Identification of memory reactivation during sleep by EEG classification

Suliman Belal, James Cousins, Wael El-Deredy, Laura Parkes, Jules Schneider, Hikaru Tsujimura, Alexia Zoumpoulaki, Marta Perapoch, Lorena Santamaria Covariabus, Penelope Lewis

Research output: Contribution to journalArticlepeer-review


Memory reactivation during sleep is critical for consolidation, but also extremely difficult to measure as it is subtle, distributed and temporally unpredictable. This article reports a novel method for detecting such reactivation in standard sleep recordings. During learning, participants produced a complex sequence of finger presses, with each finger cued by a distinct audio-visual stimulus. Auditory cues were then re-played during subsequent sleep to trigger neural reactivation through a method known as targeted memory reactivation (TMR). Next, we used electroencephalography data from the learning session to train a machine learning classifier, and then applied this classifier to sleep data to determine how successfully each tone had elicited memory reactivation. Neural reactivation was classified above chance in all participants when TMR was applied in SWS, and in 5 of the 14 participants to whom TMR was applied in N2. Classification success reduced across numerous repetitions of the tone cue, suggesting either a gradually reducing responsiveness to such cues or a plasticity-related change in the neural signature as a result of cueing. We believe this method will be valuable for future investigations of memory consolidation.

Original languageEnglish
Pages (from-to)203-214
Number of pages9
Early online date17 Apr 2018
Publication statusPublished - 1 Aug 2018


  • Journal Article


Dive into the research topics of 'Identification of memory reactivation during sleep by EEG classification'. Together they form a unique fingerprint.

Cite this