On my phone, though the CORVO app seems to load fine, it doesn't scale responsively to fit a phone screen. I can see this too in the browser for https://daos-84628.firebaseapp.com/

Did something change recently?

As a sanity check, we should include a beta/theta ratio metric in our ML work. This should be relatively straightforward to do with the MNE library.

Details on the FFT and band means used in our app:

• FFT input length, FFT length, and sampling rate: 256
• Epoch length: 256 samples (1s)
• Epoch interval: 250 ms (4times/s)
• FFT buffer smoothed over last 4 epochs
• Theta range: 4-8hz
• Beta range: 13-30hz

Thank you very much for this repo.
I only had libmuse_5.4.0 from my 2016 project that i now add the Emotiv Insight, Melon and maybe more

I found your libmuse_android.jar to be bigger in size and from 2018.
Please tell me what version yours is :-)
InteraXon stopped offering a sdk :-(

Greetings from Germany,
my does-them-all app: https://youtu.be/dSY7PYpg-c0

We don't care about absolute timestamped values and the packets coming from the Muse have confusingly overlapped values. We're going to rewrite our data recording code as follows

1. Collect start timestamp at beginning of session
2. Each sample gets a sequence number
3. SessionInfo contains start and end time as well as user-specific identifiers

In order to help people contribute, we should be able to easily share all our CORVO data collected through the app.

There's a lot of opportunity for over engineering. To start, let's try using GCPs export function combined with a python script to clean and organize the data

undefined is not a function (evaluating 'e.state.scoreBuffer.reduce(function(e,t){return e+t})')

This is the line that means the scoreBuffer, which is filled up with concentration scores as they come in. Could the score buffer be getting set to null?

Neural network approach for best performance and potential integration into app.

Inception and VGG16 both do a good job classifying eyes open vs eyes closed EEG spectrograms. In order to make this work for Neurodoro data we'll need to

1. Tweak CNN input so that it takes numerical data rather than images

2. Figure out how to take a pretrained image classification CNN from classification to regression

• rewrite final few layers?
• train on examples with difficulty/performance labels?

3. Train & tweak

First step in using the Dynamic Adaptive Online Assesment System to collect data for Neurodoro's classifier training is to modify it so it can run in our app and lay down training info alongside the EEG data that is being collected

• Modify task parameters to allow epochs of optimal performance to be identified
• Remove sign in requirement
• Use Window.postMessage() to send difficulty and performance data to the app after every trial. We can use Webview's onPostMessage() function to grab this data
• QA for making sure test works well on phone screen (might need to bridge to React Native for button presses?)

Let's see how good a performance we can get on a traditional sci-kit algorith. Whatever we come up with could inform what we put into the app.

We've already gotten ~68% just doing focused vs relaxed samples. For this next push let's organize the problem as a regression with an X matrix containing all freq bins for each electrode and a Y vector containing cognitive test data.

Outstanding Q's:
To accurately train on something that will be reflected during Pomodoro work sessions, we'll want to use a smart metric from the cognitive test. It seems like periods in which performance drops after 'mental fatigue' sets in and difficulty starts decline are what we want to ID. In that case, the derivative of difficulty is a really good measure to have. Does performance capture that accurately? How might we normalize the performance metric to make it most responsive to mental fatigue?

We often get NaNS in the difficulty score from CORVO.

I cover for NaNs in the app by converting them all to zeros, but they're obscuring some important data from us.

Right now we are stuck with the SVM, using contrib.learn will be very different from what we have, but allow us to switch out algorithms easily, including neural networks

undefined is not an object (evaluating 'this.props.availableMuses[this.state.selectedMuse].name')

Probably just need to make sure connector state is correctly matched with Redux

CORVO (our cognitive test webapp) seems to be working well, but the fact that it switches to different types of tests partway through might throw off our classifier.

Based on feedback from participants, the last test with the numbers is especially easy and probably doesn't elicit the same degree of concentration.

I think we need to stick to one task for the first iteration of the classifier.

Our current data format and cloud functions aren't maintaining the order of our time series data.

Let's first see if timestamps attached to our data can be used to overcome this. If not, maybe we can either change each BQ row to a chunk of samples or redo our insert function to insure ordering

We're breaking sessionInfo packets into a different pubsub topic. I've set it up in the app but we need to create the topic and give the neurodoro service account permission to it

No checks or xes when taking the test

Just tried to do this this morning and ran into some serious issues. Mostly, it seems with React Native Router Flux and it's dependencies (mobx-react).

When the time comes to upgrade to a new RN, it might also be a good time to refactor and switch from router flux to react-router

Currently, the CORVO test's layout is pretty messed up. It sights a little too far to the right on most phones and doesn't fill the screen nicely.

