DataScience environment for Insai BCI
Project description
Cognify - Insai Cognition Lab
Getting Started
To get started, you just need install the cognify library. The libary is constantly evolving so stay tuned for new updates.
Install
Begin by installing the cognify library, by running in your terminal
pip install cognify
Next, you need to add a separate config file containing the database credentials. This file is provided upon request. It will need to be added to the folder where cognify was installed.
To find this folder simply run
pip show cognify
This should give you the location of the cognify library
Navigate to that the cognify folder and copy the config file.
Import libraries
Retrieving data
All recorded data is stored securely in a database. We have created simple functions to retrieve data based on your user id. Therefore, only you have access to your data.
EEG
See the recordings connected to a specific user.
userId='ck9jusufs000016pbioyzehto'
recordings = dataset.get_recordings(userId)
recordings.tail()
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metricId | type | userId | createdAt | startTime | stopTime | |
---|---|---|---|---|---|---|
142 | cklujacpy119754916nk1jpgwsxp | Reading | ck9jusufs000016pbioyzehto | 2021-03-04 07:14:52.006 | "2021-03-04T07:14:51.837Z" | 2021-03-04T07:52:18.146Z |
143 | cklvxp8nw150056716nk44eebuep | Reading | ck9jusufs000016pbioyzehto | 2021-03-05 06:46:07.389 | "2021-03-05T06:46:07.128Z" | 2021-03-05T07:04:40.952Z |
144 | cklvyhjl8120897116nkythrdgkl | Reading | ck9jusufs000016pbioyzehto | 2021-03-05 07:08:07.916 | "2021-03-05T07:08:07.720Z" | 2021-03-05T08:12:21.094Z |
145 | ckm1n2i2y24577515snzllm3jxe | Reading | ck9jusufs000016pbioyzehto | 2021-03-09 06:35:07.402 | "2021-03-09T06:35:07.234Z" | 2021-03-09T06:48:31.988Z |
146 | ckm32gn98122155015snwkcr5u8y | Reading | ck9jusufs000016pbioyzehto | 2021-03-10 06:33:47.708 | "2021-03-10T06:33:47.401Z" | 2021-03-10T07:00:59.551Z |
Dataframe
Retrieve the raw eeg data from the database based on the metric id. Each recording has a single metric id. Convert the eeg data into a Pandas dataframe. Each column represents the electrical activity from a given electrode
metricId = 'ckkymq9fx5695271gntqvd743uk'
eeg = dataset.get_eeg(metricId)
df_eeg = dataset.eeg_to_df(eeg)
df_eeg.head()
Each buffer is 3 seconds long
Each buffer is sampled every 1.5 seconds
The number of buffers skipped 0
Number of timestamps: 82944
Number of unique timestamps: 82944
Some timestamps had different data values, this affected approximately 0.00 % of the data
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TP9 | AF7 | AF8 | TP10 | |
---|---|---|---|---|
time | ||||
2021-02-09 23:22:17.403875 | 95.703125000000000000000000000000 | 413.574218750000000000000000000000 | 325.195312500000000000000000000000 | 74.707031250000000000000000000000 |
2021-02-09 23:22:17.407781 | -186.523437500000000000000000000000 | -100.585937500000000000000000000000 | -19.531250000000000000000000000000 | -104.003906250000000000000000000000 |
2021-02-09 23:22:17.411687 | -285.644531250000000000000000000000 | -529.296875000000000000000000000000 | -397.949218750000000000000000000000 | -196.289062500000000000000000000000 |
2021-02-09 23:22:17.415593 | -38.574218750000000000000000000000 | -149.414062500000000000000000000000 | -156.738281250000000000000000000000 | -56.640625000000000000000000000000 |
2021-02-09 23:22:17.419500 | 228.515625000000000000000000000000 | 273.437500000000000000000000000000 | 87.402343750000000000000000000000 | 119.140625000000000000000000000000 |
MNE
Retrieve the raw eeg data based on the metric id. Export the eeg data directly to MNE. A bandpass filtered [1, 40] Hz is applied by default, but this can be removed. It returns: - Raw data in MNE format - Events related to the task - Raw data in a dataframe
metricId = 'ckkymq9fx5695271gntqvd743uk'
raw,events,df_eeg = dataset.eeg_to_mne(metricId)
Each buffer is 3 seconds long
Each buffer is sampled every 1.5 seconds
The number of buffers skipped 0
Number of timestamps: 82944
Number of unique timestamps: 82944
Some timestamps had different data values, this affected approximately 0.00 % of the data
Creating RawArray with float64 data, n_channels=4, n_times=41856
Range : 0 ... 41855 = 0.000 ... 163.496 secs
Ready.
