Real World BCI

Gordon, Stephen; Jaswa, Matthew; Solon, Amelia J.; Lawhern, Vernon J.

doi:10.1145/3038439.3038444

Cited by 22 publications

(17 citation statements)

References 6 publications

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“…In other words, we trained a BCI model using data from one experiment and set of subjects and applied that model to another, unseen, experiment and set of subjects. As expected, these across-experiments models performed worse when compared to within experiment models [24]. However, when we pooled together multiple experiments, thus increasing the amount and diversity of training data, the average performance on unseen test sets increased.…”

Section: B Pooled-experiments Visual Target Detection Modelssupporting

confidence: 58%

“…Previously, we showed that EEGNet enabled crosssubject transfer performance equal to or better than conventional approaches for several BCI paradigms. EEGNet is also the model we used to obtain our cross-experiment results described in [23,24]. Fig.…”

Section: A Bci Model Development 1) Model Architecturementioning

confidence: 99%

“…While these specific classifiers yield higher performance, they are contingent on training data that match the exact user and end application. This assumption is valid for laboratory experiments, games, and several assistive technologies, but our previous research indicates that this may be problematic for real-world applications [23,24].…”

Section: B Pooled-experiments Visual Target Detection Modelsmentioning

confidence: 99%

“…on the street or in a doorway) at an approximate rate of once every three seconds. Targets stayed on screen for one second before disappearing [24]. Participants were free to scan the environment but were instructed to indicate the type of target they observed by pressing a button with either the left or right index finger.…”

Section: B Test Set: Free-viewing Target Detection In Videomentioning

confidence: 99%

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Collaborative Brain-Computer Interface for Human Interest Detection in Complex and Dynamic Settings

Solon

Gordon

McDaniel

et al. 2018

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Humans can fluidly adapt their interest in complex environments in ways that machines cannot. Here, we lay the groundwork for a real-world system that passively monitors and merges neural correlates of visual interest across team members via Collaborative Brain Computer Interface (cBCI). When group interest is detected and co-registered in time and space, it can be used to model the task relevance of items in a dynamic, natural environment. Previous work in cBCIs focuses on static stimuli, stimulus-or response-locked analyses, and often within-subject and experiment model training. The contributions of this work are twofold. First, we test the utility of cBCI on a scenario that more closely resembles natural conditions, where subjects visually scanned a video for target items in a virtual environment. Second, we use an experiment-agnostic deep learning model to account for the real-world use case where no training set exists that exactly matches the end-users' task and circumstances. With our approach we show improved performance as the number of subjects in the cBCI ensemble grows, and the potential to reconstruct ground-truth target occurrence in an otherwise noisy and complex environment.

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Section: B Pooled-experiments Visual Target Detection Modelssupporting

confidence: 58%

Section: A Bci Model Development 1) Model Architecturementioning

confidence: 99%

Section: B Pooled-experiments Visual Target Detection Modelsmentioning

confidence: 99%

Section: B Test Set: Free-viewing Target Detection In Videomentioning

confidence: 99%

See 2 more Smart Citations

Collaborative Brain-Computer Interface for Human Interest Detection in Complex and Dynamic Settings

Solon

Gordon

McDaniel

et al. 2018

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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“…Furthermore, [13,44] showed that EEGNet enabled cross-subject transfer performance equal to or better than conventional approaches for several EEG classification paradigms, both event-related and oscillatory. EEGNet is also the model used to obtain our crossexperiment results described in [26,39,45].…”

Section: Cnns For Neural Decodingmentioning

confidence: 99%

Decoding P300 Variability using Convolutional Neural Networks

Solon

Lawhern

Touryan

et al. 2019

Preprint

Self Cite

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Deep convolutional neural networks (CNN) have previously been shown to be useful tools for signal decoding and analysis in a variety of complex domains, such as image processing and speech recognition. By learning from large amounts of data, the representations encoded by these deep networks are often invariant to moderate changes in the underlying feature spaces. Recently, we proposed a CNN architecture that could be applied to electroencephalogram (EEG) decoding and analysis. In this article, we train our CNN model using data from prior experiments in order to later decode the P300 evoked response from an unseen, hold-out experiment. We analyze the CNN output as a function of the underlying variability in the P300 response and demonstrate that the CNN output is sensitive to the experiment-induced changes in the neural response. We then assess the utility of our approach as a means of improving the overall signalto-noise ratio in the EEG record. Finally, we show an example of how CNN-based decoding can be applied to the analysis of complex data.

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Recent Advances in Brain-Computer Interface Research: A Summary of the 2019 BCI Award and Online BCI Research Activities

Guger

Tangermann

Allison

2021

SpringerBriefs in Electrical and Computer Engineering

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The introduction chapter of this book described the BCI Research Awards, selection criteria, nominees, and jury. Developing a good submission for a BCI Research Award is a formidable goal, and being nominated is even more demanding. This book has presented thirteen chapters by the authors of projects nominated for a BCI Research Award in 2019. Some of these chapters detailed the projects that were nominated, while other chapters comprised interviews with nominees. In this chapter, we review the 2019 BCI Research Awards Ceremony and present the winners. We also discuss emerging directions such as online BCI-related activities that have become much more prominent during 2020 due to COVID concerns.

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Real World BCI

Cited by 22 publications

References 6 publications

Collaborative Brain-Computer Interface for Human Interest Detection in Complex and Dynamic Settings

Collaborative Brain-Computer Interface for Human Interest Detection in Complex and Dynamic Settings

Decoding P300 Variability using Convolutional Neural Networks

Recent Advances in Brain-Computer Interface Research: A Summary of the 2019 BCI Award and Online BCI Research Activities

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