CWTSpectrum

class torcheeg.transforms.CWTSpectrum(sampling_rate: int = 250, wavelet: str = 'morl', total_scale: int = 128, contourf: bool = False, apply_to_baseline: bool = False)

A transform method to convert EEG signals of each channel into spectrograms using wavelet transform.

transform = CWTSpectrum()
transform(eeg=np.random.randn(32, 1000))['eeg'].shape
>>> (32, 128, 1000)

Part of the existing work uses Resize to warp the output spectrum to a specified size suitable for CNN processing.

transform = Compose([
    CWTSpectrum(),
    ToTensor(),
    Resize([260, 260])
])
transform(eeg=np.random.randn(32, 1000))['eeg'].shape
>>> (32, 128, 1000)

When contourf is set to True, a spectrogram of filled contours will be generated for each channel and converted to np.ndarray and returned. This option is usually used for single-channel analysis or visualization of a single channel.

transform = CWTSpectrum(contourf=True)
transform(eeg=np.random.randn(32, 1000))['eeg'].shape
>>> (32, 480, 640, 4)

Parameters

• sampling_rate (int) – The sampling period for the frequencies output in Hz. (defualt: 128)

• wavelet (str) – Wavelet to use. Options include: cgau1, cgau2, cgau3, cgau4, cgau5, cgau6, cgau7, cgau8, cmor, fbsp, gaus1, gaus2 , gaus3, gaus4, gaus5, gaus6, gaus7, gaus8, mexh, morl, shan. (defualt: 'morl')

• total_scale – (int): The total wavelet scales to use. (defualt: 128)

• contourf – (bool): Whether to output the np.ndarray corresponding to the image with content of filled contours. (defualt: False)

• 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 spectrograms based on the wavelet transform for all electrodes. If contourf=False, the output shape is [number of electrodes, total_scale, number of data points]. Otherwise, the output shape is [number of electrodes, height of image, width of image of image, 4], where 4 represents the four channels of the image colors.

Return type

np.ndarray[number of electrodes, …]