Combining Neural and Behavioral Measures Enhances Adaptive Training

Md Lutfor Rahman; Benjamin T Files; Ashley H Oiknine; Kimberly A Pollard; Peter Khooshabeh; Chengyu Song; Antony D Passaro

doi:10.3389/fnhum.2022.787576

Combining Neural and Behavioral Measures Enhances Adaptive Training

Front Hum Neurosci. 2022 Feb 14:16:787576. doi: 10.3389/fnhum.2022.787576. eCollection 2022.

Authors

Md Lutfor Rahman^{1

2

3}, Benjamin T Files², Ashley H Oiknine⁴, Kimberly A Pollard², Peter Khooshabeh², Chengyu Song³, Antony D Passaro²

Affiliations

¹ Department of Computer Science and Information Systems, California State University San Marcos, San Marcos, CA, United States.
² Human Research and Engineering Directorate, DEVCOM Army Research Laboratory, Los Angeles, CA, United States.
³ Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA, United States.
⁴ DCS Corporation, Los Angeles, CA, United States.

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.

Keywords: EEG; adaptive training; behavioral adaptive training; brain-computer interface; combined adaptive training; electroencephalography; theta-alpha ratio; theta-alpha ratio percentage (TARP).