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Quantify Your EEG – Unlock the Language of Brain Waves

Quantify Your EEG – Unlock the Language of Brain Waves

Ever wondered how scientists decode the brain’s complex electrical signals? In this experiment, you’ll record your own visual‑cortex EEG and use Python to reveal the hidden rhythms of alpha waves.

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About the Experiment

What Will You Learn?

What Will You Learn?

  • Analyse EEG with spectrograms and evoked potentials.
  • Quantify brain waves using Python and other open‑source tools.
  • Address challenges such as noise reduction and trial averaging.
Background

Background

Electroencephalography (EEG) captures the summed activity of millions of neurons. Because the signals are low‑amplitude and overlapping, analysts use time–frequency plots (spectrograms) and stimulus‑locked averages (evoked potentials) to extract meaningful patterns. You’ll implement both methods with Python (Anaconda and Spyder).

Procedure

  1. Equipment & Software Setup:

    1. Record alpha‑wave EEG from the visual cortex with the Human SpikerBox (eyes open vs. closed).
    2. Save the file using SpikeRecorder.
    3. Install the Anaconda distribution and open the Spyder IDE.
    Equipment & Software Setup:

  2. Python Environment & Data Preparation:

    1. Load your EEG recording file (for example, YourEEGRecording.wav).
    2. Import NumPy, Matplotlib and SciPy.
    3. Down‑sample the data with a resample factor of 0.05 to speed processing.
    Python Environment & Data Preparation:

  3. Generate a Spectrogram:

    1. Use Matplotlib’s spectrogram function to plot frequency versus time.
    2. Label the axes and identify alpha‑band changes.
    Generate a Spectrogram:

  4. Analyze Alpha Wave Activity:

    1. Extract 8–13 Hz power from the spectrogram matrix.
    2. Plot alpha power over time and compare eyes‑open versus eyes‑closed periods.
    Analyze Alpha Wave Activity:
Results & Analysis

Results & Analysis

Spectrogram: Alpha power should rise when eyes are closed.

Alpha Trends: Track how visual input modulates alpha rhythm over time. Discuss noise sources and real‑world uses—from sleep staging to brain‑computer interfaces.