supereeg.simulate_bo

supereeg.simulate_bo(n_samples=1000, n_elecs=10, locs=None, cov='random', sample_rate=1000, sessions=None, meta=None, noise=0.1, random_seed=False)[source]

Simulate brain object

Parameters:
n_samples : int

Number of time samples

n_elecs : int

Number of electrodes

locs : np.ndarray or pd.DataFrame

A location by coordinate (x,y,z) matrix of simulated electrode locations

cov : str or np.array

The covariance structure of the data.

If ‘eye’, the covariance will be the identity matrix.

If ‘toeplitz’, the covariance will be a toeplitz matrix.

If ‘random’, uses a random semidefinite matrix with a set random seed.

If ‘distance’calculates the euclidean distance between each electrode.

You can also pass a custom covariance matrix by simply passing numpy array that is n_elecs by n_elecs.

sample_rate : int or float

Sample rate (Hz)

sessions : list

Sesssions

meta : str

Meta info

noise : int or float

Noise added to simulation

random_seed : bool or int

Default False. If True, set random seed to 123. If int, set random seed to value.

Returns:
bo : Brain data object

Instance of Brain data object containing simulated subject data and locations