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NeuroPhone: Brain-Mobile Phone
Interface using a Wireless EEG Headset
Source: MobiHeld 2010
Presented By: Corey Campbell
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INTRODUCTION
A new way to use the mobile phone
Design and Evaluation of NeuroPhone.
EEG headset iPhone
Two different EEG signals to trigger action
Challenges involved
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BRAIN-MOBILE PHONE INTERFACE
Mobile apps can be reinvented
Driving example
Many-to-One apps
TeacherStudent example
Possibility of Group Emotional State
Meeting example
Happy
Sad
Bored
Hostile
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BRAIN-MOBILE PHONE INTERFACE (cont.)
Challenges regarding EEG headsets
Research-grade, hard-wired headsets
Offer more robust signal
Very expensive
Not mobile
Gaming headsets
Cost is cheaper
Encrypted wireless interface
More noise in signal
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BRAIN-MOBILE PHONE INTERFACE (cont.)
More challenges
Mobile phones not designed for continuous neural
sensing applications
Streaming neural info wirelessly and phone processing
Where do we use mobile phones, noisy?
Filtering out external noises
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NEUROPHONE DESIGN
App titled Dial Tim
Think & Wink modes
Contacts from iPhone address book
User concentrates on a person to call
P300 neural signal is the trigger
Wink mode uses a left or right wink to trigger
The P300 is subtle compared to a wink
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WHAT IS THE P300?
Focus on a person to call
When highlighted by app causes brain to
produce particular EEG signal Positive peak
300ms latency from onset of stimulus
Neuroscience uses this as P300
Other neural signals have potential
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WIRELESS EEG HEADSET
Emotiv EPOC headset
14 data-collecting electrodes
2 reference electrodes International 10-20 system config.
Transmits encrypted data
Windows-based
2.4Ghz frequency range
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WIRELESS EEG HEADSET (cont.)
Can detect facial expressions
Training then detection of activities
Push, pull, rotate, lift
Gyroscope
Headset not totally reliable
Challenge to extract finer P300 signals
Still, it is very useful and cost is cheap to
deploy on large scale
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DESIGN CONSIDERATIONS
Signal to Noise Ratio (SNR)
Lots of noise on every electrode
Bandpass filtering Average multiple trials of data
Signal Processing
Bandpass filtering
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DESIGN CONSIDERATIONS (cont.)
Phone Classifiers
Classification algorithms designed for powerful
machines
Algorithms not practical to run on mobile phones
Power efficiency
Resource issues
Resolving issues
Provide relevant subset of EEG channels
Use lightweight classifiers
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EVALUATION
Tested think and wink modes in various
scenarios
Sitting, walking, etc
Wink mode performance
Declines with really noisy data
Handles reasonably noisy data well
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EVALUATION (cont.)
Think mode performance
Accuracy is higher as more data is averaged
P300 signals susceptible to external noise Sitting still provides best results
Accuracy declines more when person stands up
More data accumulation and averagingprovides better detection accuracies
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EVALUATION (cont.)
Ongoing work
Usable P300 data from a single trial
Find new algorithms to handle extra noise iPhone app usage stats
CPU = 3.3%
Total memory = 9.40MB
9.14MB for GUI
Battery drain