A Novel Adaptive Independent Component Analysis Method for Multi-Channel Optically Pumped Magnetometers’ Magnetocardiography Signals

With the gradual maturation of optically pumped magnetometer (OPM) technology, the use of OPMs to acquire weak magnetocardiography (MCG) signals has started to gain widespread application. Due to the complexity of magnetic environments, MCG signals are often subject to interference from various unknown sources. Independent component analysis (ICA) is one of the most widely used methods for blind source separation. However, in practical applications, the numbers of retained components and filtering components are often selected manually, relying on subjective experience. This study proposes an adaptive ICA method that estimates the signal-to-noise ratio (SNR) before processing to determine the number of components and selects heartbeat-related components based on their characteristic indicators. The method was validated using phantom experiments and MCG data in a 128-channel OPM-MCG system. In the human subject experiment, the array output SNR reached 31.8 dB, and the processing time was significantly reduced to 1/38 of the original. The proposed method outperformed traditional techniques in terms of its ability to identify artifacts and efficiency in this regard, providing strong support for the broader clinical application of OPM-MCG.