Proceedings ArticleDOI
Single trial independent component analysis for P300 BCI system
Kun Li,Ravi Sankar,Yael Arbel,Emanuel Donchin +3 more
- Vol. 2009, pp 4035-4038
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TLDR
A single trial independent component analysis (ICA) method that is working with a BCI system proposed by Farwell and Donchin can dramatically reduce the signal processing time and improve the data communicating rate.Abstract:
A Brain Computer Interface (BCI) is a device that allows the user to communicate with the world without utilizing voluntary muscle activity (i.e., using only the electrical activity of the brain). It makes use of the well-studied observation that the brain reacts differently to different stimuli, as a function of the level of attention allotted to the stimulus stream and the specific processing triggered by the stimulus. In this article we present a single trial independent component analysis (ICA) method that is working with a BCI system proposed by Farwell and Donchin. It can dramatically reduce the signal processing time and improve the data communicating rate. This ICA method achieved 76.67% accuracy on single trial P300 response identification.read more
Citations
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Proceedings ArticleDOI
An improved P300 extraction using ICA-R for P300-BCI speller
TL;DR: A new P300 extraction method is investigated by using a form of constrained independent component analysis (cICA) algorithm called one-unit ICA-with-reference (ICA-R) which extracts the P300 signal based on its temporal information.
Proceedings ArticleDOI
Comparison of EEG blind source separation techniques to improve the classification of P300 trials
Zach Cashero,Charles W. Anderson +1 more
TL;DR: This paper provides a comparison of several blind source separation techniques as they are applied to EEG signals and analyzes the effect of adding temporal information to the original data, which allows these BSS algorithms to find more complex spatio-temporal patterns.
Proceedings ArticleDOI
A genetic algorithm for single-trial P300 detection with a low-cost EEG headset
Riley Magee,Sidney N. Givigi +1 more
TL;DR: A genetic algorithm (GA) is used in combination with both a neural network and linear discriminant analysis classifiers to improve single-trial P300 detection and explore the results of those features found influential on P300 classification.
Journal ArticleDOI
Human-in-the-loop active learning via brain computer interface
Eitan Netzer,Amir B. Geva +1 more
TL;DR: This work is proof of concept for the effectiveness of involving humans in the computer’s learning stage, i.e., human-in-the-loop as opposed to the traditional method of humans first tagging the data and the machines then learning and creating a model.
Proceedings ArticleDOI
Deep convolutional neural network based character detection in devanagari script input based P300 speller
TL;DR: DCNN, a one of the powerful tool, has adopted in this work to classify the target and non-target p300 components from acquired EEG signal and results illustrated that the proposed technique has achieved 94.18% accuracy for P300 detection, higher than existing techniques.
References
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