Paper: An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain–computer interface

• It is important to identify robust feature descriptions for BCI research, either in the time or frequency domain as derived from single-trial motor imagery (MI) EEG data
• Autoregressive models are commonly used in terms of model
  parameters without direct relation to specific brain activities
  (Anderson et al 1998, Pfurtscheller et al 1998).
• Time- domain features, such as activity, mobility and complexity, describing the properties of a signal trial have been used (Obermaier et al 2001).
  
• A direct ERD estimation calculating the energies within pieces of time windows imposed on
  EEGs at pre-selected frequency bands has been found to be a good feature in classifying right- or left-hand imagined movements (Pfurtscheller et al 1997).
  
• The primary drawback of scalp EEG is the spatial smearing
  and low signal-to-noise ratio.
• Since there are correlations among the recording electrodes, spatial filters have been used as means of accentuating localized activity and reducing diffused activity
• McFarland et al (1997) have compared the EEG classification results using different spatial filters and concluded that the common average and the Laplacian derivation yield good performance.
  
• Fifth order IIR Butterworth band-pass filters were used for
  frequency decomposition
• Two important timing cues that should be mentioned here are that at 3.75 s of a trial period,
  
• a cue (preparation cue) of one letter (L or R) appeared on the screen indicating which hand (specifically index finger) movement should be imagined;
  
• and at 5.0 s another cue (execution cue) appeared, indicating that it was time to make the requested response. The subjects were well trained to consistently respond to the cue signals within 100 ms.