Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI

@inproceedings{Manor2015ConvolutionalNN,
  title={Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI},
  author={Ran Manor and Amir B. Geva},
  booktitle={Front. Comput. Neurosci.},
  year={2015}
}
Brain computer interfaces rely on machine learning (ML) algorithms to decode the brain's electrical activity into decisions. For example, in rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. Here, we continue our previous work, presenting a deep neural network model for the use of single trial EEG… CONTINUE READING
Highly Cited
This paper has 27 citations. REVIEW CITATIONS
Related Discussions
This paper has been referenced on Twitter 1 time. VIEW TWEETS

Citations

Publications citing this paper.
Showing 1-10 of 16 extracted citations

EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces

Journal of neural engineering • 2018
View 5 Excerpts
Highly Influenced

A survey of deep neural network architectures and their applications

Neurocomputing • 2017
View 5 Excerpts
Highly Influenced

EEG classification of driver mental states by deep learning

Cognitive Neurodynamics • 2018
View 2 Excerpts

Convolutional neural networks for event-related potential detection: impact of the architecture

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) • 2017
View 1 Excerpt

Cross-subject classification of cognitive loads using a recurrent-residual deep network

2017 IEEE Symposium Series on Computational Intelligence (SSCI) • 2017
View 1 Excerpt

References

Publications referenced by this paper.
Showing 1-10 of 45 references

Spatio - temporal representations of rapid visual target detection : a single trial eeg classification algorithm

G. Fuhrmann Alpert, R. Manor, A. Spanier, L. Deouell, A. Geva
IEEE Trans . Biomed . Eng . • 2013
View 20 Excerpts
Highly Influenced

Cortically-Coupled Computer Vision

Brain-Computer Interfaces • 2010
View 4 Excerpts
Highly Influenced

Spatiotemporal Linear Decoding of Brain State

IEEE Signal Processing Magazine • 2008
View 5 Excerpts
Highly Influenced

Cortically coupled computer vision for rapid image search

IEEE Transactions on Neural Systems and Rehabilitation Engineering • 2006
View 4 Excerpts
Highly Influenced

Analyzing EEG signals

T. Felzer, B. Freisieben
2004
View 4 Excerpts
Highly Influenced

Some methods of speeding up the convergence of iteration methods

B. T. Polyak
USSR Comput . Math . Math . Phys . • 1964
View 1 Excerpt
Highly Influenced

Similar Papers

Loading similar papers…