De Novo Brain-Computer Interfacing Deforms Manifold of Populational Neural Activity Patterns in Human Cerebral Cortex

Abstract

Human brains are capable of modulating innate activities to adapt to novel environments and tasks; for senso-rimotor neural system this means acquisition of a rich repertoire of activity patterns that improve behavioral performance. To directly map the process of acquiring the neural repertoire during tasks onto performance im-provement, we analyzed net neural populational activity during the learning of its voluntary modulation by brain-computer interface (BCI) operation in female and male humans. The recorded whole-head high-density scalp electroencephalograms (EEGs) were subjected to dimensionality reduction algorithm to capture changes in cortical activity patterns represented by the synchronization of neuronal oscillations during adaptation. Although the preserved variance of targeted features in the reduced dimensions was 20%, we found system-atic interactions between the activity patterns and BCI classifiers that detected motor attempt; the neural manifold derived in the embedded space was stretched along with motor-related features of EEG by model-based fixed classifiers but not with adaptive classifiers that were constantly recalibrated to user activity. Moreover, the manifold was deformed to be orthogonal to the boundary by de novo classifiers with a fixed de-cision boundary based on biologically unnatural features. Collectively, the flexibility of human cortical signaling patterns (i.e., neural plasticity) is only induced by operation of a BCI whose classifier required fixed activities, and the adaptation could be induced even the requirement is not consistent with biologically natural re-sponses. These principles of neural adaptation at a macroscopic level may underlie the ability of humans to learn wide-ranging behavioral repertoires and adapt to novel environments.