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Brain Waves

Brain Waves

Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:

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Updated: Aug 16, 2025

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

Characterizing physiological high-frequency oscillations using deep learning.

Yipeng Zhang¹, Hoyoung Chung¹, Jacquline P Ngo²

¹Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America.

Journal of Neural Engineering

|December 21, 2022

Summary

This summary is machine-generated.

Deep learning identified distinct features of physiological high-frequency oscillations (HFOs) in the brain. This approach helps differentiate normal HFOs from those indicating epilepsy, improving surgical guidance.

Keywords:

HFO machine learning physiological HFO

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Area of Science:

Neuroscience
Epileptology
Artificial Intelligence

Background:

Intracranial high-frequency oscillations (HFOs) are potential biomarkers for the epileptogenic zone.
Distinguishing physiological HFOs from pathological ones is challenging due to their presence in healthy brain regions.

Purpose of the Study:

To characterize the morphological features of physiological HFOs using deep learning (DL).
To develop a DL model for differentiating HFOs in eloquent cortex (EC) from non-EC regions.
To assess the impact of DL-based HFO classification on predicting postoperative seizure outcomes.

Main Methods:

Analysis of 63,379 interictal HFOs from 18 children with neocortical epilepsy.
Transformation of intracranial EEG data into DL training inputs, using EC defined by functional mapping as labels.
Application of a weakly supervised DL model to analyze morphological characteristics of HFOs.

Main Results:

Physiological HFOs (ecHFOs) exhibited lower amplitude around onset and in low frequencies, with a bell-shaped time-frequency pattern.
A minority of ecHFOs (22.9%) were HFOs with spikes.
DL model predictions were influenced by HFO morphological characteristics.
Using the resection ratio of non-ecHFOs improved seizure outcome prediction (AUC 0.82 vs. 0.76).

Conclusions:

A DL algorithm successfully characterized salient features of physiological HFOs.
DL-based HFO classification can potentially distinguish physiological from pathological HFOs.
This method may enhance surgical planning and resection guidance in epilepsy.