Journal ArticleDOI
Multiple kernel learning based on three discriminant features for a P300 speller BCI
Kyungae Yoon,Kiseon Kim +1 more
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TLDR
The proposed multiple kernel learning (MKL) based on three discriminant features to learn an efficient P300 classifier to improve the accuracy of character recognition in a P300 speller BCI consistently obtains better or similar accuracy in terms of character Recognition.About:
This article is published in Neurocomputing.The article was published on 2017-05-10. It has received 21 citations till now. The article focuses on the topics: Kernel Fisher discriminant analysis & Linear discriminant analysis.read more
Citations
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Journal ArticleDOI
A review on transfer learning in EEG signal analysis
TL;DR: Four main methods of transfer learning are described and their practical applications in EEG signal analysis in recent years are explored.
Journal ArticleDOI
P300 based character recognition using convolutional neural network and support vector machine
Sourav Kundu,Samit Ari +1 more
TL;DR: In this article, a 2D convolutional layer based CNN architecture has been proposed where spatio-temporal feature is extracted in a single layer, and Fisher ratio (F-ratio) based feature selection is proposed to find the optimal features.
Journal ArticleDOI
MsCNN: A Deep Learning Framework for P300-Based Brain–Computer Interface Speller
Sourav Kundu,Samit Ari +1 more
TL;DR: A MsCNN model with transfer learning (MsCNN-TL) technique is proposed in this work for improvement of the P300 based character recognition performance with limited amount of training data and achieves better performance compared to the other state-of-the-art techniques for limited training dataset.
Journal ArticleDOI
Learning Discriminative Spatiospectral Features of ERPs for Accurate Brain–Computer Interfaces
TL;DR: This study examined the discriminatory effect of spatiospectral features of event-related potentials (ERPs) to capture the most relevant set of neural activities from electroencephalographic recordings that represent users’ mental intent and constructed predictive models that achieved remarkable performance.
Journal ArticleDOI
A Bayesian Multiple Kernel Learning Algorithm for SSVEP BCI Detection
TL;DR: The benefit of combining multiple kernels is proved by outperforming several state-of-the-art methods in two SSVEP datasets, reaching an information transfer rate of 93 b/min using only three channels from the occipital area.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
Brain-computer interfaces for communication and control.
Jonathan R. Wolpaw,Jonathan R. Wolpaw,Niels Birbaumer,Niels Birbaumer,Dennis J. McFarland,Gert Pfurtscheller,Theresa M. Vaughan +6 more
TL;DR: With adequate recognition and effective engagement of all issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.
Journal ArticleDOI
Updating P300: An Integrative Theory of P3a and P3b
TL;DR: The empirical and theoretical development of the P300 event-related brain potential is reviewed by considering factors that contribute to its amplitude, latency, and general characteristics.
Journal ArticleDOI
Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials
TL;DR: The analyses suggest that this communication channel can be operated accurately at the rate of 0.20 bits/sec, which means that subjects can communicate 12.0 bits, or 2.3 characters, per min.
Journal ArticleDOI
Learning the Kernel Matrix with Semidefinite Programming
Gert R. G. Lanckriet,Nello Cristianini,Peter L. Bartlett,Laurent El Ghaoui,Michael I. Jordan +4 more
TL;DR: This paper shows how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques and leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.
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Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials
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H Cecotti,A Graser +1 more