Brain–Computer Interfaces for Detection and Localization of Targets in Aerial Images

Matran-Fernandez, Ana; Poli, Roberto

doi:10.1109/tbme.2016.2583200

Cited by 32 publications

(37 citation statements)

References 59 publications

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“…Our results (see Table 2) show large variations of performance across participants for this classification task, an effect that had been previously noted by Awni and collaborators [41], and our own previous research [29]. …”

Section: Discussionsupporting

confidence: 85%

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Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfaces

Matran-Fernandez

Poli

2017

PLoS ONE

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The N2pc is a lateralised Event-Related Potential (ERP) that signals a shift of attention towards the location of a potential object of interest. We propose a single-trial target-localisation collaborative Brain-Computer Interface (cBCI) that exploits this ERP to automatically approximate the horizontal position of targets in aerial images. Images were presented by means of the rapid serial visual presentation technique at rates of 5, 6 and 10 Hz. We created three different cBCIs and tested a participant selection method in which groups are formed according to the similarity of participants’ performance. The N2pc that is elicited in our experiments contains information about the position of the target along the horizontal axis. Moreover, combining information from multiple participants provides absolute median improvements in the area under the receiver operating characteristic curve of up to 21% (for groups of size 3) with respect to single-user BCIs. These improvements are bigger when groups are formed by participants with similar individual performance, and much of this effect can be explained using simple theoretical models. Our results suggest that BCIs for automated triaging can be improved by integrating two classification systems: one devoted to target detection and another to detect the attentional shifts associated with lateral targets.

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Section: Discussionsupporting

confidence: 85%

“…However, as in [29, 30], in this paper we have applied BCIs to the problem of localisation of targets within images (via N2pc ERPs), not to the problem of classifying images as containing or not containing a target.…”

Section: Introductionmentioning

confidence: 99%

Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfaces

Matran-Fernandez

Poli

2017

PLoS ONE

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“…IT R(bits/minute) = R 60/τ (8) where N is the number of classes (or stimuli, 6 in this study), P is the accuracy and τ is the time in seconds for one trial (2,400 ms in this study), see section II-D. The results of our method showed an increase in bitrate in all datasets (see ).…”

Section: B Bitrate Improvementmentioning

confidence: 73%

“…Next, the quantification of ITR is described, and we used the bitrate definition proposed in [65], in which the equations (7) and (8) are used…”

Section: B Bitrate Improvementmentioning

confidence: 99%

A Low-Cost Computational Method for Characterizing Event-Related Potentials for BCI Applications and Beyond

Changoluisa

Varona²,

Rodrı́guez³

2020

IEEE Access

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Event-related potentials (ERPs) are important neurophysiological markers widely used in scientific, medical and engineering contexts. Proper ERP detection contributes to widening the scope of use and, in general, improving functionality. The morphology and latency of ERPs are variable among subject sessions, which complicates their detection. Although variability is an intrinsic feature of neuronal activity, it can be addressed with novel views on ERP detection techniques. In this paper, we propose an agile method for characterizing and thus detecting variable ERPs, which keeps track of their temporal and spatial information through the continuous measurement of the area under the curve in ERP components. We illustrate the usefulness of the proposed ERP characterization for electrode selection in brain-computer interfaces (BCIs) and compare the results with other standard methods. We assess ERP classification for BCI use with Bayesian linear discriminant analysis (BLDA) and cross-validation. We also evaluate performance with both the information transfer rate and BCI utility. The results of our validation tests show that this characterization helps to take advantage of the information on the evolution of positive and negative ERP components and, therefore, to efficiently select electrodes for optimized ERP detection. The proposed method improves the classification accuracy and bitrate of all sets of electrodes analyzed. Furthermore, the method is robust between different day sessions. Our work contributes to the efficient detection of ERPs, manages inter-and intrasubject variability, decreases the computational cost of classic detection methods and contributes to promoting low-cost personalized brain-computer interfaces.

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“…For instance, Matran-Fernandez et al (2013) used the presence of P300s to detect aeroplanes in rapidly presented aerial pictures. The N2pc, an ERP that appears approximately 250 ms after stimulus presentation on the opposite side of the scalp with respect to the visual hemispace where an object of interest is located, has been used for BCIs for determining the location(rather than the presence) of targets in aerial pictures (Matran-Fernandez and Poli, 2017a).…”

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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112

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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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Brain–Computer Interfaces for Detection and Localization of Targets in Aerial Images

Cited by 32 publications

References 59 publications

Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfaces

Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfaces

A Low-Cost Computational Method for Characterizing Event-Related Potentials for BCI Applications and Beyond

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

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