Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical method that measures cortical activity based on hemodynamics in the brain. Physiological signals (biosignals), such as blood pressure and respiration, are known to appear in cortical fNIRS recordings. Some biosignal components occupy the same frequency band as the cortical response, and respond to the subjects activity. To process an fNIRS signal in a braincomputer interface, it is desirable to know which components of the signal come from cortical response, and which come from biosignal interference. Numerous filtering methods have been proposed to this end with mixed success, possibly because they assume that the cortical and physiological signals combine linearly, or that biosignals do not correlate with subject behavior. Here, we propose an adaptive filter with a cost function based on mutual information to selectively remove information that correlates with blood pressure from the fNIRS signal. The filter was tested with real and simulated data. The real signals were measured on seven healthy subjects performing an isometric pinching task. Cross-correlation and mutual information were employed as performance measures. The filter successfully removed correlations between blood pressure and the fNIRS signal, by an equal or greater amount compared to a traditional recursive least squares adaptive filter. Blood pressure was found to be the most informative signal to classify rest and active periods using linear discriminant analysis. Any task information in the fNIRS signal was redundant to that expressed by blood pressure.