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Meme Framework

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The mind experiences and models experimenter [meme] framework, use non-evasive EEG technology to record and analyse information of user’s brain activity.

Allowing the configuration of specific test cases (experiments) based in visual, audio and another external stimuli, through sequences of image, sound and language, is possible searching for singular events into the datasets and apply models using machine learning algorithms for searching patterns. Identify user’s emotions, visualize and hear representations of our own thoughts, collaborate in the understanding of the brain and simply share knowledge are the main objectives of this framework.

Publié dans : Technologie, Formation
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Meme Framework

  1. 1. Mind Experiences and Models Experimenter Framework MEME
  2. 2. “Imagine something more mysterious than the trenches of the deep sea, more convoluted than the intricacies of the human genetic code, possibly even more infinite than the vastness of outer space...”
  3. 3. Question: What I think? Brain: “...an apparatus with wich we think we think”; the physical and chemical platform for the mind; energy, signals, thoughts... Phrenology Chart (1833)
  4. 4. “Thinking about Thinking” …really, google it: “what I think?”
  5. 5. Internet meme: Brain-to-Brain-Interface-like 
  6. 6. Limits of science http://en.wikipedia.org/wiki/Meme • The word meme is a shortening (modeled on gene) of mimeme and it was coined by the British evolutionary biologist Richard Dawkins in The Selfish Gene (1976) as a concept for discussion of evolutionary principles. • The meme, analogous to a gene, was conceived as a "unit of culture" (an idea, belief, pattern of behaviour, etc.) which is "hosted" in one or more individual minds, and which can reproduce itself, thereby jumping from mind to mind.
  7. 7. Cerebral cortex - functional map (each region with its specific computational role) Prof. Idan Segev, Coursera: Synapses, Neurons and Brains
  8. 8. Computing image correlation and binding different parts of the image (figure – ground separation) is essential for the organism Prof. Idan Segev, Coursera: Synapses, Neurons and Brains “Is intelligence the goal of a biological evolution?” How to create mind, Ray Kurzweill
  9. 9. • Cognitive • Reason (make judgment under uncertainly) • Consciousness • Represent knowledge (also commonsense) • Plan • Learn (critical to human intelligence) • Communicate (natural language) • Self-awareness, Sentience, Sapience... Hypothesis: How I think? http://en.wikipedia.org/wiki/Cognitive_science • Affective • Emotions • Conative • Natural tendency?, impulse…
  10. 10. mind loop: Sensation > Perception > Action > Emotion > • The transformation of external events into neural activity; •Processing of sensory information; we believe that the end result is a useful representation in terms of the external objects that produced the sensations; •Organisms use the representation of the world in order to act on it, optimizing rewards and minimizing punishments; •Emotion is often the driving force behind motivation, positive or negative. Our Neuronal Processing Mechanism
  11. 11. EEG, Sensors, Inputs
  12. 12. Dataset scales (hbp) http://www.humanbrainproject.eu/
  13. 13. MEME dataset scales • Spatial = S (Structure) • S = Brain Region Area (cm) = # Sensors EEG selected • Energy = E (Efficiency) • E = reflects the summation of the synchronous activity of thousands or millions of neurons that have similar spatial orientation = microVolts (mV) [2..100mV] • Time = T (Function) • T = seconds (sec); • Analysis type: continuous || steady-state • S+E+T = inputs variables to build, run and validate regression and classification models (machine learning)
  14. 14. MEME loop Sensation Perception Action Emotion mind experiences model experimenter • Signal acquisition from EEG sensors (live or recorded in EDF format) with “events marks” (M) regarding the parameters of the experience configured or manually sent by the user; • Run machine learning models using the inputs (S+E+T) and predicting the output (M) ; • Using an event manager, any time that the model predict inputs values associated with a specific mark associated with the experience, will be triggered a command to could interact with other systems; • Implementation of OCC Model.
  15. 15. • build/train/validate machine learning models • Nearest neighbor classifiers, linear classifiers, nonlinear Bayesian classifiers, neural networks and some combination of classifiers (soon, markov models); • design/edit/play mind experience • setup/validate/run models experimenter; • add manually marks at recorded experiences to measure stimulus from other senses (e.g. taste, external events). MEME features
  16. 16. MEME Framework Components BCI Sensor LayerBCI Sensor LayerBCI Sensor LayerBCI Sensor Layer Emotiv EPOC SDKEmotiv EPOC SDKEmotiv EPOC SDKEmotiv EPOC SDK Core LayerCore LayerCore LayerCore Layer Model ExperimenterModel ExperimenterModel ExperimenterModel Experimenter Mind ExperiencesMind ExperiencesMind ExperiencesMind Experiences SignalAcquisitionAdapterSignalAcquisitionAdapterSignalAcquisitionAdapterSignalAcquisitionAdapter SignalProcessingManagerSignalProcessingManagerSignalProcessingManagerSignalProcessingManager Application Interface LayerApplication Interface LayerApplication Interface LayerApplication Interface Layer FramesUIFramesUIFramesUIFramesUI UserProfileManagerUserProfileManagerUserProfileManagerUserProfileManager ExperiencesManagerExperiencesManagerExperiencesManagerExperiencesManager EventsManagerEventsManagerEventsManagerEventsManager ModelManagerModelManagerModelManagerModelManager MindWave MobileMindWave MobileMindWave MobileMindWave Mobile TemplatesFactoryTemplatesFactoryTemplatesFactoryTemplatesFactory
  17. 17. Next steps... •“When gamified, crowdsourced science is more than expediting data collection and analysis–it helps communicate science with the world” (e.g. www.eyewire.org)
  18. 18. Questions???