Related Experiment Video
Updated: May 18, 2026

Evaluation of Hemisphere Lateralization with Bilateral Local Field Potential Recording in Secondary Motor Cortex of Mice
Published on: July 31, 2019
Neuronal oscillations in the EEG under varying cognitive load: a comparative study between slow waves and faster
Charmaine Demanuele1, Samantha J Broyd, Edmund J S Sonuga-Barke
1Signal Processing and Control Group, Institute of Sound & Vibration Research, University of Southampton, UK. c.demanuele@zi-mannheim.de
Objective:
This study has been specifically designed to investigate very low frequency neuronal oscillations (VLFO, <0.5 Hz) during resting states and during goal-directed tasks of graded difficulty levels, quantify the changes that the slow waves undergo in these conditions and compare them with those for higher frequency bands (namely delta, theta and alpha).
Methods:
To this end we developed a multistage signal processing methodology comprising blind source separation coupled with a neural network based feature extraction and classification method.
Results:
Changes in the amplitude and phase of brain sources estimates in the VLF band between rest and task were enhanced with increased task difficulty, but remained lower than those experienced in higher frequency bands. The slow wave variations were also significantly correlated with task performance measures, and hence with the level of task-directed attention.
Conclusions:
These findings suggest that besides their prominent sensitivity to external stimulation, VLFOs also contribute to the cortical ongoing background activity which may not be specifically related to task-specific attention and performance.
Significance:
Our work provides important insight into the association between VLF brain activity and conventional EEG frequency bands, and presents a novel framework for assessing neural activity during various mental conditions and psychiatric states.
Related Concept Videos
Brain Waves
Sleep-Wake Cycles
NREM Sleep
NREM sleep comprises four progressive stages that seamlessly merge:
