1. The Thought Translation device
A psychophysiological system for detection of cognitive functioning of the human brain.

2. What is an EEG?
An electroencephalogram is a measure of the brain's voltage fluctuations as detected from scalp electrodes.
It is an approximation of the cumulative electrical activity of neurons.

3. Electrodes Used
EEG electrodes are small metal plates that are attached to the scalp using a conducting electrode gel. They can be made from various materials. Most frequently, tin (Sn) and silver/silver-chloride (Ag/AgCl) electrodes are used but there are gold (Au) and platinum (Pt) electrodes as well.
An EEG amplifier measure voltage difference between points on the scalp. This implies that each channel is connected to two electrodes. Usually, measurement is "unipolar" rather than "bipolar", which means that the second electrode is identical for all channels, and called "reference" (Ref). Also, amplifier inputs must be kept within a small voltage range relative to the amplifier's zero (ground) voltage level.

4. Placement of electrodes

5. Brain Computer Interfaces - Brain / Cortex Topography:
Sensomototic humunculus: (top) frontal lobe, gyrus precentralis

6. Brain Computer Interfaces - Brain / Cortex Topography:

7. Brain Computer Interfaces - Brain / Cortex Topography:
Right and left brain map

8. Other Components
A single program (BCI-2000) which runs on all MS versions, performs the task of EEG-acquisition, storage,signal processing,classification and various BCI applications.
TTD software is connected to certain EEG-amplifier systems, with a time constant of 16s is then connected to an A/D converter(PCIM-DAS 1602/16).
An interface to an MRI-compatible 16/32 channel EEG amplifier allows TTD to be used in functional magnetic resonance imaging (fMRI).

9. Brain Computer Interfaces - BCI2000
● Research Platform for BCI Systems
● Written by Gerwin Schalk, Wadsworth Center, Albany (NY)
● Modular structure: Signal Aquisition, Signal Processing
and User Application communicatie via TCP/IP
● Operator module used for configuration of the other modules
● various user tasks availbale: 1D/2D cursor, Speller, P300, SCP
● free for academic use
● driver for OpenEEG available

10. Brain-Computer-Interface (BCI) ?
“A system for controlling a device e.g. computer, wheelchair or a neuroprothesis by human intention which does not depend on the brain’s normal output pathways of peripheral nerves and muscles”
TYPES:
Dependent BCI ( depends on activity in output pathways)
Independent BCI ( does not depend on any activity)

11. The 6 x 6 matrix speller, single character flash
concentrate on „W“
Individual character intensifies for 60ms with 10ms between each intensification A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 1 2 3 4 5 6 7 8 9

12. The 6 x 6 matrix speller, single character flashB C D E F G H I J K L M N O P Q R S T U V W X Y Z 1 2 3 4 5 6 7 8 9

13. The 6 x 6 matrix speller, single character flashB C D E F G H I J K L M N O P Q R S T U V W X Y Z 1 2 3 4 5 6 7 8 9

14. The 6 x 6 matrix speller, single character flashB C D E F G H I J K L M N O P Q R S T U V W X Y Z 1 2 3 4 5 6 7 8 9

15. The 6 x 6 matrix speller, single character flashB C D E F G H I J K L M N O P Q R S T U V W X Y Z 1 2 3 4 5 6 7 8 9

16. Target:15 µV Non-target: 1 µV
The 6 x 6 matrix speller, single character flash
Letter W Presentation
0.1
0.2
0.3
0.4
0.5
0.6
0.7
-10 -8 -6 -4 -2 2 4 6 8 10
time [s]
[µV]
P300
Target:15 µV
Non-target: 1 µV
NON Target
Target

17. Block diagram of a BCI

18. Feature Extraction Event related data triggering:
100ms pre-stimulus, 700ms post-stimulus
Baseline correction was performed for pre-stimulus interval
Downsampling (15 features/channel * 8 channel)
Data segments were concatenated by channel
Assume 5 flashes were selected for training, 3 letter word e.g. BCI single character mode: 36*5 = 180 flashes * 3 repetitions
540 trials, 15 target trials, 525 non target trials
Feature matrix 180*120 -> LDA

19. Accuracy depends on letters used for classifier training
3 training characters
42 training characters

20. Transfer rates calculation
The bit rate R in bits/min is given by
where N is the number of possible selections, P is the accuracy
probability and M is the average number of decisions per minute
Average transfer rates of all subjects

21. Translation Algorithm
Changes independent variables (signal features) into dependent variables (device commands). This is done using linear or non-linear methods.
Effective algorithms adapt to user on 3 levels:
When a new user first accesses the BCI, the algorithm adapts to that users signal features.
Periodic online adjustments to reduce the impact of spontaneous
variations such as hormonal levels, temperature, fatigue, illness.
Dependence on the effective interaction between two adaptive controllers, the brain and the BCI.

