All posts by julie

Treading Waves Final – Julie & Ayo

Treading Waves Final – Julie & Ayo



The goal of this project is to create a set-up to construct emotional signifiers using visual stimulus.  The first model will be using the example of the emotion disgust.

Our conceptual foundation for this project is based on several psychological and neurological experiments from brain wave monitoring to early experiments in artificial intelligence. For example the works of Alan Turing and subsequently the Turning Normalizing Machine  ( influenced us greatly to create emotional signifiers that could one day be mapped to artificial intelligence in the future.

We were also inspired by the mindflex homework assignment where a video clip from the movie Clockwork Orange was distorted when using the attention and meditation values while the viewer watched an elicit clip.



The Turing Test

Turing Normalizing Machine

 Use of Visual Stimulus in Clockwork Orange



We plan to use the NeuroSky device to read raw data from the subjects brain. By subjecting the subject to various visual stimulus we expect to be able to measure their rest, excitement, and disgust levels. Each subject will be subjected to a series of visual cues, by monitoring several subjects we expect to get a general (normalized) understanding of the average rest, excitement, and disgust levels associated with the cues presented.


We would run the experiment in this order:

1.  Image to pick up signal


2. Image of positive food


3. Blank Image


4. Image of negative food





Mock-up of Testing Structure

IMG_2173_ IMG_2176_IMG_2172_IMG_2169_

Testing In Progress



Using the Thinkgear library created by Akira Hayasaka, we edited the library where the data parses out the different channels, and parsed out every channel available.  Then we kept the tweenlite addon that smooths out the jumpiness of the data influx and translated the data into a polyline graph for the viewers. Each line below represents raw, attention, meditation, delta, theta, high alpha, low alpha, high beta, low beta, high gamma and low gamma.

Attention and meditation are the yellow and blue lines, and raw is the lime green line running along the top.

The delta, theta, high and low alpha, beta, and gamma waves are the displayed below, with higher and more abstract numbers.

Screen shot 2014-05-15 at 4.53.09 PM Screen shot 2014-05-15 at 4.53.11 PM Screen shot 2014-05-15 at 4.55.55 PM Screen shot 2014-05-15 at 4.55.56 PM  Screen shot 2014-05-15 at 4.56.05 PM

Screen shot 2014-05-15 at 4.55.59 PM

We plan to capture the data at each stimulus point to analyze the differences from control state to stimulated state.



Link to our openFramesworks sketch for displaying and recording data: Treading Waves Graph



The following list elucidates the challenges we encountered or will expect to encounter

  1. Syncing video cues to brain wave data recording

  2. Parsing the quantity of data

  3. Analyzing the RAW data values

  4. Attributing RAW data values to significant emotional states

  5. Desensitization of subjects

  6. Eliminating errant data resulting from muscular activity



In our future tests, we will run the experiment using real food as a variable, and also smell exclusively as another variable.



Treading Waves Power Point

Final Proposal – Ayo + Julie


The Turing Normalizing Machine


To create a new classifier such as “surprise” we would need to get a baseline of raw EEG data. We will then introduce a the subject to a surprising stimulus and measure the difference in raw EEG data. We would need to repeat the process several times and have an average of the surprise stimulus data to be confident in the results. The problem here would be desensitisation to the stimulus. Over time the subject will be aware of the inducement and may fail to provide the appropriate surprise data. To counter this issue of desensitisation we would use multiple subjects. In addition to solving the desensitisation issue, we generate a more universal notion of the emotional state / classifier we are hoping to achieve. By analysing the patterns emerging from multiple surprise data response from different people, we can start to create a module for understanding a normalized physiological response based on a particular stimulus.

Brainstorm of ideas:

–       baseline of surprise

–       use a sweat sensor and the thinkgear

–       raw eeg?

–       surprise by playing a video

–       measure heart rate

–       emotional state that can be repeatedly tested

–       emotions to consider: anger, fear, nervous, agitation(annoyance), sad, apathy, empathy, disgust

–       have ppl look at optical illusions as stimulus?

