Decoding EEG for optimizing naturalistic memory.
J Neurosci Methods
; : 110220, 2024 Jul 19.
Article
em En
| MEDLINE
| ID: mdl-39033965
ABSTRACT
BACKGROUND:
Spectral features of human electroencephalographic (EEG) recordings during learning predict subsequent recall variability.METHODS:
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.RESULTS:
We observed greater memory modulation (difference between recall rates when presenting items during predicted good vs. poor learning periods) for participants with higher out-of-sample classification accuracy. COMPARISON WITH EXISTINGMETHODS:
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.CONCLUSION:
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
J Neurosci Methods
Ano de publicação:
2024
Tipo de documento:
Article