HAPPILEE: HAPPE In Low Electrode Electroencephalography, a standardized pre-processing software for lower density recordings

Lopez, Kelsie; Monachino, A.D.; Morales, Santiago; Leach, Stephanie C.; Bowers, Maureen E.; Gabard-Durnam, Laurel

doi:10.1016/j.neuroimage.2022.119390

Cited by 20 publications

(12 citation statements)

References 69 publications

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“…This is consistent with recent work from Dede et al, 2023 that failed to find a single biomarker for ASD after extracting a high number of measures from resting-state EEG (in sensor space, including band-power and frequency measures) in a dataset of 776 individuals. There is large inter-individual variation in alpha-band rhythm features, but high internal consistency (Lopez et al, 2023), which hints at the fact that usage of longitudinal data (Gabard-Durnam et al, 2019) may be a promising approach to capture diagnosis-related information sufficiently. Our analysis suggests that it is challenging to use single-session resting-state oscillations for the construction of predictive biomarkers, but does not rule out that a more specific task-operationalization of deficits may be used to uncover disorder-specific physiology, e.g.…”

Section: Discussionmentioning

confidence: 99%

Resting-state is not enough: alpha and mu rhythms change shape across development, but lack diagnostic sensitivity

Bender,

Voytek,

Schaworonkow

2023

Preprint

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In the human brain, the alpha rhythm in occipital cortex and the mu rhythm in sensorimotor cortex are among the most prominent rhythms, with both rhythms functionally implicated in gating modality-specific information. Separation of these rhythms is non-trivial due to the spatial mixing of these oscillations in sensor space. Using a computationally efficient processing pipeline, we show that alpha and mu rhythms can be separated from electroencephalography recordings performed on nearly 3000 children aged 5-18. Using the extracted time series for each rhythm, we characterized the waveform shape on a cycle-by-cycle basis and examined whether and how the waveform shape differs across development and in attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Alpha and mu rhythms both exhibit nonsinusoidal waveform shape that changes significantly across development. We found no differences in the resting-state features of these alpha-band rhythms for either ADHD or ASD in comparison to typically developing participants. While waveform shape is ignored by traditional Fourier spectral analyses, these nonsinusoidal properties may be informative for building more constrained generative models for different types of alpha-band rhythms. Our results highlight the difficulties in identifying targeted biomarkers for ADHD and ASD from resting-state alpha-band rhythms.

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Section: Discussionmentioning

confidence: 99%

Resting-state is not enough: alpha and mu rhythms change shape across development, but lack diagnostic sensitivity

Bender,

Voytek,

Schaworonkow

2023

Preprint

View full text Add to dashboard Cite

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“…However, several guidelines now offer support on processing and analyzing parent-child fNIRS and EEG (Kayhan et al, 2022;Marriott Haresign et al, 2022;Turk et al, 2022) hyperscanning data. Furthermore, there has been a noteworthy flourishing of general preprocessing recommendations for infant EEG (Gabard-Durnam et al, 2018;Lopez et al, 2022), as well as tutorials and toolboxes that can instruct naïve users on how to extract measures of coordination, neural tracking, synchrony, and information flow (Ayrolles et al, 2021;Jessen et al, 2021). Most likely, hyperscanning analysis techniques for dealing with developmental data will continue to improve over the coming years.…”

Section: Multi-person Brain Measurementsmentioning

confidence: 99%

Early social communication through music: State of the art and future perspectives

Nguyen¹,

Flaten²,

Trainor³

et al. 2023

Preprint

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A growing body of research shows that the universal capacity for music perception and production emerges early in development. Possibly building on this predisposition, caregivers around the world often communicate with infants using songs or speech entailing song-like characteristics. This suggests that music might be one of the earliest developing and most accessible forms of interpersonal communication, providing a platform for studying early communicative behavior. However, little research has examined music in truly communicative contexts. The current work aims to facilitate the development of experimental approaches that rely on dynamic and naturalistic social interactions. We first review two longstanding lines of research that examine musical interactions by focusing either on the caregiver or the infant. These include defining the acoustic and non-acoustic features that characterize infant-directed (ID) music, as well as behavioral and neurophysiological research examining infants’ processing of musical timing and pitch. Next, we review recent studies looking at early musical interactions holistically. This research focuses on how caregivers and infants interact using music to achieve co-regulation, mutual engagement, and increase affiliation and prosocial behavior. We conclude by discussing methodological, technological, and analytical developments that might empower a comprehensive study of musical communication in early childhood.

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“…However, having more frequent artifacts does not invalidate the relevance of the technology. Artifact removal is a standard procedure (and even a necessary step) in most clinical and research EEG applications (Urigüen and Garcia-Zapirain, 2015), and recent research is currently targeting specific techniques for low density EEG (Lopez et al, 2022). Therefore, while there should be lines of work trying to minimize the appearance and impact of artifacts in this new technology, there can also be progress in the field under the assumption that this type of technology can overcome the limitation of lower data quality with the effortless obtention of massive amounts of data and the exploitation of advanced machine learning techniques (Craik et al, 2019).…”

Section: Signal Quality and Artifactsmentioning

confidence: 99%

Garments that measure EEG: Evaluation of an EEG sensor layer fully implemented with smart textiles

López‐Larraz¹,

Escolano²,

Robledo³

et al. 2023

Preprint

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This paper presents the first garment capable of measuring EEG activity with accuracy comparable to state-of-the art dry EEG systems. The main innovation is an EEG sensor layer (i.e., the electrodes, the signal transmission, and the cap support) fully implemented as a garment, using threads, fabrics and smart textiles, without relying on any metal or plastic materials. The garment is interfaced via a connector to a mobile EEG amplifier to complete the measurement system. The new EEG system (Garment-EEG) has been characterized with respect to a state-of-the-art Ag/AgCl dry-EEG system (Dry-EEG) over the forehead area of healthy participants in terms of: (1) skin-electrode impedance; (2) electrophysiological measurements (spontaneous and evoked EEG activity); (3) artifacts; and (4) user ergonomics and comfort. The results show that the Garment-EEG system provides comparable recordings to Dry-EEG, but it is more prone to getting affected by artifacts in adverse recording conditions due to poorer contact impedances. Ergonomics and comfort favor the textile-based sensor layer with respect to its metal-based counterpart. User acceptance is the main obstacle for EEG systems to democratize neurotechnology and non-invasive brain-computer interfaces. EEG sensor layers encapsulated in wearables have the potential to enable neurotechnology that is naturally accepted by people in their daily lives. Furthermore, by supporting the EEG implementation in the textile industry it is manufactured with lower cost and much less pollution compared to the metal and plastic industries.

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HAPPILEE: HAPPE In Low Electrode Electroencephalography, a standardized pre-processing software for lower density recordings

Cited by 20 publications

References 69 publications

Resting-state is not enough: alpha and mu rhythms change shape across development, but lack diagnostic sensitivity

Resting-state is not enough: alpha and mu rhythms change shape across development, but lack diagnostic sensitivity

Early social communication through music: State of the art and future perspectives

Garments that measure EEG: Evaluation of an EEG sensor layer fully implemented with smart textiles

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