raw.info
<Info | 8 non-empty values
bads: []
ch_names: TP9, AF7, AF8, TP10
chs: 4 EEG
custom_ref_applied: False
dig: 7 items (3 Cardinal, 4 EEG)
highpass: 1.0 Hz
lowpass: 40.0 Hz
meas_date: unspecified
nchan: 4
projs: []
sfreq: 256.0 Hz
>
PPG
Dataframe
Retrieve the raw ppg data from the database based on the metric id. With some simple preprocessing, the heart rate can be retrieved from this signal.
metricId = 'cklv4n4gk9375316nk687ui65p'
ppg = dataset.get_ppg(metricId)
df_ppg = dataset.ppg_to_df(ppg)
begin, end = 1500,2500
plt.subplot(311)
plt.plot(df_ppg[0].to_numpy()[begin:end])
plt.ylabel('Ambient')
plt.subplot(312)
plt.plot(df_ppg[1].to_numpy()[begin:end])
plt.ylabel('IR')
plt.subplot(313)
plt.plot(df_ppg[2].to_numpy()[begin:end])
plt.ylabel('Red')
plt.xlabel("seconds")
Text(0.5, 0, 'seconds')
Heart rate (In development)
Calculate the heart rate of the signal from the ppg signal. Simple preprocessing is done to clean up the signal and extract the heart rate. The segment width (in seconds) and segment overlap (in seconds) can be configured to obtain the heart rate.
metricId = 'cklvxp8nw150056716nk44eebuep'
df_hr = heartrate.get_hr(metricId,segment_width=30, segment_overlap = 0.9)
df_hr.head()
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timestamp | hr | |
---|---|---|
0 | 0.0 | 95.929464 |
1 | 3.0 | 96.145675 |
2 | 6.0 | 93.090909 |
3 | 9.0 | 91.569231 |
4 | 12.0 | 91.366417 |
plt.plot(df_hr['hr'])
[<matplotlib.lines.Line2D at 0x24a208cf970>]
Accelerometer and Gyroscope
Dataframe
Retrieve the raw accelerometer and gyroscope data from the database based on the metric id. It may be useful to use these streams to detect motion artifact and denoise the other biosignals.
metricId = 'ckjsogpjw2206420ypu7iuepcth'
accel = dataset.get_xyz(metricId,'Accelerometer')
gyro = dataset.get_xyz(metricId,'Gyroscope')
df_accel = dataset.motion_to_df(accel)
df_gyro = dataset.motion_to_df(gyro)
accel_np = df_accel.to_numpy()
times = (df_accel.timestamp-df_accel.timestamp.iloc[0])
print(np.shape(accel_np))
plt.figure(1)
plt.subplot(311)
plt.plot(times,accel_np[:,0])
plt.title('Accelerometer X')
plt.subplot(312)
plt.plot(times,accel_np[:,1])
plt.title('Y')
plt.subplot(313)
plt.plot(times,accel_np[:,2])
plt.title('Z')
gyro_np = df_gyro.to_numpy()
times = (df_gyro.timestamp-df_gyro.timestamp.iloc[0])
print(np.shape(gyro_np))
plt.figure(2)
plt.subplot(311)
plt.plot(times,gyro_np[:,0])
plt.title('Gyroscope X')
plt.subplot(312)
plt.plot(times,gyro_np[:,1])
plt.title('Y')
plt.subplot(313)
plt.plot(times,gyro_np[:,2])
plt.title('Z')
(7521, 4)
(7521, 4)
Text(0.5, 1.0, 'Z')
09-Mar-21 17:22:50 | WARNING | findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.
Recommendations
Install collapsible headings and toc2
There are two jupyter lab extensions that I highly recommend when working with projects like this. They are:
- Collapsible headings: This lets you fold and unfold each section in your notebook, based on its markdown headings. You can also hit
left
to go to the start of a section, andright
to go to the end - TOC2: This adds a table of contents to your notebooks, which you can navigate either with the Navigate menu item it adds to your notebooks, or the TOC sidebar it adds. These can be modified and/or hidden using its settings.
Expose Lab server to public
./ngrok http 8888
Export
from nbdev.export import *
notebook2script()
Converted 00_core.ipynb.
Converted 01_dataset.ipynb.
Converted 02_model.ipynb.
Converted 03_spectra.ipynb.
Converted 04_metric.ipynb.
Converted 05_report.ipynb.
Converted 06_cognitive.ipynb.
Converted 07_heartrate.ipynb.
Converted 08_summary.ipynb.
Converted Experiment1.ipynb.
Converted Experiment2.ipynb.
Converted Experiment_BehaviorVisualization.ipynb.
Converted index.ipynb.
Project details
Download files
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