Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration

Xing, Le; Casson, Alexander J.

doi:10.1109/tbme.2024.3408331

IEEE Trans. Biomed. Eng.

2024

DOI: 10.1109/tbme.2024.3408331

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Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration

Le Xing,

Alexander J. Casson

Abstract: Xing, Le; Casson, Alex (2023). Deep autoencoder for real-time single-channel EEG cleaning and its smartphone implementation using TensorFlow Lite with hardware/software acceleration. TechRxiv. Preprint.

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Cited by 2 publications

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Advanced Signal Processing and Machine/Deep Learning Approaches on a Preprocessing Block for EEG Artifact Removal: A Comprehensive Review

Agounad,

Tarahi,

Moufassih

et al. 2024

Circuits Syst Signal Process

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Advanced Signal Processing and Machine/Deep Learning Approaches on a Preprocessing Block for EEG Artifact Removal: A Comprehensive Review

Agounad,

Tarahi,

Moufassih

et al. 2024

Circuits Syst Signal Process

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Integrating spatial and temporal features for enhanced artifact removal in multi-channel EEG recordings

Yin,

Liu,

Wang

et al. 2024

J. Neural Eng.

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Objective. Various artifacts in electroencephalography (EEG) are a big hurdle to prevent brain–computer interfaces from real-life usage. Recently, deep learning-based EEG denoising methods have shown excellent performance. However, existing deep network designs inadequately leverage inter-channel relationships in processing multi-channel EEG signals. Typically, most methods process multi-channel signals in a channel-by-channel way. Considering the correlations among EEG channels during the same brain activity, this paper proposes utilizing channel relationships to enhance denoising performance. Approach. We explicitly model the inter-channel relationships using the self-attention mechanism, hypothesizing that these correlations can support and improve the denoising process. Specifically, we introduce a novel denoising network, named spatial-temporal fusion network (STFNet), which integrates stacked multi-dimension feature extractor to explicitly capture both temporal dependencies and spatial relationships. Main results. The proposed network exhibits superior denoising performance, with a 24.27% reduction in relative root mean squared error compared to other methods on a public benchmark. STFNet proves effective in cross-dataset denoising and downstream classification tasks, improving accuracy by 1.40%, while also offering fast processing on CPU. Significance. The experimental results demonstrate the importance of integrating spatial and temporal characteristics. The computational efficiency of STFNet makes it suitable for real-time applications and a potential tool for deployment in realistic environments.

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Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration

Abstract: Xing, Le; Casson, Alex (2023). Deep autoencoder for real-time single-channel EEG cleaning and its smartphone implementation using TensorFlow Lite with hardware/software acceleration. TechRxiv. Preprint.

Cited by 2 publications

References 55 publications

Advanced Signal Processing and Machine/Deep Learning Approaches on a Preprocessing Block for EEG Artifact Removal: A Comprehensive Review

Advanced Signal Processing and Machine/Deep Learning Approaches on a Preprocessing Block for EEG Artifact Removal: A Comprehensive Review

Integrating spatial and temporal features for enhanced artifact removal in multi-channel EEG recordings

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