Objectives: Understanding the influence of cognitive activity on subsequent sleep has both theoretical and applied implications. This study aims to investigate the effect of pre-sleep cognitive activity, in the context of avoiding emotional interference, on macro-sleep and sleep spindles.
Methods: In a within-subjects design, participants' sleep electroencephalography was collected in both the with and without pre-sleep cognitive activity conditions. Subsequent macro-sleep (i.e., sleep stage distribution and sleep parameters) and spindle characteristics (i.e., density, amplitude, duration, and frequency) were analyzed. In addition, a novel machine learning framework (i.e., deep neural network, DNN) was used to discriminate between cognitive activity and control conditions.
Results: There were no significant differences in macro-sleep and sleep spindles between the cognitive activity and control conditions. Spindles-based DNN models achieved over 96% accuracy in differentiating between the two conditions, with fast spindles performing better than full-range and slow spindles.
Conclusions: These results suggest a weak but positive effect of pre-sleep cognitive activity on subsequent sleep. It sheds light on a possible low-cost and easily accessible sleep intervention strategy for clinical and educational purposes.
Keywords: Deep neural network; Macro-sleep; Pre-sleep cognitive activity; Sleep spindles.
Published by Elsevier Inc.