Training set extension for SVM ensemble in P300-speller with familiar face paradigm

• Published in: Technology and health care Vol. 26; no. 3; pp. 469 - 482
• Main Authors: Li, Qi, Shi, Kaiyang, Gao, Ning, Li, Jian, Bai, Ou
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2018; Sage Publications Ltd
• Subjects: Adult; Algorithms; Brain; Brain-Computer Interfaces; Classification; Computer applications; Data acquisition; Electroencephalography; Event-related potentials; Fatigue; Female; Human-computer interface; Humans; Implants; Male; Support Vector Machine; Support vector machines; Time Factors; Training; Young Adult; Brain-computer interface; P300 speller; training set extension; SVM ensemble; familiar face paradigm
• ISSN: 0928-7329; 1878-7401; 1878-7401
• DOI: 10.3233/THC-171074

BACKGROUND: 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. OBJECTIVE: This study aimed to develop a method for acquiring more training data based on a collected small training set. METHODS: 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. RESULTS: 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. CONCLUSION: 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.