Brain-computer interfaces (BCI) allow communication between the brain and external devices using electroencephalography (EEG) to measure brain activity. BCIs can help patients with neuromuscular disorders by using remaining brain pathways to provide new channels for communication and control. Non-invasive BCIs use EEG electrodes placed on the scalp to detect patterns in frequency bands associated with events like movements to control devices. Invasive BCIs are implanted in the brain but non-invasive options avoid risks of surgery.
3. 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 http://www.crossroadsinstitute.org/eeg.html and http://www.cs.colostate.edu/eeg/index.html
4. 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
5. A general Brain-Computer Interface
Invasive/ Non-invasive
Design of Experiments
Different Features Photographs from McFarland et.al. 2002
Not reading thoughts, rather enforce subjects to certain
mental states which can be recognized by the machine
6. P300 BCI
Video : Spelling devices with P300
http://www.youtube.com/watch?v=NlUPFpZswJk
Photographs from http://www.gtec.at/products/g.BCIsys/bci.htm
Bayliss’s thesis 2000
7. 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
difference
Photographs from Pfurscheller 2002
11. 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
12. Direct Brain Interface
Head of Florian D.
Photographs from http://www.gtec.at/products/g.BCIsys/bci.htm