def METHOD_NAME(
    eda_signal, sampling_rate=1000, frequency_band=[0.045, 0.25], show=True, out={}
):
    min_frequency = frequency_band[0]
    max_frequency = frequency_band[1]
    # Downsample, normalize, filter
    desired_sampling_rate = 50
    downsampled = signal_resample(
        eda_signal, sampling_rate=sampling_rate, desired_sampling_rate=desired_sampling_rate
    )
    normalized = standardize(downsampled)
    filtered = signal_filter(
        normalized,
        sampling_rate=desired_sampling_rate,
        lowcut=0.01,
        highcut=0.5,
        method="butterworth",
    )
    # Divide the signal into segments and obtain the timefrequency representation
    overlap = 59 * 50  # overlap of 59s in samples
    # TODO: the plot should be improved for this specific case
    _, _, bins = signal_timefrequency(
        filtered,
        sampling_rate=desired_sampling_rate,
        min_frequency=min_frequency,
        max_frequency=max_frequency,
        method="stft",
        window=60,
        window_type="blackman",
        overlap=overlap,
        show=show,
    )
    eda_symp = np.mean(bins)
    eda_symp_normalized = eda_symp / np.max(bins)
    out = {"EDA_Sympathetic": eda_symp, "EDA_SympatheticN": eda_symp_normalized}
    return out