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Updated: Sep 5, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

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EEG Identity Authentication in Multi-Domain Features: A Multi-Scale 3D-CNN Approach.

Rongkai Zhang¹, Ying Zeng^1,2, Li Tong¹

¹Henan Key Laboratory of Imaging and Intelligent Processing, People's Liberation Army (PLA) Strategic Support Force Information Engineering University, Zhengzhou, China.

Frontiers in Neurorobotics

|July 5, 2022

Summary

Electroencephalogram (EEG) authentication uses brainwaves for secure identification. This study introduces a novel framework using 3D EEG signal representation and multi-scale convolutions, significantly improving authentication accuracy and efficiency.

Keywords:

3D-CNN EEG ERP identity authentication multi-scale

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Area of Science:

Biometrics and Information Security
Neuroscience and Signal Processing

Background:

Electroencephalogram (EEG) authentication is promising for security due to inherent, living traits.
Current EEG authentication faces limitations from low signal-to-noise ratio (SNR), instability, and randomness.
Feature fusion across domains is key to enhancing EEG identity authentication performance.

Purpose of the Study:

To propose an innovative EEG authentication framework enhancing identity recognition.
To explore the effectiveness of 3D EEG signal representation and multi-scale convolution for feature extraction.
To investigate optimal strategies for combining authentication methods and network architectures.

Main Methods:

Developed an EEG authentication framework with efficient 3D spatial-temporal-frequency signal representation.
Employed a multi-scale convolution structure to extract individual identity features from EEG signals.
Investigated various convolution kernel combinations and authentication strategy pairings.

Main Results:

The 3D EEG representation effectively captures multi-angle mixed features.
Multi-scale convolution proved effective in extracting high-quality identity characteristics.
Small-size, long-shape convolution kernels showed suitability for event-related potential (ERP) tasks, improving accuracy and convergence.
Matching the number of network branches to EEG components yielded excellent cost-performance.

Conclusions:

The proposed framework demonstrates excellent classification performance for EEG authentication.
Multi-scale convolution is a powerful technique for extracting robust identity features from EEG across domains.
Network architecture design, including branch number and module combination, significantly impacts EEG signal processing performance.