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Table 1 Classification of target and standard events using a Support Vector Machine (SVM) with RBF kernel and k-nearest neighbor (k-NN) classifier with the k= 5

From: Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques

 

Accuracy

Sensitivity

Specificity

Precision

AUC

SVM

93.60 ± 6.5

93.55 ± 4.5

94.85 ± 4.2

92.50 ± 5.5

0.93 ± 0.3

k-NN

90.80 ± 7.4

91.50 ± 6.2

90.20 ± 7.3

91.45 ± 6.5

0.91 ± 0.5

  1. Data is arranged as mean±standard deviation, AUC stands for ‘Area Under the ROC Curve’. RBF stands for ‘radial basis function’
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Journal of NeuroEngineering and Rehabilitation

ISSN: 1743-0003

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