BandPowerSpectralDensity

class torcheeg.transforms.BandPowerSpectralDensity(sampling_rate: int = 128, fft_n: Optional[int] = None, num_window: int = 1, band_dict: Dict[str, Tuple[int, int]] = {'alpha': [8, 14], 'beta': [14, 31], 'gamma': [31, 49], 'theta': [4, 8]}, apply_to_baseline: bool = False)

A transform method for calculating the power spectral density of EEG signals in several sub-bands with EEG signals as input.

transform = BandPowerSpectralDensity()
transform(eeg=np.random.randn(32, 128))['eeg'].shape
>>> (32, 4)

Parameters

• sampling_rate (int) – The sampling rate of EEG signals in Hz. (defualt: 128)

• fft_n (int) – Computes the one-dimensional n-point discrete Fourier Transform (DFT) with the efficient Fast Fourier Transform (FFT) algorithm. If set to None, it will automatically match sampling_rate. (defualt: None)

• num_window (int) – Welch’s method computes an estimate of the power spectral density by dividing the data into non-overlapping segments, where the num_window denotes the number of windows. (defualt: 1)

• order (int) – The order of the filter. (defualt: 5)

• band_dict – (dict): Band name and the critical sampling rate or frequencies. By default, the power spectral density of the four sub-bands, theta, alpha, beta and gamma, is calculated. (defualt: {...})

• apply_to_baseline – (bool): Whether to act on the baseline signal at the same time, if the baseline is passed in when calling. (defualt: False)

__call__(*args, eeg: ndarray, baseline: Optional[ndarray] = None, **kwargs) → Dict[str, ndarray]

Parameters

• eeg (np.ndarray) – The input EEG signals in shape of [number of electrodes, number of data points].

• baseline (np.ndarray, optional) – The corresponding baseline signal, if apply_to_baseline is set to True and baseline is passed, the baseline signal will be transformed with the same way as the experimental signal.

Returns

The power spectral density of several sub-bands for all electrodes.

Return type

np.ndarray[number of electrodes, number of sub-bands]