How to prepare a config file for Visualizer

A config file includes one section for defining specific configs for each kind of data.

EEG

To display EEG data, first of all we should specify the path of recorded EEG file by giving value to the path option. Also, we should identify the sampling_rate of recorded data. Octopus-sensing-visualizer has provided several options for displaying raw or processed EEG data. By giving True value to each option, we specify which kind of graphs to be displayed.

Octopus-sensing-visualizer supports the following graphs for displaying the EEG signals:

  • display_signal: If True, displays raw signals for all channels

  • display_power_band_bars: If True, displays a bar chart of average of each power band signal

  • display_alpha_signal: If True, displays alpha band signal extracted from all channels.

  • display_beta_signal: If True, displays beta band signal extracted from all channels.

  • display_gamma_signal: If True, displays gamma band signal extracted from all channels.

  • display_delta_signal: If True, displays delta band signal extracted from all channels.

  • display_theta_signal: If True, displays theta band signal extracted from all channels.

  • window_size: It specifies the size of window for measuring power bands in seconds

  • overlap: Shows the overlap between consequences windows in measuring power bands

Example

>>> [EEG]
>>> path=data/OpenBCI_01_01.csv
>>> sampling_rate=128
>>> display_signal=False
>>> display_power_band_bars=True
>>> display_alpha_signal=False
>>> display_beta_signal=False
>>> display_gamma_signal=False
>>> display_delta_signal=False
>>> display_theta_signal=False
>>> window_size=3
>>> overlap=2

GSR

All options related to GSR signal.

  • path: Path to GSR data (csv file)

  • sampling_rate: recording sampling rate

  • display_signal: If True, displays GSR raw signal

  • display_phasic: If True, extracts phasic component and displays it

  • display_tonic: If True, extracts tonic component and displays it

Octopus-sensing-visualizer uses neurokit library for extracting GSR components.

Example

>>> [GSR]
>>> path=data/gsr-01-01.csv
>>> sampling_rate=128
>>> display_signal=True
>>> display_phasic=True
>>> display_tonic=True
```

PPG

All options related to PPG signal.

  • path: Path to PPG data (csv file)

  • sampling_rate: recording sampling rate

  • display_signal: If True, displays PPG raw signal

  • display_hr: If True, extracts heart rate (hr) and displays it

  • display_hrv: If True, extracts heart rate variability (hrv) and displays it

  • display_breathing_rate: If True, extracts breathing rate (br) and displays it

  • window_size: The window size for extracting hr, hrv and br.

  • overlap: Shows the overlap between consequences windows

Octopus-sensing-visualizer uses heartpy library for extracting hr, hrv and breathing rate.

Example

>>> path=octopus_sensing_visualizer/test_data/ppg_video-43-00-08.csv
>>> sampling_rate=128
>>> display_signal=True
>>> display_hr=True
>>> display_hrv=True
>>> display_breathing_rate=True
>>> window_size=20
>>> overlap=19