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- Title
Training set extension for SVM ensemble in P300-speller with familiar face paradigm.
- Authors
Li, Qi; Shi, Kaiyang; Gao, Ning; Li, Jian; Bai, Ou
- Abstract
<bold>Background: </bold>P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale training data necessitate a prolonged collection time for each subject, which results in data collected toward the end of the period being contaminated by the subject's fatigue.<bold>Objective: </bold>This study aimed to develop a method for acquiring more training data based on a collected small training set.<bold>Methods: </bold>A new method was developed in which two corresponding training datasets in two sequences are superposed and averaged to extend the training set. The proposed method was tested offline on a P300-speller with the familiar face paradigm.<bold>Results: </bold>The SVM ensemble with extended training set achieved 85% classification accuracy for the averaged results of four sequences, and 100% for 11 sequences in the P300-speller. In contrast, the conventional SVM ensemble with non-extended training set achieved only 65% accuracy for four sequences, and 92% for 11 sequences.<bold>Conclusion: </bold>The SVM ensemble with extended training set achieves higher classification accuracies than the conventional SVM ensemble, which verifies that the proposed method effectively improves the classification performance of BCI P300-spellers, thus enhancing their practicality.
- Subjects
BRAIN-computer interfaces; BIOMECHATRONICS; SUPPORT vector machines; COMPUTER science; SUPERVISED learning
- Publication
Technology & Health Care, 2018, Vol 26, Issue 3, p469
- ISSN
0928-7329
- Publication type
journal article
- DOI
10.3233/THC-171074