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Aim of a brain-computer interface (BCI) is to provide a communication channel for paralyzed patients to interact with the outer world. I will start with the motivation behind brain-computer interfaces followed by description of a general BCI. I will then describe various kinds of BCI experiments and methods.From the Un-Distinguished Lecture Series (http://ws.cs.ubc.ca/~udls/). The talk was given Jun. 8, 2007.
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An overview of Brain-Computer Interface
Emtiyaz
Brain Computer Interfacehttp://www.youtube.com/watch?v=NIG47YgndP8
http://www.youtube.com/watch?v=qCSSBEXBCbY
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.
Photographs http://www.crossroadsinstitute.org/eeg.html and http://www.cs.colostate.edu/eeg/index.html
θ (4-8 Hz)δ (0.1 to 3 Hz) α (8-12 Hz) β(above 12 Hz)
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 et.al.2002
A general Brain-Computer Interface
Not reading thoughts, rather enforce subjects to certainmental states which can be recognized by the machine
Invasive/ Non-invasive
Different Features
Design of Experiments
Photographs from McFarland et.al. 2002
P300 BCI
Photographs from http://www.gtec.at/products/g.BCIsys/bci.htmBayliss’s thesis 2000
Video : Spelling devices with P300http://www.youtube.com/watch?v=NlUPFpZswJk
ERD and ERSEvent Related Desynchronization /Synchronization is an amplitude attenuation/ enhancement in the specific frequency bands associated with an event.
Frequency dependent
Left rightdifference
Photographs from Pfurscheller 2002
Why Movements related imaginations?
Photographs from Kendel 1991
ERD/ERS BCI ExperimentsTraining sessions
Testing sessions
Photographs from Pfurtscheller 1999
Inter-trial Variance (IV) Method
Photographs from Pfurtscheller 1998
RLS ApproachAdaptive Autoregressive (AAR) Model
Solved with Recursive least square (RLS) algorithms and features classified with Linear Discriminant Analysis (LDA)
observation noise
Photographs from Pfurtscheller 2000
Direct Brain Interface
Photographs from http://www.gtec.at/products/g.BCIsys/bci.htm
Head of Florian D.
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