Reduction of noise from magnetoencephalography data

A noise reduction method for magnetoencephalography (MEG) data is proposed. The method is a combination of Kalman filtering and factor analysis. A statespace model for a Kalman filter was constructed using the forward problem in MEG measurement. Factor analysis provide estimations of noise covariances required by the Kalman filter to eliminate independent additive sensor noise. The proposed method supports independent component analysis (ICA), which is difficult to use in MEG analysis owing to the sensor noise. Numerical experiments were conducted to investigate the performance of the proposed method. In a single dipole case where the maximum signal-to-noise ratio (SNR) was — 10 dB, approximately equivalent to raw MEG data, noise-free signals were successfully estimated from noisy data; a 0.02 s delay of the peak latency and 15–40% of attenuation of the peak amplitude were observed. Moreover, in a multiple dipole case, independent components preprocessed with the proposed method had high correlation, 0.88 at the lowest, with correlation of 0.69 and 0.52 for those preprocessed with conventional bandpass filters. The results show that the noise reduction method reduces sensor noise effectively. High SNR-independent components are obtained by the proposed method. Real MEG data analysis was also demonstrated. The proposed method extracted auditory evoked responses from unaveraged single-trial data.