neurowarp 0.0.1

Toolbox for Dynamic Time Warping based latency differences and temporal correlations between two time series for neuroscience

Python NeuroWarp

Python NeuroWarp is provided as a package that can be installed via PyPi. It consists of two functions that can be called after importing neurowarp: timeseries_correlation and latency_difference - these are the general-purpose scripts enable the DTW analyses presented in Transient Attention Gates Access Consciousness: Coupling N2pc and P3 Latencies using Dynamic Time Warping.

Installation

We recommend that you create a new virtual environment for our module via:

1. Open a terminal / cmd.exe window, navigate (via cd) to the directory you want to create the environment and enter: python -m venv neurowarp_env

2. Activate the neurowarp_env via:

   Windows: path\to\neurowarp_env\Scripts\activate

   MacOS: source neurowarp_env/bin/activate

3. Install neurowarp via pip (enter while neurowarp_env is active): pip install neurowarp

4. The functions can be used as explained below after importing the module

For more detail on virtual environments & pip click here

DTW Temporal Correlation - neurowarp.timeseries_correlation()

Perform a DTW based bootstrap analysis to assess the temporal correlation between two time series (Figure 5 of our paper).

Important Notes

• Time series must be 2D matrices
  • I.e., data points (e.g. time) x subjects (i.e., replications)
• We provide the ERPs of correct and intrusion trials for users to explore this function

Running timeseries_correlation using our ERPs

Enter the following into Python and make sure to enter your actual paths!

1. import neurowarp
2. from scipy.io import loadmat
3. data = loadmat("your/path/to/example_series_N2pcP3s")
4. series_1 = data["P3_Correct"]
5. series_2 = data["N2pc_Correct"]
6. name_1 = "P3"
7. name_2 = "N2pc"
8. savepath = "where/to/store/results/to"
9. num_boots = 10000
   • The number of bootstrap samples that you want to implement
10. outlier = 0
    • Exclude outliers if their DTW area is +-5 standard deviations from the mean
11. try_to_fix_ylims = 1
    • Attempt to standardise y-limits of marginals
12. neurowarp.timeseries_correlation(series_1, series_2, name_1, name_2, savepath, num_boots, outlier, try_to_fix_ylims)

Note that the figure will look slightly different to that of our paper due to different x/y limits. See the replicate_figures folder if you want to replicate our figure as it was printed.

DTW Latency Difference - neurowarp.latency_difference()

Assess the latency difference between two conditions (i.e., within-subjects effect) or between two groups (i.e., across-subjects effect) of any signal of interest (in milliseconds).

Figures 3 & 4 of our paper show a two conditions analysis

Important Notes

• Reference and query time series must be 2D matrices
  • I.e., data points (e.g., time) x subjects (i.e., replications)
  • Time series have to be of equal sizes
• analysis_design determines whether you want to assess a within- or between-subjects latency effect (can only take “within” or “between” as input)
• We provide the ERPs of correct and intrusion trials for users to explore this function

Running timeseries_correlation using our ERPs

Enter the following into Python and make sure to enter your actual paths!

1. import neurowarp
2. from scipy.io import loadmat
3. data = loadmat("your/path/to/example_series_N2pcP3s")
4. analysis_design = "within"
5. query = data["N2pc_Intrusion"]
6. reference = data["N2pc_Correct"]
7. name_query = "Intrusion"
8. name_reference = "Correct"
9. units = "\u03BCV"
   • The units that your signals are measured in (in our case micro volts)
10. sampling_rate = 500
    • The number of data points per second in Hertz
11. savepath = "where/to/store/results/to"
12. permutations = 10000
    • The number of permutations you would like to implement in statistical testing (we recommend >=10000)
13. neurowarp.latency_difference(analysis_design, query, reference, name_query, name_reference,units, sampling_rate, savepath, permutations)

Dependencies

Python NeuroWarp requires the following toolboxes which are automatically installed via pip install neurowarp

• Numpy
• Matplotlib
• Tslearn
• Scipy

Tests

Python NeuroWarp was tested with Python 3.10.9.