SpikerTools

SpikerTools is a Python library designed to help students analyze SpikeRecorder files from SpikerBoxes. It provides easy-to-use functions for loading, processing, and visualizing neural and EEG data, enabling students to explore neuroscience concepts through hands-on data analysis.

Table of Contents

• Features
• Installation
• Getting Started
• Usage Examples
  • Loading Data
  • Plotting Session Overview
  • Event-Related Potential (ERP)
  • Peri-Event Time Histogram (PETH)
  • Spectrogram Analysis
  • Average Power Spectrum
• For Teachers
  • Sample Lesson Plan
• Contributing
• License

Features

• Load neural and EEG data from WAV files.
• Handle events and neuronal spikes with timestamped annotations.
• Filter and normalize signal data.
• Compute statistical measures like inter-spike intervals and firing rates.
• Visualize data with various plots:
  • Session overview with event markers.
  • Event-Related Potentials (ERPs).
  • Peri-Event Time Histograms (PETH) with raster plots.
  • Spectrograms with event overlays.
  • Average power spectra for frequency analysis.
  • Histograms of inter-event and inter-spike intervals.

Installation

You can install SpikerTools using pip:

pip install spikertools

Note: If SpikerTools is not yet available on PyPI, you can install it directly from the source code:

git clone https://github.com/BackyardBrains/SpikerTools.git
cd SpikerTools
pip install .

Getting Started

Prerequisites

• Python 3.6 or higher
• Required Python packages:
  • numpy
  • scipy
  • matplotlib
  • seaborn

Install the required packages using:

pip install numpy scipy matplotlib seaborn

Usage Examples

Loading Data

from spikertools import Session

# Load your data file (WAV file and corresponding events file)
wav_file_path = 'data/neurons/rate_coding/BYB_Recording_2022-01-13_13.18.29.wav'

# Initialize the Session
session = Session(wav_file_path)

Plotting Channels from Session

# Plot the session overview with event markers
session.plots.plot_channels()

Event-Related Potential (ERP)

p300_wav_file = 'data/eeg/p300/BYB_Recording_2019-06-11_13.23.58.wav'
s = Session(p300_wav_file)

# Define events for P300 (default is '1' and '2') and add colors
s.events[0].name = 'standard'
s.events[0].color = 'blue'

s.events[1].name = 'oddball'
s.events[1].color = 'red'

# Assign location as name to the P300 channel (Optional)
s.channels[0].name = 'P4'

# Optional: Filter the P300 channel to reduce high frequency noise
s.channels[0].filter(ftype='lp', cutoff=10, order=3)

Peri-Event Time Histogram (PETH)

# Plot PETH with raster for a neuron aligned to an event
session.plots.plot_peth(
    neuron=s.neurons[0],     # Neuron to plot (defaults to first)
    events=s.events,        # List of Event objects (defaults to all)
    epoch_window=(-0.5, 1.0),      # 500ms before to 1000ms after event
    bin_size=0.04,           # 40ms bins
    title="Peri-Event Time Histogram (PETH) with Raster Plots for Touch Pressure Events", # Add a custom title
    save_path=None, # Save the plot to a file
    show=True
)

Spectrogram Analysis

# Plot spectrogram of the EEG data with event markers
session.plots.plot_spectrogram(
    channel=session.channels[0],
    freq_range=(0, 50),
    events=session.events
)

Average Power Spectrum

# Plot average power spectra during 'Open' and 'Close' events
session.plots.plot_average_power(
    events=session.events,
    freq_range=(0, 30),
    epoch_window=(0, 5),
    channel=session.channels[0]
)

For Teachers

SpikerTools is designed to be an educational tool that integrates seamlessly into classroom activities. Here's how you can incorporate it into your teaching:

• Hands-On Data Analysis: Provide students with real neural or EEG data recordings and guide them through loading and analyzing the data using SpikerTools.
• Visualization of Neural Activity: Use the plotting functions to help students visualize neural spikes, event-related potentials, and frequency content of EEG signals.
• Concept Reinforcement: Reinforce concepts like neuronal firing rates, inter-spike intervals, and the effects of stimuli on neural activity.
• Customizable Plots: Encourage students to explore different parameters and customize plots to deepen their understanding.
• Interdisciplinary Learning: Integrate programming skills with neuroscience, promoting interdisciplinary education.

Sample Lesson Plan

1. Introduction to Neural Signals:

   • Discuss the basics of neural spikes and EEG signals.
   • Explain the significance of events and stimuli in neural recordings.

2. Data Loading and Preprocessing:

   • Show students how to load data into SpikerTools.
   • Demonstrate filtering and normalization techniques.

3. Data Visualization:

   • Guide students through plotting session overviews and ERPs.
   • Analyze spectrograms to understand frequency components.

4. Data Analysis:

   • Calculate firing rates and inter-spike intervals.
   • Compare neural responses to different stimuli.

5. Discussion and Interpretation:

   • Interpret the results and discuss their implications.
   • Encourage students to ask questions and explore further.

Contributing

We welcome contributions to enhance SpikerTools. If you have ideas for new features, improvements, or bug fixes, please:

1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Commit your changes with clear messages.
4. Submit a pull request describing your changes.

Please ensure that your code follows best practices and includes appropriate tests.

License

SpikerTools is released under the MIT License.