torcheeg.utils
plot_raw_topomap
- torcheeg.utils.plot_raw_topomap(tensor: ~torch.Tensor, channel_list: ~typing.List[str], sampling_rate: int, plot_second_list: ~typing.List[int] = [0.0, 0.25, 0.5, 0.75], montage: ~mne.channels.montage.DigMontage = <DigMontage | 0 extras (headshape), 0 HPIs, 3 fiducials, 94 channels>)[source]
Plot a topographic map of the input raw EEG signal as image.
eeg = torch.randn(32, 128) img = plot_raw_topomap(eeg, channel_list=DEAP_CHANNEL_LIST, sampling_rate=128) # If using jupyter, the output image will be drawn on notebooks.
- Parameters
tensor (torch.Tensor) – The input EEG signal, the shape should be [number of channels, number of data points].
channel_list (list) – The channel name lists corresponding to the input EEG signal. If the dataset in TorchEEG is used, please refer to the CHANNEL_LIST related constants in the
torcheeg.constants
module.sampling_rate (int) – Sample rate of the data.
plot_second_list (list) – The time (second) at which the topographic map is drawn. (default:
[0.0, 0.25, 0.5, 0.75]
)montage (any) – Channel positions and digitization points defined in obj:mne. (default:
mne.channels.make_standard_montage('standard_1020')
)
- Returns
The output image in the form of
np.ndarray
.- Return type
np.ndarray
plot_feature_topomap
- torcheeg.utils.plot_feature_topomap(tensor: ~torch.Tensor, channel_list: ~typing.List[str], feature_list: ~typing.Optional[~typing.List[str]] = None, montage: ~mne.channels.montage.DigMontage = <DigMontage | 0 extras (headshape), 0 HPIs, 3 fiducials, 94 channels>)[source]
Plot a topographic map of the input EEG features as image.
eeg = torch.randn(32, 4) img = plot_feature_topomap(eeg, channel_list=DEAP_CHANNEL_LIST, feature_list=["theta", "alpha", "beta", "gamma"]) # If using jupyter, the output image will be drawn on notebooks.
- Parameters
tensor (torch.Tensor) – The input EEG signal, the shape should be [number of channels, dimensions of features].
channel_list (list) – The channel name lists corresponding to the input EEG signal. If the dataset in TorchEEG is used, please refer to the CHANNEL_LIST related constants in the
torcheeg.constants
module.feature_list (list) – . The names of feature dimensions displayed on the output image, whose length should be consistent with the dimensions of features. If set to None, the dimension index of the feature is used instead. (default:
None
)montage (any) – Channel positions and digitization points defined in obj:mne. (default:
mne.channels.make_standard_montage('standard_1020')
)
- Returns
The output image in the form of
np.ndarray
.- Return type
np.ndarray
plot_signal
- torcheeg.utils.plot_signal(tensor: ~torch.Tensor, channel_list: ~typing.List[str], sampling_rate: int, montage: ~mne.channels.montage.DigMontage = <DigMontage | 0 extras (headshape), 0 HPIs, 3 fiducials, 94 channels>)[source]
Plot signal values of the input raw EEG as image.
eeg = torch.randn(32, 128) img = plot_signal(eeg, channel_list=DEAP_CHANNEL_LIST, sampling_rate=128) # If using jupyter, the output image will be drawn on notebooks.
- Parameters
tensor (torch.Tensor) – The input EEG signal, the shape should be [number of channels, number of data points].
channel_list (list) – The channel name lists corresponding to the input EEG signal. If the dataset in TorchEEG is used, please refer to the CHANNEL_LIST related constants in the
torcheeg.constants
module.sampling_rate (int) – Sample rate of the data.
montage (any) – Channel positions and digitization points defined in obj:mne. (default:
mne.channels.make_standard_montage('standard_1020')
)
- Returns
The output image in the form of
np.ndarray
.- Return type
np.ndarray
plot_3d_tensor
- torcheeg.utils.plot_3d_tensor(tensor: Tensor, color: Union[Colormap, str] = 'hsv')[source]
Visualize a 3-D matrices in 3-D space.
eeg = torch.randn(128, 9, 9) img = plot_3d_tensor(eeg) # If using jupyter, the output image will be drawn on notebooks.
- Parameters
tensor (torch.Tensor) – The input 3-D tensor.
color (colors.Colormap or str) – The color map used for the face color of the axes. (default:
hsv
)
- Returns
The output image in the form of
np.ndarray
.- Return type
np.ndarray
plot_2d_tensor
- torcheeg.utils.plot_2d_tensor(tensor: Tensor, color: Union[Colormap, str] = 'hsv')[source]
Visualize a 2-D matrices in 2-D space.
eeg = torch.randn(9, 9) img = plot_2d_tensor(eeg) # If using jupyter, the output image will be drawn on notebooks.
- Parameters
tensor (torch.Tensor) – The input 2-D tensor.
color (colors.Colormap or str) – The color map used for the face color of the axes. (default:
hsv
)
- Returns
The output image in the form of
np.ndarray
.- Return type
np.ndarray
plot_graph
- torcheeg.utils.plot_graph(data: Data, channel_location_dict: Dict[str, List[int]], color: Union[Colormap, str] = 'hsv')[source]
Visualize a graph structure. For the electrode position information, please refer to constants grouped by dataset:
datasets.constants.emotion_recognition.deap.DEAP_CHANNEL_LOCATION_DICT
datasets.constants.emotion_recognition.dreamer.DREAMER_CHANNEL_LOCATION_DICT
datasets.constants.emotion_recognition.seed.SEED_CHANNEL_LOCATION_DICT
…
eeg = np.random.randn(32, 128) g = ToG(DEAP_ADJACENCY_MATRIX)(eeg=eeg)['eeg'] img = plot_graph(g) # If using jupyter, the output image will be drawn on notebooks.
- Parameters
data (torch_geometric.data.Data) – The input graph structure represented by torch_geometric.
channel_location_dict (dict) – Electrode location information. Represented in dictionary form, where
key
corresponds to the electrode name andvalue
corresponds to the row index and column index of the electrode on the grid.color (colors.Colormap or str) – The color map used for the face color of the axes. (default:
hsv
)
- Returns
The output image in the form of
np.ndarray
.- Return type
np.ndarray