Paper: Classifying Single Trial EEG: Towards BCI

Classifying Single Trial EEG: Towards BCI

• single trial is difficult for signal processing and machine learning
• This study a pseudo-online evaluation of single-trial EEGs from voluntary self-paced finger movements and exploit the laterality of the left/right hand signal as one bit of information for later control
• Two key issues to start with when conceiving a BCI are
  
• (1) the definition of a behavioral context in which a subject’s brain signals will be monitored and used eventually as surrogate for a bodily, e.g., manual, input of computer commands, and
  
• (2) the choice of brain signals which are optimally capable to
  convey the subject’s intention to the computer.
• Types of experiments: two alternative force reaction VS endogeneous (natural reaction)
• a negative ›Bereitschaftspotential‹ (BP) precedes the voluntary initiation of the movement.
• A differential scalp potential distribution can be reliably demonstrated in a majority of experimental subjects with larger BP at lateral scalp positions (C3, C4) positioned over the left or right hemispherical primary motor cortex, respectively, consistenly correlating with the performing (right or left) hand
• While it is true thatbrain potentials comparable to BP are associated with an imagination of hand movements, which indeed is consistent with the assumption that the primary motor cortex is active with motor imagery, actual motor performance significantly increased these potentials
• Reason 4actually perform the typewriting finger movements, rather than to merely imagine their performance, for two reasons:
• (1)this will increase the BP signal strength optimising the signal-to-noise ratio in BCI-related single trial analyses; and
  
• (2)we propose that it is important for the subject’s task efficiency not to be engaged in an unnatural condition where, in addition to the preparation of a
  motor command, a second task, i.e., to ›veto‹ the very same movement, has to be executed.
  
• ADD SECTION: slow cortical potentials (SCP) and how they can be self-regulated in a feedback scenario
• Typical preprocessing techniques are adaptive autoregressive parameters, common spatial patterns (after band pass filtering) and band power in subject specific frequency bands. Classification is done by Fisher discriminant analysis, multi-layer neural networks or LVQ variants
• Linear methods (pg 4): Fisher Discriminant, Regular Fisher Discriminant, Sparse Fisher Discriminant, Support Vector Machine, K-Nearest Neighbor, Classification of response-aligned windows, Classificationinslidingwindows, Movementdetectionandpseudo-onlineclassification,
  
• Conclusion, High classification rate with low errors