%0 Journal Article
%T Decoding EEG for optimizing naturalistic memory.
%A Rudoler JH
%A Bruska JP
%A Chang W
%A Dougherty MR
%A Katerman BS
%A Halpern DJ
%A Diamond NB
%A Kahana MJ
%J J Neurosci Methods
%V 410
%N 0
%D 2024 Oct 20
%M 39033965
%F 2.987
%R 10.1016/j.jneumeth.2024.110220
%X <B>BACKGROUND: </B>Spectral features of human electroencephalographic (EEG) recordings during learning predict subsequent recall variability.<BR><B>METHODS: </B>Capitalizing on these fluctuating neural features, we develop a non-invasive closed-loop (NICL) system for real-time optimization of human learning. Participants play a virtual navigation-and-memory game; recording multi-session data across days allowed us to build participant-specific classification models of recall success. In subsequent closed-loop sessions, our platform manipulated the timing of memory encoding, selectively presenting items during periods of predicted good or poor memory function based on EEG features decoded in real time.<BR><B>RESULTS: </B>The induced memory effect (the difference between recall rates when presenting items during predicted good vs. poor learning periods) increased with the accuracy of neural decoding.<BR><B>METHODS: </B>This study demonstrates greater-than-chance memory decoding from EEG recordings in a naturalistic virtual navigation task with greater real-world validity than basic word-list recall paradigms. Here we modulate memory by timing stimulus presentation based on noninvasive scalp EEG recordings, whereas prior closed-loop studies for memory improvement involved intracranial recordings and direct electrical stimulation. Other noninvasive studies have investigated the use of neurofeedback or remedial study for memory improvement.<BR><B>CONCLUSIONS: </B>These findings present a proof-of-concept for using non-invasive closed-loop technology to optimize human learning and memory through principled stimulus timing, but only in those participants for whom classifiers reliably predict out-of-sample memory function.