A method for imaging by a magnetoencephalography system characterised in that: (a) for each voxel of the brain tissue, eigenvectors and eigenvalues of a diffusion matrix are determined, by means of a diffusion MRI system; (b) an observation matrix of the state of the electrical currents in the different voxels is calculated, from images of the brain tissue which are provided by an anatomical MRI system, of an electromagnetic propagation model and positions of the sensors of the MEG system; (c) a state transition matrix (A II) of a particle spatio-temporal model giving the evolution of a state vector representative of the electrical currents in the different voxels is calculated, the state transition matrix (A II) being calculated from the diffusion eigenvectors and eigenvalues determined in step (b) as well as from electrical parameters related to each voxel, the electrical parameters of the different voxels forming a particle of the particle spatio-temporal model; (d) by means of a Kalman filter, the state vector X ^ t | t Π is estimated at a given instant (t), from the state vector estimated at a previous instant X ^ t − 1 | t − 1 Π and an observation vector ( Y t), representative of the physiological signals measured by the sensors of said MEG system at said given instant; (e) the a posteriori error covariance matrix P t | t Π is calculated from the state vector estimated at said given instant and the observation vector at this instant; steps (c), (d), (e) being performed for a plurality of particles of the particle spatio-temporal model; (f) the weights of the different particles are updated as a function of the a posteriori error covariance matrices calculated in step (e), the weight of a particle being all the more increased as the corresponding error covariance is low; (g) the highest-weight particle is selected from said plurality of particles; (h) at least one of the electrical parameters of the highest-weight particle is represented as an image, for the different voxels of the brain tissue. (a) for each voxel of the brain tissue, eigenvectors and eigenvalues of a diffusion matrix are determined, by means of a diffusion MRI system; (b) an observation matrix of the state of the electrical currents in the different voxels is calculated, from images of the brain tissue which are provided by an anatomical MRI system, of an electromagnetic propagation model and positions of the sensors of the MEG system; (c) a state transition matrix (A II) of a particle spatio-temporal model giving the evolution of a state vector representative of the electrical currents in the different voxels is calculated, the state transition matrix (A II) being calculated from the diffusion eigenvectors and eigenvalues determined in step (b) as well as from electrical parameters related to each voxel, the electrical parameters of the different voxels forming a particle of the particle spatio-temporal model; (d) by means of a Kalman filter, the state vector X ^ t | t Π is estimated at a given instant (t), from the state vector estimated at a previous instant X ^ t − 1 | t − 1 Π and an observation vector ( Y t), representative of the physiological signals measured by the sensors of said MEG system at said given instant; (e) the a posteriori error covariance matrix P t | t Π is calculated from the state vector estimated at said given instant and the observation vector at this instant; steps (c), (d), (e) being performed for a plurality of particles of the particle spatio-temporal model; (f) the weights of the different particles are updated as a function of the a posteriori error covariance matrices calculated in step (e), the weight of a particle being all the more increased as the corresponding error covariance is low; (g) the highest-weight particle is selected from said plurality of particles; (h) at least one of the electrical parameters of the highest-weight particle is represented as an image, for the different voxels of the brain tissue.