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•We propose a method to handle data corruption in EEG recorded with very few channels.•An attention-based neural network reweighs EEG channels according to task relevance.•We validate the method on clinical EEG and at-home mobile EEG with strong corruption.•Our method outperforms other denoising strategies under strong channel corruption. 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 consumer-grade 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 ∼4000 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 the effective 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.

In this paper we present a computationally economical method of recovering the projective motion of head mounted cameras or EyeTap devices, for use in wearable computer-mediated reality. The tracking system combines featureless vision and inertial methods in a closed loop system to achieve accurate robust head tracking using inexpensive sensors. The combination of inertial and vision techniques provides the high accuracy visual registration needed for fitting computer graphics onto real images and the robustness to large interframe camera motion due to fast head rotations. Operating on a 1.2 GHz Pentium III wearable computer with graphics accelerated hardware, the system is able to register live video images with less than 2 pixels of error (0.3 degrees) at 12 frames per second. Fast image registration is achieved by offloading computer vision computation onto the graphics hardware, which is readily available on many wearable computer systems. As an application of this tracking approach, we present a system which allows wearable computer users to share views of their current environments that have been stabilised to another viewer''s head position.

EEG is an established method for quantifying large-scale neuronal dynamics which enables diverse real-world biomedical applications including brain-computer interfaces, epilepsy monitoring and sleep staging. Advances in sensor technology have freed EEG from traditional laboratory settings, making low-cost ambulatory or at-home assessments of brain function possible. While ecologically valid brain assessments are becoming more practical, the impact of their reduced spatial resolution and susceptibility to noise remain to be investigated. This study set out to explore the potential of at-home EEG assessments for biomarker discovery using the brain age framework and four-channel consumer EEG data. We analyzed recordings from more than 5200 human subjects (18-81 years) during meditation and sleep, focusing on the age prediction task. With cross-validated R2 scores between 0.3 - 0.5, prediction performance was within the range of results obtained by recent benchmarks focused on laboratory-grade EEG. While age prediction was successful from both meditation and sleep recordings, the latter led to higher performance. Analysis by sleep stage uncovered that N2-N3 stages contained most of the signal. When combined, EEG features extracted from all sleep stages gave the best performance, suggesting that the entire night of sleep contains valuable age-related information. Furthermore, model comparisons suggested that information was spread out across electrodes and frequencies, supporting the use of multivariate modeling approaches. Thanks to our unique dataset of longitudinal repeat sessions spanning 153 to 529 days from eight subjects, we finally evaluated the variability of EEG-based age predictions, showing that they reflect both trait- and state-like information. Overall, our results demonstrate that state-of-the-art machine learning approaches based on age prediction can be readily applied to real-world EEG recordings obtained during at-home sleep and meditation practice.

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 consumer-grade 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.

PurposeUncertainties remain about the safety and efficacy of therapies for managing intracranial hypertension in acute brain injured (ABI) patients. This study aims to describe the therapeutical approaches used in ABI, with/without intracranial pressure (ICP) monitoring, among different pathologies and across different countries, and their association with six months mortality and neurological outcome.MethodsA preplanned subanalysis of the SYNAPSE-ICU study, a multicentre, prospective, international, observational cohort study, describing the ICP treatment, graded according to Therapy Intensity Level (TIL) scale, in patients with ABI during the first week of intensive care unit (ICU) admission.Results2320 patients were included in the analysis. The median age was 55 (I-III quartiles = 39–69) years, and 800 (34.5%) were female. During the first week from ICU admission, no-basic TIL was used in 382 (16.5%) patients, mild-moderate in 1643 (70.8%), and extreme in 295 cases (eTIL, 12.7%). Patients who received eTIL were younger (median age 49 (I–III quartiles = 35–62) vs 56 (40–69) years, p < 0.001), with less cardiovascular pre-injury comorbidities (859 (44%) vs 90 (31.4%), p < 0.001), with more episodes of neuroworsening (160 (56.1%) vs 653 (33.3%), p < 0.001), and were more frequently monitored with an ICP device (221 (74.9%) vs 1037 (51.2%), p < 0.001). Considerable variability in the frequency of use and type of eTIL adopted was observed between centres and countries. At six months, patients who received no-basic TIL had an increased risk of mortality (Hazard ratio, HR = 1.612, 95% Confidence Interval, CI = 1.243–2.091, p < 0.001) compared to patients who received eTIL. No difference was observed when comparing mild-moderate TIL with eTIL (HR = 1.017, 95% CI = 0.823–1.257, p = 0.873). No significant association between the use of TIL and neurological outcome was observed.ConclusionsDuring the first week of ICU admission, therapies to control high ICP are frequently used, especially mild-moderate TIL. In selected patients, the use of aggressive strategies can have a beneficial effect on six months mortality but not on neurological outcome.