Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG

Koushik, Abhay; Amores, Judith; Maes, Pattie

doi:10.48550/arxiv.1811.10111

Cited by 1 publication

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“…The Muse S is a four-channel dry EEG device (TP9, Fp1, Fp2, TP10, referenced to Fpz), sampled at 256 Hz. The Muse headband has been previously used for event-related potentials research [86], brain performance assessment [6], research into brain development [87], sleep staging [88], and stroke diagnosis [89], among others. A total of 98 partial and complete overnight recordings (mean duration: 6.3 h) from 67 unique users were selected from InteraXon's anonymized database of Muse customers, and annotated by a trained scorer following the AASM manual.…”

Section: Muse Sleep Dataset (Msd)mentioning

confidence: 99%

Robust learning from corrupted EEG with dynamic spatial filtering

Banville¹,

Wood²,

Aimone³

et al. 2021

Preprint

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Building machine learning models using EEG recorded outside of the laboratory setting requires methods robust to noisy data and randomly missing channels. This need is particularly great when working with sparse EEG montages (1-6 channels), often encountered in consumergrade or mobile EEG devices. Neither classical machine learning models nor deep neural networks trained end-to-end on EEG are typically designed or tested for robustness to corruption, and especially to randomly missing channels. While some studies have proposed strategies for using data with missing channels, these approaches are not practical when sparse montages are used and computing power is limited (e.g., wearables, cell phones). To tackle this problem, we propose dynamic spatial filtering (DSF), a multi-head attention module that can be plugged in before the first layer of a neural network to handle missing EEG channels by learning to focus on good channels and to ignore bad ones. We tested DSF on public EEG data encompassing ∼4,000 recordings with simulated channel corruption and on a private dataset of ∼100 at-home recordings of mobile EEG with natural corruption. Our proposed approach achieves the same performance as baseline models when no noise is applied, but outperforms baselines by as much as 29.4% accuracy when significant channel corruption is present. Moreover, DSF outputs are interpretable, making it possible to monitor channel importance in real-time. This approach has the potential to enable the analysis of EEG in challenging settings where channel corruption hampers the reading of brain signals.

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Section: Muse Sleep Dataset (Msd)mentioning

confidence: 99%