–       trigger: disgust, primal instinct, visceral, elicit instinct, fear

–       map body’s physical chemical reaction with brainwave data

–       use a person without stimulus as control

–      test more ppl with stimulus

–       possibly use brainwaves of other beings besides humans

–       And It gives a universal response

–       Stimulus, sound and video

–       creating a new classifier, problem of being desensitized

–       take difference btwn all the testers and come up with an average

Chapter 3 Reading Response – Julie Huynh

How to Create a Mind: Chapter 3 Response

I think Kurzweil’s thesis poses a strong theory on how the brain processes pattern recognition.  Memories such as deja-vu fit into this theory where although you cannot specifically place the direct connection, the brain confabulates a connection triggered by undirected thoughts.  The neocortex confabulates a connection caused by the trigger of undirected thoughts, such as a smell could trigger a familiar memory from the past.  Dreams are examples of undirected thoughts that are linked with direct thoughts, which are a sequence of patterns.  However, our dreams, lucid dreaming fit outside of this theory because our brain is in a state where it can be lax on the cultural rules enforced by the neocortex, and it can explore consequences of actions without the repercussions and triggering the fear from the amygdala.  Interpretation of the pattern input of dreams would give us a peak into understanding unresolved fears and desires, but I think a different system would be necessary to understanding dream patterns.  The PRTM thesis relates to artificial intelligence because by emulating these pattern recognizers and the system that house them we could create artificial intelligence that could recognize speech, or in the future process information for us.  By creating programs to recognize phonemes, recognizing the spoken word using hierarchical hidden Markov models will incorporate the distribution of magnitudes of each input, on the speech level, this will computationally simulate human intelligence.  Starting with the simulation of speech recognition we can take the next step towards artificial intelligence with a cortical algorithm in the temporal of constituent lists.

MindFlex Assignment – Julie & Ayo

Title: Mind Bender


Our concept was to use a movie clip from the movie Clockwork Orange where they are brainwashing the protagonist.  We thought it would be a comical parody to reference brainwashing in a 1971 science fiction movie that comments on brainwave technology.  Our goal was to set the attention variable to a red overlay on the playing movie, so that it translates to the more attention you give to the movie it evokes red which is a color that connotes anger and violence.  The more attention you give the movie the images are distorted by the red overlay, so it censors the playing movie based on your attention level.  Then we wanted to control the running frame rate of the movie with the meditation level, so the movie would play faster or slower based on how meditative the user is while watching the movie.  The attention and meditation creates a cyclical interaction of the movie with the viewer by using those variables to control, but also be effected by the viewer’s reaction to the playing movie clip.

However, we experienced technical difficulties with receiving a serial read for attention and meditation, so we substituted those values with High Beta and High Alpha.  We used the ofMap(); function to map those numbers to be translatable for the color tint and frame rate by inputting the lowest and highest values for those variables and mapped it to the color tint, and to the frame rate.


Effects of the red overlay on movie:




Final Presentation

Final Peculiar Patterns Presentation Link

Final Prototype:

3-D Sketches of New Prototype:






We decided to fabricate our test receptacle out of acrylic sheets by creating clear tubes to hold the copper sulfate and then a black tray to hold the clear test tubes to see the chemical reaction.  We choose black acrylic for the tray to frame the viewing of the test tubes using contrast.


Custom Laser-Cut Receptacle



Fabricated Acrylic Test Tubes


Test Tubes in Black Acrylic Tray






Etching Solution for Custom Circuit


Custom Circuit

We created a circuitry with switches and transistors, the switch allowed the device to switch between a learning and memory state.  We set the voltage to 3V, so the chemical reaction does not happen too quickly to observe the reaction over time.  In the future, we will set add a “demo” state to increase our voltage to display the immediate reaction with a higher voltage.


