An overview of Brain-Computer Interface

Document Sample
An overview of Brain-Computer Interface Powered By Docstoc
					An overview of
Brain-Computer Interface

       Brain Computer Interface
ElectroEncephaloGram (EEG)
• Hans Berger (1929)
    – It is out of the question that the α-w and β-w of my EEG exert any effect at a
      distance; they can not be transmitted through space. Upon the advice of
      experienced electrophysicists, I refrained from any attempt to observe possible
      distant effects.

  δ (0.1 to 3 Hz)           θ (4-8 Hz)               α (8-12 Hz)             β(above 12 Hz)

Photographs and
Why BCI?
•   Patients with neuromuscular disorders
      – ALS, multiple sclerosis
•   Solutions
      – Use the capabilities of remaining pathways
      – Detour around the points of damage
      – Provide the brain with new channels for communication control

Photographs Bayliss’ thesis 2000 and Pfurtscheller
 A general Brain-Computer Interface

Invasive/ Non-invasive

Design of Experiments

Different Features                    Photographs from McFarland 2002

Not reading thoughts, rather enforce subjects to certain
mental states which can be recognized by the machine
P300 BCI

       Video : Spelling devices with P300

                    Photographs from
                                    Bayliss’s thesis 2000
   ERD and ERS
    Event Related Desynchronization /Synchronization is an
    amplitude attenuation/ enhancement in the specific
    frequency bands associated with an event.

                                        Frequency dependent

Left right

                                         Photographs from Pfurscheller 2002
Why Movements related imaginations?

                         Photographs from Kendel 1991
ERD/ERS BCI Experiments
                     Training sessions

  Testing sessions
                          Photographs from Pfurtscheller 1999
Inter-trial Variance (IV) Method

                            Photographs from Pfurtscheller 1998
RLS Approach
                                               observation noise
Adaptive Autoregressive (AAR) Model

Solved with Recursive least square (RLS) algorithms
and features classified with Linear Discriminant
Analysis (LDA)

                                       Photographs from Pfurtscheller 2000
Direct Brain Interface
                                                   Head of Florian D.

                 Photographs from