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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