22. Brain Computer Interfaces
Principles of operation:

23. Brain Computer Interfaces – Major Types
● SCP (Slow Cortical Potentials)
● Mu and beta rhythms (Movement Imagination)
● P300 signals
● cortical neurons,
and Visual envoke potentials (VEP)
The control information
is extracted from the
real time EEG-recording

24. Brain Computer Interfaces – SSVEP
● Steady State Visual Evoked Potentials
derived from the visual (occipital) cortex
● focussing attention to visual stimuli of different frequency
shows up in the EEG freqeuncy bands
● relibable and high transfer rate, but some prerequisites
semirec/semi-Reilly/

25. Brain Computer Interfaces – SCP BCIs
● detection of slow cortical potentials (SCPs)
● needs DC EEG Amplifiers (no highpass filter)
● Niel Birbaumers: Thought translation device
intensive training with necessary to gain
control over the SCP waves
Patinet using TTD to write a letter

26. Brain Computer Interfaces - μ-rhythm BCIs
● μ–rhythm is the idle-rhythm of the motor cortex
● frequencies around 10 and 18 Hz, location : gyrus praecentralis
individual differences -> multichannel EEG (QEEG) for offline analysis
● ERD / ERS – event related desynchronisation / synchronisation
movements or imagination of movements inhibit the μ–rhythm
ERD/ERS at around 10, 22 Hz
Berlin-BCI,

27. Brain Computer Interfaces - μ-rhythm BCIs
● two dimensional cursor control using different frequency bands
for vertical horizontal movements (Wadsworth BCI)
● control of an orthesis, adaptive algorithm (Graz BCI)
CSA of Mu-rhythms,
Wadsworth BCI, 2 dimensional control Graz BCI, orthesis

28. Brain Computer Interfaces - P300 BCIs
● P300 wave – posivite component in
the event related potential, 300ms
after a stimulus
● natural response to events considered
as important
● selection of a symbol: count the flashes,
algorithm averages trails and finds a P300
P300 runtime user interface

29. Brain Computer Interfaces - μ / P300 comparison
μ - BCIs P300 BCIs
Require training do not require training
Work in realtime require averaging
2d-control possible D control only
Continous control discrete control
movement imagination concentration / decision
affected by movement affected by distraction

30. Brain Computer Interfaces - direct brain interfaces
● Electrocorticogram:
implanted electrode array
● better signal quality , increased SNR
radio transmission of signals
● problems: decreasing signal quality,
risk of infection, invasive technique
Components of a DBI system,
P.R. Kennedy, R.A. Bakey, M.M. Moore,
1987, IEEE Trans Rehabil Eng. 8 (2):
ECoG electrode grid photo by
Gerwin Schalk (Wadsworth Center, Albany, USA),
Kai Miller, Jeff Ojemann (University of Washington)

31. Discussion
The performance of a BCI system can be measured in terms of:
Decision speed (how many seconds are required for one decision?)
1-10 seconds for one decision with P300
same for osciallatory, SSVEP and slow waves
Degrees of freedom (how many classes can be selected?)
motor imagery task: max. 3 – 4 classes possible
slow cortical shift: continuous feedback for one dimension (up-down)
steady-state evoked potentials: up to 12 keys (phone keyboard)
P300-spelling: e.g. 36 letters (6 x 6 matrix) or more
P300 allows better control of smart home

32. Control matrix for smart home
Select music

33. Goto specific position – The Beamer

34. Results
3 subjects 42 training characters
7 different control masks
3 runs with specific tasks with 15, 11 and 17 decisions (e.g. open the door, go to the living room, pick up the phone,...)
-> Accuracy depends on arrangement of characters, background,...

35. Some examples of BCI applications
Leeb et al., Computational Intelligence and Neuroscience, 2007 (doi: /2007/79642)

36. ten ways to improve BCIs:
Brain Computer Interfaces -
ten ways to improve BCIs:
● Better recording techniques
● Better understanding of EEG
● New brainwave parameters / hybrid BCI
● Customization of BCIs to each user
● Better pattern recognition
● Improved interfaces
● Better noise rejection
● Further testing with patients
● Studies on effects of training and long term use
● Improvements in computing and electronics
„BCI development is an interdisciplinary problem, involving neurobiology, psychology, engineering, methematics, computer science and clinical rehabilitation“

37. Bibliography
[1] Brendan Allison. Brain computer interface systems
[2] Niels Birbaumer et al. The thought translation device (ttd) for completely paralyzed patients.
IEEE Transactions on Rehabilitation Engineering, 8(2):190–193, 2000.
[3] Niels Birbaumer et al. Communication in paralysis and neurological disease.

38. Thank You!
A Seminar by: Achyuth S Chakravarthy