 13 ]. Therefore, building a novel cBCI framework to highlight the performance advantages of multi-mind enhancement has become the research focus to improve the performance of dynamic visual target detection.…”

Section: Introductionmentioning

confidence: 99%

A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets

Song

Zeng

et al. 2022

Computational Intelligence and Neuroscience

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The superiority of collaborative brain-computer interface (cBCI) in performance enhancement makes it an effective way to break through the performance bottleneck of the BCI-based dynamic visual target detection. However, the existing cBCIs focus on multi-mind information fusion with a static and unidirectional mode, lacking the information interaction and learning guidance among multiple agents. Here, we propose a novel cBCI framework to enhance the group detection performance of dynamic visual targets. Specifically, a mutual learning domain adaptation network (MLDANet) with information interaction, dynamic learning, and individual transferring abilities is developed as the core of the cBCI framework. MLDANet takes P3-sSDA network as individual network unit, introduces mutual learning strategy, and establishes a dynamic interactive learning mechanism between individual networks and collaborative decision-making at the neural decision level. The results indicate that the proposed MLDANet-cBCI framework can achieve the best group detection performance, and the mutual learning strategy can improve the detection ability of individual networks. In MLDANet-cBCI, the F1 scores of collaborative detection and individual network are 0.12 and 0.19 higher than those in the multi-classifier cBCI, respectively, when three minds collaborate. Thus, the proposed framework breaks through the traditional multi-mind collaborative mode and exhibits a superior group detection performance of dynamic visual targets, which is also of great significance for the practical application of multi-mind collaboration.

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Section: Introductionmentioning

confidence: 99%

A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets

Song

Zeng

et al. 2022

Computational Intelligence and Neuroscience

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“…Collaborative BCIs have also been used to control robots (Iturrate et al, 2013; Katyal et al, 2014; Li and Nam, 2015), video games (Nijholt and Gürkök, 2013; Nijholt, 2015), cursors and simulated space crafts (Poli et al, 2013), spellers (https://www.youtube.com/watch?v=A3SnmhlOTtQ) as well as to analyse the neural signals of people watching movies and identify a relationship between the length of a shot and the amplitude of a large-scale ERPs called post-cut negativity (Matran-Fernandez and Poli, 2015). For a review on collaborative BCIs see (Valeriani and Matran-Fernandez, 2018).…”

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.

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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 is done, for example, in the areas of: (a) neuroergonomics , which uses the neural and cognitive activity underpinning human performance to design systems that allow humans to perform in a safer and more efficient way in everyday tasks and in the work-place 50 , 51 ; (b) passive BCIs 52 – 55 , which monitor spontaneous (i.e. not directly triggered by the BCI itself) brain activity of users performing everyday activities, and react in ways that facilitate such activities for the users; and (c) collaborative BCIs (cBCIs), where the brain activities of multiple users are integrated to achieve a common goal 33 , 56 – 67 . The last form, cBCIs, offer a solution to the problem of improving group decision-making.…”

Section: Introductionmentioning

confidence: 99%

Anytime collaborative brain–computer interfaces for enhancing perceptual group decision-making

Bhattacharyya

Valeriani

Cinel

et al. 2021

Sci Rep

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In this paper we present, and test in two realistic environments, collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of perceptual group decision-making. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to be able to provide a group decision at any time after the quickest team member casts their vote, but the quality of a cBCI-assisted decision improves monotonically the longer the group decision can wait; (2) we apply our cBCIs to two realistic scenarios of military relevance (patrolling a dark corridor and manning an outpost at night where users need to identify any unidentified characters that appear) in which decisions are based on information conveyed through video feeds; and (3) our cBCIs exploit Event-Related Potentials (ERPs) elicited in brain activity by the appearance of potential threats but, uniquely, the appearance time is estimated automatically by the system (rather than being unrealistically provided to it). As a result of these elements, in the two test environments, groups assisted by our cBCIs make both more accurate and faster decisions than when individual decisions are integrated in more traditional manners.

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Past and Future of Multi-Mind Brain–Computer Interfaces

Cited by 9 publications

References 50 publications

A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets

A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets

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

Anytime collaborative brain–computer interfaces for enhancing perceptual group decision-making

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