21,531 research outputs found
Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation
Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the
application of cortically coupled computer vision to rapid image search. In
RSVP, images are presented to participants in a rapid serial sequence which can
evoke Event-related Potentials (ERPs) detectable in their Electroencephalogram
(EEG). The contemporary approach to this problem involves supervised spatial
filtering techniques which are applied for the purposes of enhancing the
discriminative information in the EEG data. In this paper we make two primary
contributions to that field: 1) We propose a novel spatial filtering method
which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we
provide a comprehensive comparison of nine spatial filtering pipelines using
three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern
(CSP) and three linear classification methods Linear Discriminant Analysis
(LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR). Three
pipelines without spatial filtering are used as baseline comparison. The Area
Under Curve (AUC) is used as an evaluation metric in this paper. The results
reveal that MTWLB and xDAWN spatial filtering techniques enhance the
classification performance of the pipeline but CSP does not. The results also
support the conclusion that LR can be effective for RSVP based BCI if
discriminative features are available
Dispelling Classes Gradually to Improve Quality of Feature Reduction Approaches
Feature reduction is an important concept which is used for reducing
dimensions to decrease the computation complexity and time of classification.
Since now many approaches have been proposed for solving this problem, but
almost all of them just presented a fix output for each input dataset that some
of them aren't satisfied cases for classification. In this we proposed an
approach as processing input dataset to increase accuracy rate of each feature
extraction methods. First of all, a new concept called dispelling classes
gradually (DCG) is proposed to increase separability of classes based on their
labels. Next, this method is used to process input dataset of the feature
reduction approaches to decrease the misclassification error rate of their
outputs more than when output is achieved without any processing. In addition
our method has a good quality to collate with noise based on adapting dataset
with feature reduction approaches. In the result part, two conditions (With
process and without that) are compared to support our idea by using some of UCI
datasets.Comment: 11 Pages, 5 Figure, 7 Tables; Advanced Computing: An International
Journal (ACIJ), Vol.3, No.3, May 201
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