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Related Experiment Video

Updated: Jan 10, 2026

Recording Network Activity in Spinal Nociceptive Circuits Using Microelectrode Arrays

Recording Network Activity in Spinal Nociceptive Circuits Using Microelectrode Arrays

Published on: February 9, 2022

Predicting spiking activity from scalp EEG.

Dixit Sharma^1,2, Bart Krekelberg¹

¹Center for Molecular and Behavioral Neuroscience, Rutgers University-Newark, Newark, NJ 07102, United States of America.

Journal of Neural Engineering

|November 27, 2025

Summary

This summary is machine-generated.

Researchers used electroencephalography (EEG) to estimate spiking activity in the visual cortex. Comprehensive EEG features reliably predicted neural spiking activity, improving brain-machine interface (BMI) potential.

Keywords:

EEG flickering visual stimulus nonhuman primates primary visual cortex spiking activity steady-state visual evoked potential

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Last Updated: Jan 10, 2026

Recording Network Activity in Spinal Nociceptive Circuits Using Microelectrode Arrays

Recording Network Activity in Spinal Nociceptive Circuits Using Microelectrode Arrays

Published on: February 9, 2022

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Area of Science:

Neuroscience
Computational Neuroscience
Biomedical Engineering

Background:

The relationship between electroencephalography (EEG) and neural spiking activity is not fully understood.
This knowledge gap hinders the inference of neural dynamics from non-invasive EEG and the development of brain-machine interfaces (BMIs).

Purpose of the Study:

To estimate spiking activity in the visual cortex using non-invasive scalp EEG.
To investigate the predictive power of EEG spectrotemporal features for neural spiking activity.

Main Methods:

Recorded simultaneous scalp EEG and V1 multi-unit activity envelope (MUAe) in a macaque monkey.
Analyzed the relationship between MUAe and EEG frequency bands, extracting phase, amplitude, and phase-amplitude coupling.
Used linear regression to predict MUAe from EEG features.

Main Results:

Spectrotemporal EEG features reliably predicted V1 MUAe, despite complex and frequency-dependent relationships.
EEG phase, amplitude, and coupling each contributed to MUAe prediction accuracy.
MUAe prediction was more accurate in superficial cortical layers, and stimulus frequency phase further improved predictions.

Conclusions:

Comprehensive spectrotemporal features of non-invasive EEG contain significant information about underlying spiking activity.
This finding highlights the richness of EEG signals and their complex relationship with neural dynamics.
Utilizing detailed spectrotemporal EEG signatures can enhance the performance of BMI applications.