def METHOD_NAME(
    eda_signal, frequency_band=[0.045, 0.25], sampling_rate=1000, show=True, out={}
):
    # This method assumes signal longer than 64 s
    if len(eda_signal) <= sampling_rate * 64:
        warn(
            "The 'posada2016' method requires a signal of length > 60 s. Try with"
            + " `method='ghiasi2018'`. Returning NaN values for now.",
            category=NeuroKitWarning,
        )
        return {"EDA_Sympathetic": np.nan, "EDA_SympatheticN": np.nan}
    # Resample the eda signal before calculate the synpathetic index based on Posada (2016)
    eda_signal_400hz = signal_resample(
        eda_signal, sampling_rate=sampling_rate, desired_sampling_rate=400
    )
    # 8-th order Chebyshev Type I low-pass filter
    sos = scipy.signal.cheby1(8, 1, 0.8, "lowpass", fs=400, output="sos")
    eda_signal_filtered = scipy.signal.sosfilt(sos, eda_signal_400hz)
    # First step of downsampling
    downsampled_1 = scipy.signal.decimate(eda_signal_filtered, q=10, n=8)  # Keep every 10th sample
    downsampled_2 = scipy.signal.decimate(downsampled_1, q=20, n=8)  # Keep every 20th sample
    # High pass filter
    eda_filtered = signal_filter(
        downsampled_2, sampling_rate=2, lowcut=0.01, highcut=None, method="butterworth", order=8
    )
    nperseg = 128
    overlap = nperseg // 2  # 50 % data overlap
    # Compute psd
    frequency, power = _signal_psd_welch(
        eda_filtered, sampling_rate=2, nperseg=nperseg, window_type="blackman", noverlap=overlap
    )
    psd = pd.DataFrame({"Frequency": frequency, "Power": power})
    # Get sympathetic nervous system indexes
    eda_symp = _signal_power_instant_compute(psd, (frequency_band[0], frequency_band[1]))
    # Compute normalized psd
    psd["Power"] /= np.max(psd["Power"])
    eda_symp_normalized = _signal_power_instant_compute(psd, (frequency_band[0], frequency_band[1]))
    psd_plot = psd.loc[
        np.logical_and(psd["Frequency"] >= frequency_band[0], psd["Frequency"] <= frequency_band[1])
    ]
    if show is True:
        ax = psd_plot.plot(x="Frequency", y="Power", title="EDA Power Spectral Density (us^2/Hz)")
        ax.set(xlabel="Frequency (Hz)", ylabel="Spectrum")
    out = {"EDA_Sympathetic": eda_symp, "EDA_SympatheticN": eda_symp_normalized}
    return out