This isn't for a lack of effort. Ive spent about 8 hours trying to fix the test's responsive layout, but was unable to get it to work correctly. What we have now is the best I could do to get the test playable on most devices.

In my opinion, our tech stack has gotten us into some pretty tricky territory. Phaser wasn't really designed to run on mobile and, on top of that, we're not running in a typical mobile browser, but an Android WebView embedded in a native app. IIRC, the issue is that the browser height and width dimensions reported by the WebView to Phaser are completely mismatched from the phone's actual dimensions, even when controlling for pixel vs display pixel. However, I had no experience with Phaser before this project so maybe I'm missing something.

Following the Tensorflow for Poets tutorial, I've tested the ability of a retrained Inception network to identify open vs closed eye epochs of data.

The idea is to convert our EEG data into spectrograms (2s epochs) and then feeding these images (as jpgs to start with) to this classifer and teaching it distinguish between open vs closed eye spectrograms.

However, taking a look at the spectrograms we're getting from Neurodoro data, I want to reevaluate this step. There may be errors in the processing code in the jupyter notebook or even in the app's filtering code.

I got to try the data collection experiment in Neurodoro during the first NeuroTechTO hack night this year. And because I didn't exactly follow the expected procedure during the data collection, Dano suggested me to create an issue here about what other instructions could be given to the participants before they start their data collection.

I think the first thing is that it should say that the participants should try to be focused on the task, and be void of as much distraction as possible. This includes walking around, listening to other things etc. My concern is that the participants may not realize how much attention they actually need to pay to the task in order to complete it well before they click start, and they could be listening to music/talking with friends during the experiment even when they actually want to contribute good data.

And the other thing I was wondering when I was completing the task was, whether we should just rely on an instinct ("feel like...") to determine the answer, or we should actually try our best to think hard and strike for the closest answer as possible, especially for the harder (both pictures in the pair have a large amount of dots and the difference between the number is small) comparisons. Maybe I wasn't supposed to think about this question at all, but I did it because I supposed the two different processes would involve different brain mechanisms and I wasn't sure which one the experimenter was tapping at. After I talked to Dano I understood the second approach is correct. I am not sure if the other participants would have the same question as me, but if so I suggest giving an instruction to "try as hard as possible to get the correct answer in the allowed time".

I hope I did not include anything that's already there in the experiment instruction. And am I right that the main goal of the experiment is to put the participants into the state of being very focused on a task, and then collect their EEG data? If so, I wonder if this can be indicated directly to the participants as well before the start of the experiment, so that they have a guiding principle to determine the answers to any questions they may have.

Noticed that after about 4 minutes the difficulty really dropped. Never seemed to go back up

At the least the test end screen is appearing

So, we know that we're going to have to collect a bunch of data and go deep to make something really impressive, but for our debut in the app store we might as well include a 'decent enough' classifier so people can get some benefit.

The cross-validation accuracy of a SVM applied to denoised PSD data isn't terrible. Why don't we just do that?

• Select best features (research says alpha, beta, and alpha/beta ratio). Also try some kind of mathematical feature selection?
• Collect some higher quality training data with new version of app and cognitive testing screener
• Tune hyperparameters and get decent accuracy in sci-kit learn
• Implement the same SVM in Tensorflow
• Port into app

Running the app on tablet looks a little weird. Here's some UI changes to make it look better

• Change OK to Yes in go to timer Modal
• Dramatically decrease disclaimer text size
• Attention test needs to be recentered (could use a heavy overhaul, actually)

After playing around with a quite a bit of machine learning on datasets collected with the current CORVO implementation, we're concerned that there's some patterns in the performance metric that is hindering our ability to fit it to brain data.

1. The difficulty metric doesn't appear to be scaling correctly. Currently, it has the tendency to increase shortly after the test begins, peak, and then decline down to 0. Thereafter, the test gets stuck at a low difficulty.

2. The performance metric, while responsive to missed trials, also seems to have this trend to slowly decrease over time (perhaps as a function of the difficulty). It looked nice at first, as maybe an indicator of fatigue over time, but it shows up in nearly every dataset with almost exactly the same slope. Thus, we don't think it's coming directly from the user, but from the performance metric implementation.

Here's an example of what difficulty (blue) and performance (green) look like over a session:

What we'd love to try is to turn off all scaling of difficulty and performance and see if that improves the accuracy of our regression algorithms. I agree there's something to be gained from having dynamic difficulty, but a constant high difficulty combined with a performance score that's always the same should minimize sources of variance in our labels.

Mostly just because

Just noticed that my Muse seemed to disconnect after I'd been running the timer with it on my head for about 15 minutes.