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Pick a Card Project Update: Deploying Hardware for TinyML Model

Pick a Card Project Update

— Written by Nour Chahine —

Before I start discussing my updates, I have an announcement to make: I solemnly swear that I will solely be working with SSVEPs for the remainder of my project!

Over the past few weeks, I worked on feeding different forms of data into the neural network. I mainly applied two approaches: the power spectral density vector method and the notch filter knockout vector method. Both methods rely on quantifying the EEG response relative to the flashing frequencies of the cards.

Power Spectral Density Vector

The power spectral density vector method involves computing the power spectral density of the EEG signal, and then making a vector of the total power of the signal at each of the target frequencies.

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Bluff or No Bluff: Can a Neural Network Tell Them Apart?

Bluff or No Bluff - Sachin playing poker online

— Written by Sachin Pillai —

Hello to everyone out there! This is Sachin here again with updates on your very own portable poker bluff detector. I ended my last blog post mentioning how I developed the capability to collect electrodermal activity during full rounds of poker. This has allowed me to amass a large dataset of subjects playing poker. I am now nearing the end of my data collection/analysis phase and am close to producing a functional bluff detection method.

The past three weeks have consisted of lots of dead-ends, retracing, and fine-tuning. My initial approach to classifying bluff from neutral signals was to use the raw images of the galvanic skin responses and feed these graphs into the neural network. Just by judging these samples with my eye, I was able to somewhat distinguish between samples that contained a bluff versus those that did not. Data samples representing these two scenarios are shown below.

the difference in galvanic skin response 1
the difference in galvanic skin response 2

Both images represent electrodermal activity during a live poker session. Each image contains the GSR (galvanic skin response) and pulse response five seconds after either the flop, river, or turn has been uncovered. The image on the left is data collected during a neutral trial where the player was not disadvantaged by the cards on the table and did not decide to bluff. The image on the right shows when a player made a conscious decision to bluff after seeing the cards on the table. The orange line (SBChannel1) is the galvanic skin response while the green line (SB Channel 2) represents the pulse rate.

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I Couldn’t Replicate Readiness Potential. Here’s What I Did Instead

replicate readiness potential
Alpha waves loading!

— Written by Samuel Kuhn —

So, the summer is coming to an end, and I have faced off against a behemoth of a challenge. The readiness potential has proved elusive at every turn. I have tried different hardware, software, different electrode locations and experimental locations, different participants with different hair lengths, and yes, I even shaved my head to find this signal… no dice. Weeks and weeks of consistent effort have led me to only one clear conclusion: the readiness potential is NOT easy to detect in single trials.

The allure of this project from the outset seemed so clear to me: let’s try to make a device that can use the electrical activity in your brain to predict when you are going to move. The electrical activity of interest in this case was the readiness potential that Kornhuber, Deecke, and Libet, all world-renowned scientists, built their careers upon. All for one silly little squiggle with a bump in it!

replicate readiness potential
Source here

And even though this signal has a history longer than my own life, I have had no clear success in replicating it, but I’m not gonna let that stop me. In the face of such a challenge, I have had to grow as a scientist in order to make progress. Because I haven’t been able to replicate the Readiness Potential, I have pivoted into trying to explain some of the science behind the Readiness Potential.

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