Streaming brain waves to machine learning services, made easy.

Neurostack runs in Python 3.6, but we plan to update it to support new Python versions. We recommend working in a virtual environment (see Conda or venv).

For users who want to connect to the server that is already running in the cloud, or for users who do not need machine learning services (Such as those who only want to stream raw EEG data from the headset) only requirements.txt is needed. To install dependencies for the Neurostack client, run

pip install -r requirements.txt

For users who wish to use their own server (instead of the one already running in the cloud), server_requirements.txt are also needed. To install these dependencies for the Neurostack server, run

pip install -r server_requirements.txt

To run the Neurostack server, use python start_server.py. It will run on localhost:8001.

Neurostack server is currently running on neurostack.neurotechuoft.com on port 8001, so if you do not wish to run the server locally, you may directly connect to our server.

To run the Neurostack client from the command line, use python neurostack.py.

It takes three optional arguments:

--address: ip: port to run Neurostack client on. The default is localhost:8002.
--server_address: ip: port for Neurostack server to connect to.
--use_fake_data: Use flag to generate fake data.

Example Usage:

python neurostack.py --server_address neurostack.neurotechuoft.com:8001 --address localhost:8002 --use_fake_data

To use, send a job to the backend with parameters and a callback function. To connect to the backend:

from socketIO_client import SocketIO
import time
import json


def print_results(*args):
    print(args)

# server running on localhost:8002
socket_client = SocketIO('localhost', 8002)
socket_client.connect()

# initialize handlers
socket_client.on('uuid', print_results)
socket_client.on('train', print_results)
socket_client.on('predict', print_results)

To generate a UUID:

# Generate a UUID
socket_client.emit('generate_uuid', None)
socket_client.wait(seconds=1)

To send a prediction job:

uuid = 'None'             # Universally unique ID for identification
timestamp = time.time()   # Timestamp of data

# Send a prediction job with the data at timestamp
args = json.dumps({
    'uuid': uuid,
    'timestamp': timestamp
})
socket_client.emit("p300_predict", args)
socket_client.wait(seconds=2)

To send a training job:

p300 = 1                  # For training: 1 is P300, 0 is no P300

# Send a training job with the data at timestamp
args = json.dumps({
    'uuid': uuid,
    'timestamp': timestamp,
    'p300': p300
})
socket_client.emit("p300_train", args)
socket_client.wait(seconds=2)

Below are Neurostack's API endpoints. Currently we support detection for the following:

• P300 brain wave
• Left/right brain activity

Generate a universally unique identifier.

Parameters:

None

Returns:

generated UUID

Start streaming raw EEG data. Applications that want to use this should listen for the event raw_data, which Neurostack will continuously emit to.

Parameters:

uuid: UUID of whoever is wants to stream raw data. This will open up a raw data stream for this specific user.

Stop streaming raw EEG data.

Parameters:

uuid: UUID of whoever is wants to stop streaming raw data.

Make a prediction for whether P300 occurs at a timestamp.

Parameters:

uuid: UUID of whoever is making a prediction. This will determine which classifier we will load up and use.
timestamp: timestamp of chunk of data

Emits an event called predict with arguments:

uuid: UUID of caller
timestamp: timestamp of chunk of data p300: either True or False, predicting whether there is a P300 ERP
score: a value from 0 to 1 denoting the confidence in the prediction

Give a training example to the P300 classifier.

Parameters:

uuid: UUID of whoever is making a prediction. This will determine which classifier we will load up and use.
timestamp: timestamp of chunk of data
p300: either True or False (or 1 or 0), depending on whether there should be a P300 ERP

Emits an event called train with arguments:

uuid: UUID of caller timestamp: timestamp of chunk of data acc: accuracy of current classifier. This is either None/null (not enough training samples for training), or a number between 0 and 1.

Make a prediction for whether the user is using their left or right brain at a timestamp.

Parameters:

uuid: UUID of whoever is making a prediction. This will determine which classifier we will load up and use.
timestamp: timestamp of chunk of data

Emits an event called predict with arguments:

uuid: UUID of caller
timestamp: timestamp of chunk of data left: either True or False, predicting whether the user is using their left (True) or right (False) brain
score: a value from 0 to 1 denoting the confidence in the prediction

Give a training example to the left/right brain classifier.

Parameters:

uuid: UUID of whoever is making a prediction. This will determine which classifier we will load up and use.
timestamp: timestamp of chunk of data
left: True if using left brain, or False if using right brain

Emits an event called train with arguments:

uuid: UUID of caller
timestamp: timestamp of chunk of data acc: accuracy of current classifier. This is either None/null (not enough training samples for training), or a number between 0 and 1.