Paper: Towards adaptive classification for BCI

A Tutorial?

• Purpose of the paper:
• Non-stationarities are ubiquitous in EEG signals.
• (a) in the differences between the initial calibration measurement and the online operation of a BCI,
  
• (b) caused by changes in the subject’s brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc)
• we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions.
• we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-)stationarities
• Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session.
• we propose several adaptive classification schemes and study their performance on
  data recorded during online experiments.
• Classification methods
• ORIG: this is the unmodified classifier trained on data from the offline scenario and serves as a baseline.
  
• REBIAS:we use the continuous output of the unmodified classifier and shift the output by an amount that would minimize the error on the labeled feedback data.
  
• RETRAIN:we use the features as chosen from the offline scenario, but retrain the LDA classifier to choose the hyper plane that minimizes the error on labeled feedback data.
  
• RECSP:we completely ignore the offline training data and perform CSP feature selection and classification training solely on the feedback data.
  
• Types and controls
• (1) all the labeled online data up to the current point (cumulative),
  
• (2) only a window over the immediate past (moving), or
  
• (3) only an initial window of data from each session(initial).
  
• We thus have C-REBIAS7, C-RETRAIN and C-RECSP, W-REBIAS, W-RETRAIN and W-RECSP, and I-REBIAS, I-RETRAIN and I-RECSP, respectively, for the three cases considered.