Optimized EEG Multi-Noise Removal and Compression: Deploying a PbP-QLP Enhanced Autoencoder on STM32 Microcontroller

Electroencephalograms (EEGs) are effective and patient-friendly for diagnosing, monitoring, and preventing mental disorders. However, due to their low voltage, EEG signals often contain noise that obscures critical features, risking misdiagnosis. Current denoising methods typically address one or tw...

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Veröffentlicht in:IEEE transactions on consumer electronics Jg. 71; H. 2; S. 3218 - 3228
Hauptverfasser: Kumar, Deepak, Satija, Udit
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Autoencoders
Brain modeling
Compression
Decoding
Deep learning
edge device
electroencephalogram
Electroencephalography
Feature extraction
Low voltage
Memory devices
Mental disorders
microcontroller
Noise measurement
Noise reduction
Power lines
signal denoising
Training
ISSN:0098-3063, 1558-4127
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Zusammenfassung:Electroencephalograms (EEGs) are effective and patient-friendly for diagnosing, monitoring, and preventing mental disorders. However, due to their low voltage, EEG signals often contain noise that obscures critical features, risking misdiagnosis. Current denoising methods typically address one or two noise types and struggle with memory limitations on edge devices. To overcome these challenges, we introduce a quantization-based compressed denoising autoencoder (DAE) model using a PbP-QLP, a low-rank approximation (LRA) technique, for multi-noise removal (15 types, including power-line, baseline wander, ocular, muscle artifacts, and combinations) in EEGs on low-memory edge devices. Our compression technique reduces the model size from 8 to 1.51 MB, achieving 81% weight compression with minimal loss.
Bibliographie:ObjectType-Article-1
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ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2025.3562388