Real-time feedback for decoding :: Server Side

This example demonstrates how to setup a real-time feedback mechanism using StimServer and StimClient.

The idea here is to display future stimuli for the class which is predicted less accurately. This allows on-demand adaptation of the stimuli depending on the needs of the classifier.

This will execute the rt_feedback_client.py script in a separate process so that both can run concurrently.

All brain responses are simulated from a fiff file to make it easy to test. However, it should be possible to adapt this script for a real experiment.

Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Reading 0 ... 41699  =      0.000 ...   277.709 secs...
Running subprocess: /home/circleci/.pyenv/versions/3.12.7/bin/python3.12 rt_feedback_client.py
Trial 5 accuracy: 0.0%
Trial 6 accuracy: 16.7%
Trial 7 accuracy: 0.0%
Trial 8 accuracy: 25.0%
Trial 9 accuracy: 50.0%
Trial 10 accuracy: 16.7%
Trial 11 accuracy: 16.7%
Trial 12 accuracy: 0.0%
Trial 13 accuracy: 41.7%
Trial 14 accuracy: 0.0%
Trial 15 accuracy: 58.3%
Trial 16 accuracy: 10.0%
Trial 17 accuracy: 33.3%
Trial 18 accuracy: 50.0%
Trial 19 accuracy: 50.0%
Trial 20 accuracy: 66.7%
Trial 21 accuracy: 70.8%
Trial 22 accuracy: 58.3%
Trial 23 accuracy: 55.0%
Trial 24 accuracy: 29.2%
Trial 25 accuracy: 58.3%
Trial 26 accuracy: 50.0%
Trial 27 accuracy: 62.5%
Trial 28 accuracy: 50.0%
Trial 29 accuracy: 62.5%
Trial 30 accuracy: 53.3%
Trial 31 accuracy: 66.7%
Trial 32 accuracy: 58.3%
Trial 33 accuracy: 77.5%
Trial 34 accuracy: 77.5%
Trial 35 accuracy: 75.0%
Trial 36 accuracy: 70.8%
Trial 37 accuracy: 76.2%
Trial 38 accuracy: 62.5%
Trial 39 accuracy: 61.9%
Trial 40 accuracy: 70.8%
Shutting down ...

# Author: Mainak Jas <mainak@neuro.hut.fi>
#
# License: BSD (3-clause)

import subprocess
import sys
import time

import numpy as np
import matplotlib.pyplot as plt

from sklearn import preprocessing
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix

import mne
from mne.datasets import sample
from mne.utils import running_subprocess
from mne_realtime import StimServer, MockRtClient
from mne.decoding import Vectorizer

print(__doc__)

# Load fiff file to simulate data
data_path = sample.data_path()
raw_fname = data_path  / 'MEG' / 'sample' / 'sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw_fif(raw_fname, preload=True)

fig, ax = plt.subplots(1)
ax.set(xlabel='Trials', ylabel='Classification score (% correct)',
       title='Real-time feedback')
isi = 0.01  # this is unrealistic, but will make the example run quickly
n_trials = 40  # number of trials to simulate
n_start = 5  # number of trials to run before decoding
rng = np.random.RandomState(0)

# Instantiating stimulation server
# The with statement is necessary to ensure a clean exit
with StimServer(port=4218) as stim_server:

    # The channels to be used while decoding
    picks = mne.pick_types(raw.info, meg='grad')

    rt_client = MockRtClient(raw)

    # Constructing the pipeline for classification
    # don't highpass filter because of short signal length of epochs
    scaler = preprocessing.StandardScaler()
    vectorizer = Vectorizer()
    clf = SVC(C=1, kernel='linear')
    concat_classifier = Pipeline([('vector', vectorizer),
                                  ('scaler', scaler), ('svm', clf)])
    ev_list = list(rng.randint(3, 5, n_start))  # some random starting events
    score_lv, score_rv, score_x = [], [], []

    command = [sys.executable, 'rt_feedback_client.py']
    with running_subprocess(command, after='kill',
                            stdout=subprocess.PIPE, stderr=subprocess.PIPE):
        for ii in range(n_trials):
            # Tell the stim_client about the next stimuli
            stim_server.add_trigger(ev_list[ii])

            # Collecting data
            if ii == 0:
                X = rt_client.get_event_data(event_id=ev_list[ii], tmin=-0.2,
                                             tmax=0.5, picks=picks,
                                             stim_channel='STI 014')[None, ...]
                y = ev_list[ii]
            else:
                X_temp = rt_client.get_event_data(
                    event_id=ev_list[ii], tmin=-0.2, tmax=0.5, picks=picks,
                    stim_channel='STI 014')
                X_temp = X_temp[np.newaxis]
                X = np.concatenate((X, X_temp), axis=0)
                time.sleep(isi)  # simulating the isi
                y = np.append(y, ev_list[ii])

            # Start decoding after collecting sufficient data
            if ii >= n_start - 1:
                # Now start doing rtfeedback
                X_train, X_test, y_train, y_test = train_test_split(
                    X, y, test_size=0.2, random_state=rng)
                y_pred = concat_classifier.fit(X_train,
                                               y_train).predict(X_test)
                cm = confusion_matrix(y_test, y_pred)
                score_lv.append(float(cm[0, 0]) / sum(cm, 1)[0] * 100)
                score_rv.append(float(cm[1, 1]) / sum(cm, 1)[1] * 100)
                score_x.append(ii + 1)

                # add events for the lower-performing class
                ev_list.append(3 if score_lv[-1] < score_rv[-1] else 4)
                print('Trial %d accuracy: %0.1f%%'
                      % (ii + 1, np.mean([score_lv[-1], score_rv[-1]])))

                # Now plot the accuracy
                lvh = ax.plot(score_x[-2:], score_lv[-2:],
                              c='r', marker='o', ls='-')[0]
                rvh = ax.plot(score_x[-2:], score_rv[-2:],
                              c='b', marker='o', ls='-')[0]
                ax.set(ylim=[0, 100])
                ax.legend((lvh, rvh), ('LV', 'RV'), loc='upper left')
                plt.draw()
                plt.pause(0.01)

Estimated memory usage: 303 MB