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Recording Brain Activity with Ear-Electroencephalography

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Published on: March 31, 2023

Forehead and In-Ear EEG Acquisition and Processing: Biomarker Analysis and Memory-Efficient Deep Learning Algorithm

Roberto De Fazio^1,2, Şule Esma Yalçınkaya¹, Ilaria Cascella¹

¹Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy.

Sensors (Basel, Switzerland)

|October 16, 2025

Summary

This study developed a wearable electroencephalography (EEG) system for sleep staging. The system achieved high accuracy in classifying sleep stages using a single EEG derivation, enabling unobtrusive home-based monitoring.

Keywords:

EEG acquisition feature selection forehead EEG in-ear EEG physiological signal analysis sleep disorders sleep staging two-step DL algorithm wearable EEG

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Area of Science:

Biomedical Engineering
Neuroscience
Signal Processing

Background:

Wearable electroencephalography (EEG) systems offer non-invasive, continuous sleep monitoring outside clinical settings.
Advancements in EEG technology and feature extraction enable portable sleep analysis.
Current methods often require complex setups, limiting home-based applications.

Purpose of the Study:

To develop and evaluate an EEG-based acquisition system for sleep staging adaptable for wearable applications.
To identify and validate a robust feature set for sleep stage classification from a single EEG derivation.
To assess the feasibility of a deep learning model for accurate sleep staging in unobtrusive monitoring systems.

Main Methods:

Utilized a custom experimental setup with the ADS1299EEG-FE-PDK evaluation board for EEG signal acquisition.
Extracted time, frequency, and non-linear domain features, reduced using mRMR and PCA.
Trained a two-step deep learning model (LSTM and dense layers) with attention and augmentation for 5-class sleep stage classification on the BOAS dataset.

Main Results:

Achieved high overall accuracies of 93.5% and 94.7% with reduced feature sets (94% and 98% cumulative explained variance).
Attained an accuracy of 97.9% using the complete feature set.
Demonstrated reliable sleep stage classification using a single frontal EEG derivation (F4-F3).

Conclusions:

A single EEG frontal derivation is sufficient for reliable sleep stage classification.
The developed system is feasible for unobtrusive, home-based sleep monitoring.
Wearable EEG systems can significantly advance sleep disorder diagnosis and management.