title: Combining multiple features for error detection and its application in brain-computer interface.
abstract: BACKGROUND
  Brain-computer interface (BCI) is an assistive technology that conveys users' intentions by decoding various brain activities and translating them into control commands, without the need of verbal instructions and/or physical interactions. However, errors existing in BCI systems affect their performance greatly, which in turn confines the development and application of BCI technology. It has been demonstrated viable to extract error potential from electroencephalography recordings.
  
  
  METHODS
  This study proposed a new approach of fusing multiple-channel features from temporal, spectral, and spatial domains through two times of dimensionality reduction based on neural network. 26 participants (13 males, mean age = 28.8 ± 5.4, range 20-37) took part in the study, who engaged in a P300 speller task spelling cued words from a 36-character matrix. In order to evaluate the generalization ability across subjects, the data from 16 participants were used for training and the rest for testing.
  
  
  RESULTS
  The total classification accuracy with combination of features is 76.7 %. The receiver operating characteristic (ROC) curve and area under ROC curve (AUC) further indicate the superior performance of the combination of features over any single features in error detection. The average AUC reaches 0.7818 with combined features, while 0.7270, 0.6376, 0.7330 with single temporal, spectral, and spatial features respectively.
  
  
  CONCLUSIONS
  The proposed method combining multiple-channel features from temporal, spectral, and spatial domain has better classification performance than any individual feature alone. It has good generalization ability across subject and provides a way of improving error detection, which could serve as promising feedbacks to promote the performance of BCI systems.
authors: School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, China. jijuntong@zstu.edu.cn.
authors: Jijun
authors: J
authors: Tong
authors: School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, China. lqg288@163.com.
authors: Qinguang
authors: Q
authors: Lin
authors: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA. ranxiao@ou.edu.
authors: Ran
authors: R
authors: Xiao
authors: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA. leiding@ou.edu.|Center for Biomedical Engineering, University of Oklahoma, Norman, OK, 73019, USA. leiding@ou.edu.
authors: Lei
authors: L
authors: Ding
mesh_terms: D000328:Adult; D019540:Area Under Curve; D062207:Brain-Computer Interfaces; D004566:Electrodes; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D009415:Nerve Net / Q000502:physiology; D012372:ROC Curve; D012107:Research Design; D012815:Signal Processing, Computer-Assisted; D062211:Spatio-Temporal Analysis; D055815:Young Adult