Paper: Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis

• GREAT PATTERN REC PAPER!!!
• The required discrimination of different brain states may be based on evoked poten-
  tials (like steady-state visual evoked potentials or P300) a motor output, i.e., self-paced typewriting on a computer keyboard.
• This Paper
• (a) We exploit advanced machine learning and signal processing technol-
  ogy for single-trial EEG evaluation requiring no prior sub-
  ject training.
  
• (b) We use slow pre-movement potentials as
  physiological signals, and
  
• (c) we utilize a fast-paced experimental paradigm.
  
• "Let the machine learn" so they chose any every day task
• experiment (paradim)
  
• We let our sub jects (all without neurological deficit) make a binary (left/right hand) decision coupled to a motor output, i.e., self-paced typewriting on a computer
  keyboard.
• Using multi-channel scalp EEG recordings, we
  analyze the single-trial differential potential distributions
  of the Bereitschaftspotential (BP) preceding voluntary (left
  or right hand) finger movements over the corresponding
  (right/left) primary motor cortex.
• non-oscillatory event-related potentials (ERPs) two reasons:
• (physiological) if a bci is monitoring an idling rhythm then a prerequisite is stable presence of that rhythm. This could become a problem at a fast pace because the signal might not have the time to fully recover or idle again (my words pg.2)
• (data analysis) How to classify the noisy, high-demensional EEG data? The difference between task-related brain activity and non-task-related activity might not be enough (my words pg.2)
• Robust Preprocessing, Distro of ERPs, and Classification section
• 8 subjects
• Fisher Discriminant (again)
• GREAT PATTERN REC PAPER!!!
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