Representation Learning for Electroencephalogram-Based Biometrics Using Holo-Hilbert Spectral Analysis
In this paper, we propose a subject-independent learning method for electroencephalogram-based biometrics using the Holo-Hilbert spectral analysis method. We propose a neural network architecture that uses as input the spectral maps constructed using this method and considering both frequency and am...
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| Veröffentlicht in: | Pattern recognition and image analysis 2022-09, Vol.32 (3), p.682-688 |
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| container_title | Pattern recognition and image analysis |
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| creator | Svetlakov, M. Hodashinsky, I. Sarin, K. |
| description | In this paper, we propose a subject-independent learning method for electroencephalogram-based biometrics using the Holo-Hilbert spectral analysis method. We propose a neural network architecture that uses as input the spectral maps constructed using this method and considering both frequency and amplitude modulation. The neighbourhood components analysis loss function was used as the loss function for subject-independent learning. The architecture was tested on the publicly available PhysioNet Electroencephalogram Motor Movement/Imagery Dataset achieving a 9.5% equal error rate. The main advantages of the proposed approach are subject-independency and suitability for interpretation using created spectra and Integrated Gradients method. |
| doi_str_mv | 10.1134/S1054661822030415 |
| format | Article |
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The main advantages of the proposed approach are subject-independency and suitability for interpretation using created spectra and Integrated Gradients method.</description><subject>Amplitude modulation</subject><subject>Biometrics</subject><subject>Computer architecture</subject><subject>Computer Science</subject><subject>Image Processing and Computer Vision</subject><subject>Learning</subject><subject>Neural networks</subject><subject>Pattern Recognition</subject><subject>SELECTED PAPERS OF THE 8th INTERNATIONAL WORKSHOP “IMAGE MINING. 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| subjects | Amplitude modulation Biometrics Computer architecture Computer Science Image Processing and Computer Vision Learning Neural networks Pattern Recognition SELECTED PAPERS OF THE 8th INTERNATIONAL WORKSHOP “IMAGE MINING. THEORY AND APPLICATIONS” Spectrum analysis |
| title | Representation Learning for Electroencephalogram-Based Biometrics Using Holo-Hilbert Spectral Analysis |
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