Automatic single-trial classification of prefrontal hemodynamic activity in an individual with Duchenne muscular dystrophy

Abstract

Brain–computer interfaces (BCIs) allow users to control external devices via brain activity alone, circumventing the somatic nervous system and the need for overt movement. Essential to BCI development is the ability to accurately detect and classify patterns of activation associated with different mental tasks. Here, we investigate the ability to automatically distinguish a mental arithmetic (MA) task from a natural baseline state in an individual with Duchenne muscular dystrophy (DMD) using signals acquired via multichannel near-infrared spectroscopy (NIRS). Using dual-wavelength NIRS, we interrogated nine sites around the frontopolar locations while the individual performed MA to answer multiple-choice questions within a system-paced paradigm. An encouraging overall classification accuracy of 71.1% was obtained, which is comparable to the average accuracy we previously reported for healthy individuals performing the same task. This result demonstrates the potential of NIRS–BCI based on task-induced prefrontal activity for use by individuals with DMD.