| """ | |
| ================================ | |
| Spectral analysis of the trials | |
| ================================ | |
| This example demonstrates how to perform spectral | |
| analysis on epochs extracted from a specific subject | |
| within the :class:`moabb.datasets.Cattan2019_PHMD` dataset. | |
| """ | |
| # Authors: Pedro Rodrigues <pedro.rodrigues01@gmail.com> | |
| # Modified by: Gregoire Cattan <gcattan@hotmail.fr> | |
| # License: BSD (3-clause) | |
| import warnings | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from moabb.datasets import Cattan2019_PHMD | |
| from moabb.paradigms import RestingStateToP300Adapter | |
| warnings.filterwarnings("ignore") | |
| ############################################################################### | |
| # Initialization | |
| # -------------- | |
| # | |
| # 1) Specify the channel and subject to compute the power spectrum. | |
| # 2) Create an instance of the :class:`moabb.datasets.Cattan2019_PHMD` dataset. | |
| # 3) Create an instance of the :class:`moabb.paradigms.RestingStateToP300Adapter` paradigm. | |
| # By default, the data is filtered between 1-35 Hz, | |
| # and epochs are extracted from 10 to 50 seconds after event tagging. | |
| # Select channel and subject for the remaining of the example. | |
| channel = "Cz" | |
| subject = 1 | |
| dataset = Cattan2019_PHMD() | |
| events = ["on", "off"] | |
| paradigm = RestingStateToP300Adapter(events=events, channels=[channel]) | |
| ############################################################################### | |
| # Estimate Power Spectral Density | |
| # ------------------------------- | |
| # 1) Obtain the epochs for the specified subject. | |
| # 2) Use Welch's method to estimate the power spectral density. | |
| f, S, _, y = paradigm.psd(subject, dataset) | |
| ############################################################################### | |
| # Display of the data | |
| # ------------------- | |
| # | |
| # Plot the averaged Power Spectral Density (PSD) for each label condition, | |
| # using the selected channel specified at the beginning of the script. | |
| fig, ax = plt.subplots(facecolor="white", figsize=(8.2, 5.1)) | |
| for condition in events: | |
| mean_power = np.mean(S[y == condition], axis=0).flatten() | |
| ax.plot(f, 10 * np.log10(mean_power), label=condition) | |
| ax.set_xlim(paradigm.fmin, paradigm.fmax) | |
| ax.set_ylim(100, 135) | |
| ax.set_ylabel("Spectrum Magnitude (dB)", fontsize=14) | |
| ax.set_xlabel("Frequency (Hz)", fontsize=14) | |
| ax.set_title("PSD for Channel " + channel, fontsize=16) | |
| ax.legend() | |
| fig.show() | |