Attentional load classification in multiple object tracking task using optimized support vector machine classifier: a step towards cognitive brain–computer interface

2021 ◽  
pp. 1-9
Author(s):  
Sweeti
Keyword(s):  
Support Vector Machine ◽  
Object Tracking ◽  
Support Vector Machine Classifier ◽  
Brain Computer Interface ◽  
Multiple Object Tracking ◽  
Tracking Task ◽  
Computer Interface ◽  
Support Vector ◽  
Multiple Object ◽  
Attentional Load

scholarly journals Pupil Sizes Scale with Attentional Load and Task Experience in a Multiple Object Tracking Task

PLoS ONE ◽  
2016 ◽  
Vol 11 (12) ◽  
pp. e0168087 ◽  
Author(s):  
Basil Wahn ◽  
Daniel P. Ferris ◽  
W. David Hairston ◽  
Peter König
Keyword(s):  
Object Tracking ◽  
Multiple Object Tracking ◽  
Tracking Task ◽  
Multiple Object ◽  
Attentional Load ◽  
Task Experience ◽  
And Task

scholarly journals A Prototype SSVEP Based Real Time BCI Gaming System

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Ignas Martišius ◽  
Robertas Damaševičius
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Disabled People ◽  
Linear Discriminant ◽  
Average Accuracy ◽  
Wave Atom Transform ◽  
Linear Discriminant Analysis Classifier

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

scholarly journals Expanding Attentional Capacity with Adaptive Training on a Multiple Object Tracking Task

2011 ◽  
Vol 11 (11) ◽  
pp. 292-292
Author(s):  
T. W. Thompson ◽  
M. L. Waskom ◽  
J. D. E. Gabrieli ◽  
G. A. Alvarez
Keyword(s):  
Object Tracking ◽  
Multiple Object Tracking ◽  
Tracking Task ◽  
Attentional Capacity ◽  
Adaptive Training ◽  
Multiple Object

Practical Artifact Removal Brain-Computer Interface System

2014 ◽  
pp. 265-277
Author(s):  
Wei-Yen Hsu
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Artifact Removal ◽  
Eeg Data ◽  
Interface System ◽  
Electroencephalogram Eeg ◽  
Bci System

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface

2011 ◽  
Vol 3 (3) ◽  
pp. 43-56 ◽  
Author(s):  
Ling Zou ◽  
Xinguang Wang ◽  
Guodong Shi ◽  
Zhenghua Ma
Keyword(s):  
Support Vector Machine ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Average Power ◽  
Computer Interface ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Reconstructed Signal

Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode and C4 electrode in left-right hands imagery movement during some periods of time. The reconstructed signal of approximation coefficient A6 on the sixth level was selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis with two different threshold calculation ways and Support Vector Machine methods were compared. The final classification results showed that false classification rate by Support Vector Machine was lower and gained an ideal classification results.

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