Combining Neural and Behavioral Measures Enhances Adaptive Training

Abstract

Adaptive training adjusts a training task with the goal of improving learning outcomes. Adaptive training has been shown to improve human performance in attention, working memory capacity, and motor control tasks. Additionally, correlations have been observed between neural EEG spectral features (4–13 Hz) and the performance of some cognitive tasks. This relationship suggests some EEG features may be useful in adaptive training regimens. Here, we anticipated that adding a neural measure into a behavioral-based adaptive training system would improve human performance on a subsequent transfer task. We designed, developed, and conducted a between-subjects study of 44 participants comparing three training regimens: Single Item Fixed Difficulty (SIFD), Behaviorally Adaptive Training (BAT), and Combined Adaptive Training (CAT) using both behavioral and EEG measures. Results showed a statistically significant transfer task performance advantage of the CAT-based system relative to SIFD and BAT systems of 6 and 9 percentage points, respectively. Our research shows a promising pathway for designing closed-loop BCI systems based on both users' behavioral performance and neural signals for augmenting human performance.

Resonance Scanning as an Efficiency Enhancer for EEG-Guided Adaptive Neurostimulation

“…Creation and improvement of closed-loop techniques for non-invasive brain stimulation is an exciting and rapidly developing field of neuroscience. These technologies are known to modulate brain circuits and networks for successful treatment of affective symptoms and disorders [ 1 ] and to improve human performance in normal individuals [ 2 ]. EEG-guided adaptive neurostimulation is an innovative kind of such a technique that uses sensory stimulation on-line modulated by rhythmical EEG components of the individual.…”