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University of California, San Diego Brain – Computer Technology

University of California, San Diego Brain – Computer Technology. Brain-Computer Technology. Applications ACTIVE PASSIVE Interact with computers Biometrics “just by thinking”. Vision. Long term: A BCI platform. MEDICAL. GAMES. MILITARY. BCI. TRADING. ANTI-TERRORISM. MARKET

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University of California, San Diego Brain – Computer Technology

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  1. University of California, San DiegoBrain – Computer Technology

  2. Brain-Computer Technology Applications ACTIVE PASSIVE Interact with computers Biometrics “just by thinking”

  3. Vision Long term: A BCI platform MEDICAL GAMES MILITARY BCI TRADING ANTI-TERRORISM MARKET RESEARCH AIR TRAFFIC CONTROL ∞

  4. Near term Two initial markets ANTI-TERRORISM GAMES MILITARY MEDICAL B-C TRADING MARKET RESEARCH AIR TRAFFIC CONTROL ∞

  5. Seizure DetectionUsing Scalp EEG

  6. Goals • Detect EEG indices of pre-seizure states in real time • Maximize warning intervals • Integrate UCSD technologies with therapies that prevent seizure onset

  7. Current Techniques • Traditional (Linear) • Frequency Domain • 15-25 Hz burst • Time Domain • Number of interictal discharges (spikes and interspike intervals) • Energy • Variance

  8. Current Techniques • Non-linear • Synchronization • Fractal dimension • Lyapunov exponents • Correlation dimension • Similarity measures • Nonlinear predictability • Cross correlation integral

  9. Current technology has failed to produce reliable, prospective seizure prediction

  10. Problems • Non-stationarity • Parameter optimization • Single pre-ictal features • Noise • Environmental • Muscle/eye/body movements

  11. Our Solution • Integrated traditional and nonlinear approaches • nonparametric adaptive segmentation • Efficient real time algorithms • Intelligent pattern recognition • discriminate normal/abnormal brain activity • classify data • remove artifactual activity • maximize warning interval • Biofeedback therapy

  12. Proof of Concept Two methods have been employed to anticipate onset of a seizure: Method 1: Plot the variance of the EEG signal vs. time Method 2: Proprietary, non-linear analysis of EEG signal vs. time; onset is easily identified in a spectrographic analysis of this data

  13. Behavioral manifestations of seizure Earliest reliable prediction based on Method 1 Earliest reliable prediction Based on Method 2

  14. Spectographic analysis:

  15. Combine with Remedial Therapy • Biofeedback • Drugs • Brain Stimulation • Vagus Nerve Stimulation (VNS)

  16. Seizure Detection System Multichannel Com Feature Extraction Data Acquisition Interface Technology Pattern Recognition VNS Initiate a Variety of Therapies User Biofeedback

  17. Technical Milestones Completed Current Future

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