Stagnant training accuracy and loss?

[ayay-netizen]

First off I wanted to say that I think the idea behind this project is amazing and thank you for creating it and releasing it.

I seem to struggle to be able to use it properly however.

First, I tried to run it locally on my windows x64 machine. I followed the instructions (for the muse_p3) example, used the example commands in the readme, and there were no errors, however the training accuracy and loss seemed to be relatively stagnant and never went too far above a low percentage. I then changed the model_type to a 3DCNN and subsequently an LSTM to see if those models would better be able to interpret the data, but got similar behavior. I had assumed the example data you included would show the model going to a high level of competency so I felt like something was off.

Assuming there must have been something wrong with my setup, I then went to the COLAB example. As I am interested in MUSE data, I followed the link for the MUSE jupyter notebook. On COLAB, just running all the cells by default I still got the same behavior. Validation accuracy barely went over 50%. Is this really intended? I also tried increasing the number of epochs to 350 in the colab notebook but again to no avail.

Please give me guidance on how I can get better results with this incredible pipeline.

Thank you!

[kylemath]

Thanks for your comment, unfortunately I have not yet found any model architecture that can reliably predict that data conditions, perhaps you will have more luck

On Sat., Sep. 14, 2019, 1:23 p.m. Ayay, [email protected] wrote:

First off I wanted to say that I think the idea behind this project is amazing and thank you for creating it and releasing it.

I seem to struggle to be able to use it properly however.

First, I tried to run it locally on my windows x64 machine. I followed the instructions (for the muse_p3) example, used the example commands in the readme, and there were no errors, however the training accuracy and loss seemed to be relatively stagnant and never went too far above a low percentage. I then changed the model_type to a 3DCNN and subsequently an LSTM to see if those models would better be able to interpret the data, but got similar behavior. I had assumed the example data you included would show the model going to a high level of competency so I felt like something was off.

Assuming there must have been something wrong with my setup, I then went to the COLAB example. As I am interested in MUSE data, I followed the link for the MUSE jupyter notebook. On COLAB, just running all the cells by default I still got the same behavior. Validation accuracy barely went over 50%. Is this really intended? I also tried increasing the number of epochs to 350 in the colab notebook but again to no avail.

Please give me guidance on how I can get better results with this incredible pipeline.

Thank you!

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[ayay-netizen]

Oh, okay. So the program is not supposed to work on that example dataset? Could you point me to a dataset that demonstrates the network getting a decent result?

[kylemath]

Making a simulated data set will work

On Sat., Sep. 14, 2019, 4:39 p.m. Ayay, [email protected] wrote:

Oh, okay. So the program is not supposed to work on that example dataset? Could you point me to a dataset that demonstrates the network getting a decent result?

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[ayay-netizen]

But is there any real data that it works on? For simulated data, it could just be finding patterns that only exist because of the way the simulation algorithms work.

[kylemath]

Yes sure it could be, there are plenty of eeg datasets available online you could try

On Sat., Sep. 14, 2019, 5:13 p.m. Ayay, [email protected] wrote:

But is there any real data that it works on? For simulated data, it could just be finding patterns that only exist because of the way the simulation algorithms work.

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