NeuroPhone : Brain-Mobile Phone Interface using a Wireless EEG Headset

NeuroPhone: Brain-Mobile Phone Interfaceusing a Wireless EEG Headset Source: MobiHeld 2010 Presented By: Corey Campbell

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

BRAIN-MOBILE PHONE INTERFACE • Mobile apps can be reinvented • Driving example • Many-to-One apps • Teacher – Student example • Possibility of Group Emotional State • Meeting example • Happy • Sad • Bored • Hostile

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

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

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

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

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

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

DESIGN CONSIDERATIONS • Signal to Noise Ratio (SNR) • Lots of noise on every electrode • Bandpass filtering • Average multiple trials of data • Signal Processing • Bandpass filtering

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

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

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 averaging provides better detection accuracies

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

NeuroPhone : Brain-Mobile Phone Interface using a Wireless EEG Headset