The document explores the relationship between mind experiences and experimental frameworks, emphasizing the concept of memes as cultural units that influence cognitive processes. It discusses various components of brain-computer interaction (BCI) and machine learning models for analyzing neural data, highlighting the integration of emotions and cognition in understanding human intelligence. Additionally, it suggests the potential of gamified science to enhance communication and data collection in research.

1. Mind Experiences and
Models Experimenter
Framework
MEME

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. 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. “Thinking about Thinking”
…really, google it: “what I think?”

5. Internet meme:
Brain-to-Brain-Interface-like 

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. Cerebral cortex - functional map
(each region with its specific computational role)
Prof. Idan Segev, Coursera:
Synapses, Neurons and Brains

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. • 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. 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. EEG, Sensors, Inputs

12. Dataset scales (hbp)
http://www.humanbrainproject.eu/

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. 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. • 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. 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. 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. Questions???