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Brain-Computer Interfaces


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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 ( The talk was given Jun. 8, 2007.

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Brain-Computer Interfaces

  1. 1. An overview of Brain-Computer Interface Emtiyaz
  2. 2. Brain Computer Interface
  3. 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 and
  4. 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
  5. 5. 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
  6. 6. P300 BCI Video : Spelling devices with P300 Photographs from Bayliss’s thesis 2000
  7. 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
  8. 8. Why Movements related imaginations? Photographs from Kendel 1991
  9. 9. ERD/ERS BCI Experiments Training sessions Testing sessions Photographs from Pfurtscheller 1999
  10. 10. Inter-trial Variance (IV) Method Photographs from Pfurtscheller 1998
  11. 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. 12. Direct Brain Interface Head of Florian D. Photographs from