title: The predictive role of pre-cue EEG rhythms on MI-based BCI classification performance.
abstract: BACKGROUND
  One of the main issues in motor imagery-based (MI-based) brain-computer interface (BCI) systems is a large variation in the classification performance of BCI users. However, the exact reason of low performance of some users is still under investigation. Having some prior knowledge about the performance of users may be helpful in understanding possible reasons of performance variations.
  
  
  METHODS
  In this study a novel coefficient from pre-cue EEG rhythms is proposed. The proposed coefficient is computed from the spectral power of pre-cue EEG data for specific rhythms over different regions of the brain. The feasibility of predicting the classification performance of the MI-based BCI users from the proposed coefficient is investigated.
  
  
  RESULTS
  Group level analysis on N=17 healthy subjects showed that there is a significant correlation r=0.53 (p=0.02) between the proposed coefficient and the cross-validation accuracies of the subjects in performing MI. The results showed that subjects with higher cross-validation accuracies have yielded significantly higher values of the proposed coefficient and vice versa.
  
  
  METHODS
  In comparison with other previous predictors, this coefficient captures spatial information from the brain in addition to spectral information.
  
  
  CONCLUSIONS
  The result of using the proposed coefficient suggests that having higher frontal theta and lower posterior alpha prior to performing MI may enhance the BCI classification performance. This finding reveals prospect of designing a novel experiment to prepare the user towards improved motor imagery performance.
authors: Institute for Infocomm Research (I(2)R), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01 Connexis, Singapore 138632, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore. Electronic address: stuab@i2r.a-star.edu.sg.
authors: Atieh
authors: A
authors: Bamdadian
authors: Institute for Infocomm Research (I(2)R), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01 Connexis, Singapore 138632, Singapore. Electronic address: ctguan@i2r.a-star.edu.sg.
authors: Cuntai
authors: C
authors: Guan
authors: Institute for Infocomm Research (I(2)R), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01 Connexis, Singapore 138632, Singapore. Electronic address: kkang@i2r.a-star.edu.sg.
authors: Kai Keng
authors: KK
authors: Ang
authors: Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore. Electronic address: elexujx@nus.edu.sg.
authors: Jianxin
authors: J
authors: Xu
mesh_terms: D000513:Alpha Rhythm; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D003463:Cues; D004569:Electroencephalography / Q000379:methods*; D005240:Feasibility Studies; D006801:Humans; D007092:Imagination / Q000502:physiology*; D009043:Motor Activity / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D013826:Theta Rhythm; D014796:Visual Perception / Q000502:physiology
keywords: Brain–computer interface; EEG rhythms; Motor imagery; Performance prediction