| Title: | Near-lossless EEG signal compression using a convolutional autoencoder : case study for 256-channel binocular rivalry dataset |
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| Authors: | ID Kukrál, Martin (Author)
ID Duc Pham, Tien (Author)
ID Kohout, Josef (Author)
ID Kohek, Štefan (Author)
ID Havlík, Marek (Author)
ID Grygarová, Dominika (Author)
ID Kohek, Štefan (Copyright holder) |
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| Files: |  Near-lossless_EEG_accepted_manuscript.pdf (4,18 MB)
MD5: EFCDE7C6E0531D5F2213AA9341E100DF
 https://www.sciencedirect.com/science/article/abs/pii/S0010482525002392
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| Language: | English |
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| Work type: | Not categorized |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | Electroencephalography (EEG) experiments typically generate vast amounts of data due to the high sampling rates and the use of multiple electrodes to capture brain activity. Consequently, storing and transmitting these large datasets is challenging, necessitating the creation of specialized compression techniques tailored to this data type. This study proposes one such method, which at its core uses an artificial neural network (specifically a convolutional autoencoder) to learn the latent representations of modelled EEG signals to perform lossy compression, which gets further improved with lossless corrections based on the user-defined threshold for the maximum tolerable amplitude loss, resulting in a flexible near-lossless compression scheme. To test the viability of our approach, a case study was performed on the 256-channel binocular rivalry dataset, which also describes mostly data-specific statistical analyses and preprocessing steps. Compression results, evaluation metrics, and comparisons with baseline general compression methods suggest that the proposed method can achieve substantial compression results and speed, making it one of the potential research topics for follow-up studies. |
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| Keywords: | EEG signals, electroencephalography, compression results |
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| Publication status: | Not published |
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| Publication version: | Author Accepted Manuscript |
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| Submitted for review: | 23.09.2024 |
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| Article acceptance date: | 15.02.2025 |
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| Publication date: | 05.05.2025 |
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| Number of pages: | 17 str. |
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| Numbering: | Vol. 189, [article no.] 109888 |
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| PID: | 20.500.12556/DKUM-100116  |
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| UDC: | 004.9 |
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| ISSN on article: | 1879-0534 |
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| COBISS.SI-ID: | 228270083 |
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| DOI: | 10.1016/j.compbiomed.2025.109888 |
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| Publication date in DKUM: | 08.09.2026 |
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| Views: | 167 |
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| Downloads: | 6 |
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| Metadata: |    |
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| Categories: | Misc.
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