The learning state involves the electrolysis and the depositing of copper on the contacts to form a connection of copper over time.

The memory state used a current that passes through the anode and the test electrode.  If there is a connection between the anode and the test electrode then an LED is lit.


Prototype on LED Platform



Prototype with Light Sensor Trigger on LED Platform

Final Conclusion:

We concluded that our tests were more effective with creating specific controls with our custom receptacle.  We would like to add improvements for the next version of our receptacle designed with better viewing of the test tubes.  Also we will add a “demo” state to increase the voltage to demonstrate an immediate chemical reaction.  We will create a new design with a better light sensor to redirect the light that will effect the learning state, and make a unique memory deposit based on the analog lighting changes.

Midterm Inspiration – Ayo & Julie


We were inspired by the Star Trek memory pack. Storing a network in a gel solution. It made us want to create an organic Arduino that could store patterns that we could program it and then create new patterns.

Neuromorphic Atomic Switch Networks : Adam Z. Steig’s work on self organization of natural systems.

Our idea is to create a programmable organic system.  As biological systems have interconnections created with chemical bonds, we were interested in controlling these bonds to create a patterned system that we could manipulate and program.


The brainbow shows all the electrical interconnections that create an organic program.  We want to map neuron networks as in the brainbow, as they did in the brainbow. They used green florescent proteins to see the electric neuron connections of a mouse’s brain, so this inspired us and help understand the cellular connections between the neurons to better understand. Our project tried to mimic these connections using chemicals.

Midterm Update – Ayo & Julie

Updated Midterm Presentation Link

Tested theory:

Hypothesis: Using electrolysis and copper sulfate solution, we can create a learned hard wired connection, analogous to neural synapse, simulating the connection by repeating passing electricity through out system, over time deposits copper on the anode which connects the electrical gap.



Pictures of tests:

Procedure: We created a control substance with just copper sulfate and water, and tested various viscosities of gelatinous copper sulfate substrates.

We set up the circuit on a breadboard prototype, and the different substrates in an ice tray.


Tray set-up


Liquid Solution


Gel Solution


Copper build-up

Substrate over time

Copper deposit in substrate

Conclusion: Our test circuit worked with 2 instances of contacts fusing which equates to a successful learned pattern.  Gel substrate stuck to electrodes, possibly interfering with connections or gave false positives.

Failed tests

Copper sulfate substrate was too heavy in viscosity, so upon removal,  broke off the connection made.

Successful Tests

The connection was able to create a circuit to light the LED.



Future tests:

In our future tests, we will use an evaporative solution, so the gel substrate will not interfere with our tests.


Midterm Proposal – Ayo & Julie

Midterm Project Proposal Link


Chemo-neural Memory Pak

The aim of this project is to create a system that mimics neural networks using embodied computation. The system will utilize the method of electrolysis in aqueous and gelatinous copper sulphate solution to fuses electrodes, thus creating hard wired electrical contacts.

The project is designed to learn by creating a hard contact of copper deposited between electrodes.  Sensors will send patterns to the memory pak via transistors for amplification. The system will have two states. A learning state and a memory state. We will also utilize logic gates.

We will try to have the system remember patterns, and forget patterns via chemical processes. The remembered patterns will be utilized to stimulate output actuators.

Sensors will send patterns amplified by transistors to the memory pack.

We will try to have the system remember patterns, and forget patterns via chemical processes.

We will utilize aqueous copper sulphate as well a gelatinous copper sulphate in our process.

Zebrafish Eye:


Our idea is to biologically mimic the inner workings of a zebra fish’s eye. We plan to dissect a zebra fish and remove the eye to get a closer look at it, and possibly test sending currents through it. We want to make a physical computational representation of how the eye works, and use electrical currents to imitate the currents sent from the zebra fish’s eye to its brain when it recognizes images.  We want to create a representation of what happens when a zebra fish sees and processes an image, and recreate that with physical computer parts.