Issue initializing Muse2 on MacOs

[pjkohler]

ℹ Computer information

• Platform OS (e.g Windows, Mac, Linux etc): macOS 10.15
• Python Version: 3.7
• Brain Interface Used (e.g Muse, OpenBCI, Notion etc): Muse with BLED112 dongle

📝 Provide detailed reproduction steps (if any)

1. Install eeg-notebooks following the instructions here: https://neurotechx.github.io/eeg-notebooks/getting_started/installation.html
2. Attempt to open EEG stream, using this code (adapted from this):

   
   import os
   import sys
   sys.path.insert(1, '/Users/kohler/code/git/eeg-notebooks')
   from eegnb import generate_save_fn
   from eegnb.devices.eeg import EEG
   from eegnb.experiments.visual_n170 import n170
   eeg_device = EEG(device=board_name)

board_name = "muse2" experiment = "visual_n170" subject_id = 0 session_nb = 0 record_duration = 120

eeg_device = EEG(device=board_name)

### ✔️ Expected result

EEG device initialization

### ❌ Actual result

muse_get_recent throws an error, message "Couldn't find any stream, is your device connected?"

Note, connecting directly with muselsl like this:

from muselsl import stream, view, list_muses

muses = list_muses() stream(muses[0]['address'])

works fine. 

## 📷 Screenshots

<img width="1136" alt="Screen Shot 2021-09-15 at 12 53 09 PM" src="https://user-images.githubusercontent.com/6529161/133478881-a5a8d4e3-4b8c-433d-88aa-f758524c4014.png">

[pjkohler]

Okay, so I think I have to start the stream in a separate terminal window before running the notebook. When I do that, I get a different error. I get the same error whether I use "muse" or "muse2" as the device.

[oreHGA]

Hi @pjkohler - Do you mind giving this a shot again? We have made recent changes that should attempt to fix this

[pjkohler]

of course! how do I update?

[oreHGA]

thanks! here are some steps to try

• pulling the latest changes - git pull origin master
• (optional) reinstalling dependencies in your environment for the project - pip install -e .
• try running the sample here https://neurotechx.github.io/eeg-notebooks/getting_started/installation.html#installing-the-python-library

lmk if you have any questions

[pjkohler]

ignore previous comment (that I deleted), I realize now I also have to start the stream in uvicMuse, before running eeg-notebooks.

[pjkohler]

okay, now I am able to start the experiment, but then I get the following error after pressing spacebar. Note that I have connected and started streaming using uvicMuse prior to running the eeg-notebooks code.

[pjkohler]

I was able to get around that error by adding import nest_asyncio nest_asyncio.apply() above. Now I get this error:

[pjkohler]

The muse is connected and streaming, I can data from it using LabRecorder software.

[JohnGriffiths]

Hi Peter.

Have you tried the brainflow option?

Device name for native Bluetooth with brainflow is 'muse2_bfn'.

[JohnGriffiths]

Relevant info re: OS compatibility etc here

https://brainflow.readthedocs.io/en/stable/SupportedBoards.html#muse

[pjkohler]

I haven't. Brainflow would be used to initialize the stream, right?

[pjkohler]

ah, I misunderstood ... maybe I misunderstood the whole approach you take here. There is no need to initialize the stream ahead of time, is there? I can simply pass "muse2_bfn" as the device name, and then your code will take of the rest, right? It appears so. I was able to get some data, and will continue to debug.

[pjkohler]

okay, this is pretty awesome. Thanks @JohnGriffiths, @oreHGA, everyone. I will use this in my class on Tuesday, and a lot more in future courses!

Also, for some reason, not sure why, I do not have to run import nest_asyncio nest_asyncio.apply() to get this to work anymore.

[JohnGriffiths]

Great to hear this is working well for you Peter!

You misunderstanding is very understandable: we are using brainflow differently to how muselsl and BlueMuse are have been used in eeg-notebooks previously, namely the stream is initiated within EEG device object initialization, without the need of the user to initiate any separate third-party streaming processes, through Python or otherwise.

BrainFlow is a relatively new library that does a great job of handling data streams across a wide variety of devices. They added muse support just after Xmas, and so we are gradually moving over to that ( mainly `_bfn' ) as the default / recommended streaming option, whilst continuing to keep the others available.

You should also check out the CLI if you haven't yet: type

eegnb runexp -ip

and follow the options.

Finally, since you mentioned your class, here is a minimal eeg-notebooks script we are using in a current experiment, that may be handy as a refernce

https://github.com/GriffithsLab/muse-aob-tms/blob/main/code/run_aob_task.py

If you configure the 'run with' option in windows/mac to the correct python environment binary, then this can run a complete experiment - including stream initiation and data saving - with a simple double-click of a desktop icon. In windows I found this works better if I create a separate shortcut that points to the run_aob_task.py file. Not sure whether that would be helpful on Mac.

[pjkohler]

holy smokes, John. I have the N170 experiment working, and that may just have to do for now, since we are coming down to the wire. But I plan to teach this course for many years (Neuroscience Techniques - a sampler of different neuroscience methodologies) so will explore more in the future.

[JohnGriffiths]

Another little snippet that might be useful

This is a quick way to grab data with the device class without having to run a psychopy experiment or to save data to file

from eegnb.devices.eeg import EEG
myeeg = EEG(device='muse2_bfb') # automatically initiates a brainflow connection
dat = myeeg.get_recent() # returns a time x channel pandas dataframe 
dat.plot() # pandas matplotlib method

Unfortunately we don't have a livestreaming figure like you get with muselsl view -v 2 yet, but we will add one at some point (unless the brainflow guys beat us to it)

In the mean time there is a simple signal quality check

Command lne:

eegnb checksigqual -ed muse2_bfn

Python:

from eegnb.analysis.utils import check_report
check_report(myeeg)

That prints out standard deviations on the command line every 5 secs (like the numbers on the axes in the muselsl view plots ); also a bit like the actual muse app.

[pjkohler]

okay, so I have eeg-notebooks running on my primary computer, which is running Mac OSX 10.15. One thing I am still confused about is the BLED dongle - so I do not need that at all?

In parallel, I am struggling with getting eeg-notebooks to run on my secondary computer which has Mac OSX 10.13 - I can share my specific hassles, but any general tips?

[pjkohler]

okay, upgrading to Catalina did not help. This is a MacBook Pro from 2013. Fresh conda environment.

Here are the errors that I am getting, when trying to run eegnb runexp -ip: CBManager is not powered on and libc++abi.dylib: terminating with uncaught exception of type NSException

...

[pjkohler]

okay from eegnb.analysis.utils import check_report check_report(myeeg) is real cool, and should just be run before each block I would think.

[oreHGA]

Hey @pjkohler curious, is this still an active issue for you?

[pjkohler]

no, I think issue has been resolved.