brain signal seminar

1. Integrating Language Models into
classifiers for BCI communication :
a review
W Speier, C Arnold and N Pouratian.
6 May, 2016
PRESENTED BY
NAFIZ ISHTIAQUE AHMED
UOU- UNIVERSITY OF ULSAN.

2. Brain Computer interface(BCI) communication
1. Brain Computer interface is a participation between a
brain and a device that enables signals to brain.
2. BCI communication system using language model in
classifier to progress : word completion; signal
classification; integration of process models;
dynamic stopping; unsupervised learning; error
correction; and evaluation.
3. Brain computer interface system meets the needs of
the amyotrophic lateral sclerosis (ALS) population

3. Introduction
o BCI communication worked for disable patients who have motor neuron disorders such as
amyotrophic lateral sclerosis (ASL).
o Assistive communication technology help patients by providing indirect communication method
based on eye tracking.
o BCI system can restore ‘Locked-in’ patients ability and identifying intent from
electroencephalogram(EEG) signals.
o And translating them into computer commands.

4. BCI communication
BCI communication
system have been
established several
communication
system for ‘locked- in’
patients-
P300 speller.
Rapid serial visual presentation(RSVP).
Auditory multi-class spatial ERP(AMUSE).
& Steady State Visually evoked potentials(SSVEP).

5. P300 Speller

6. Rapid serial visual presentation(RSVP) speller :
• This system elicits P300 signal by serially presenting visual stimuli in a graphical interface.
• This system is slower than the P300 speller because showing characters one at a time
requires more time to display all possibilities.
• Advantage of being gaze-independent.
Rapid serial visual presentation(RSVP)

7. Auditory multi-class spatial ERP (AMUSE) system
• AMUSE present the user with a series of distinct auditory stimuli.
• These stimuli vary based on pitch and or location, with each combination assigned to a
specific character.
• Need sufficient training that user could learn the associations and the system could
function without a visual interface.
• Eliminate any dependence on eye gaze.

8. Steady State Visually evoked potentials(SSVEP)

9. Scope of the review
• BCI classifiers has great potentials as users who have difficulty typing with a communication system
stand to gain the most from language integration.
• This review focuses on how language information can be used by classifier to improve the speed and
accuracy of decision making when classifying neural signals for BCI communication .
• Compared with others BCI communication system and improve system day by day.

10. Enhance BCI communication
Identified & discuss seven different domains in which languages models have been used to
enhance BCI communication :
1. Word completion.
2. Signal classification.
3. Integration of process models.
4. Dynamic Stopping.
5. Unsupervised Learning.
6. Error correction
7. Evaluation metrics.

11. Conclusion
 BCI-Brain computer interface is to replace or restore useful function to people
disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral
palsy, stroke, or spinal cord injury.
 Brain-computer interfaces may also prove useful for rehabilitation after stroke and for
other disorders