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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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Presentation on theme: "NeuroPhone: Brain-Mobile Phone Interface using a Wireless EEG Headset Source: MobiHeld 2010 Presented By: Corey Campbell."— Presentation transcript:

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

2 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

3 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

4 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

5 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

6 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

7 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

8 WIRELESS EEG HEADSET Emotiv EPOC headset – 14 data-collecting electrodes – 2 reference electrodes – International system config. Transmits encrypted data Windows-based 2.4Ghz frequency range

9 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

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

11 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

12 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

13 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

14 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


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