Enhancing source separation quality via optimal sensor placement in noisy environments

The paper aims to bridge a part of the gap between source separation and sensor placement studies by addressing a novel problem: “Predicting optimal sensor placement in noisy environments to improve source separation quality”. The structural information required for optimal sensor placement is modeled as the spatial distribution of source signal gains and the spatial correlation of noise. The sensor positions are predicted by optimizing two criteria as measures of separation quality, and a gradient-based global optimization method is developed to efficiently address this optimization problem. Numerical results exhibit superior performance when compared with classical sensor placement methodologies based on mutual information, underscoring the critical role of sensor placement in source separation with noisy sensor measurements. The proposed method is applied to actual electroencephalography (EEG) data to separate the P300 source components in a brain-computer interface (BCI) application. The results show that when the sensor positions are chosen using the proposed method, to reach a certain level of spelling accuracy, fewer sensors are required compared with standard sensor locations. •The novel problem of optimal sensor placement for source separation in noisy environments is addressed.•Source-to-sensor gains are modeled by Gaussian processes.•A multistart gradient-based method is developed to optimize the SINR and MSE criteria with respect to sensor locations.•As a real-world application, the method is applied to an EEG dataset for P300-based BCI.