Abstract:Researchers are increasingly attempting to undertake electroencephalography (EEG) recordings in novel environments and contexts outside of the traditional static laboratory setting. The term “mobile EEG,” although commonly used to describe many of these undertakings, is ambiguous, since it attempts to encompass a wide range of EEG device mobility, participant mobility, and system specifications used across investigations. To provide quantitative parameters for “mobile EEG,” we developed a Categorisation of Mob… Show more
“…Several terms associated with mobile EEG have been used in this rapidly expanding literature (e.g., portable/wireless/wearable/dry EEG). As this field consolidates, terminology used by neurodevelopmentalists is likely to become more consistent ( Bateson et al, 2017 ). For example, the concept of ‘transparent EEG’ has recently been introduced to describe the combination of features deemed necessary for everyday mobile sensing applications, such as the system also needing to be self-applicable, motion-tolerant, near invisible and suitable for long recordings ( Bleichner and Debener, 2017 ).…”
Section: Recent Advancesmentioning
confidence: 99%
“…However, given the diversity of manufacturers – for both research-grade systems (tailored to scientific research) and consumer-oriented systems (targeted primarily for everyday applications) – not all of these features are necessarily present in a given system. Thus, the degree of the systems’ mobility can vary, such that some devices need to be carried within a backpack while others are fully head-mounted ( Bateson et al, 2017 ). Fig.…”
Mobile electroencephalography (mobile EEG) represents a next-generation neuroscientific technology – to study real-time brain activity – that is relatively inexpensive, non-invasive and portable. Mobile EEG leverages state-of-the-art hardware alongside established advantages of traditional EEG and recent advances in signal processing. In this review, we propose that mobile EEG could open unprecedented possibilities for studying neurodevelopmental disorders. We first present a brief overview of recent developments in mobile EEG technologies, emphasising the proliferation of studies in several neuroscientific domains. As these developments have yet to be exploited by neurodevelopmentalists, we then identify three research opportunities: 1) increase in the ease and flexibility of brain data acquisition in neurodevelopmental populations; 2) integration into powerful developmentally-informative research designs; 3) development of innovative non-stationary EEG-based paradigms. Critically, we address key challenges that should be considered to fully realise the potential of mobile EEG for neurodevelopmental research and for understanding developmental psychopathology more broadly, and suggest future research directions.
“…Several terms associated with mobile EEG have been used in this rapidly expanding literature (e.g., portable/wireless/wearable/dry EEG). As this field consolidates, terminology used by neurodevelopmentalists is likely to become more consistent ( Bateson et al, 2017 ). For example, the concept of ‘transparent EEG’ has recently been introduced to describe the combination of features deemed necessary for everyday mobile sensing applications, such as the system also needing to be self-applicable, motion-tolerant, near invisible and suitable for long recordings ( Bleichner and Debener, 2017 ).…”
Section: Recent Advancesmentioning
confidence: 99%
“…However, given the diversity of manufacturers – for both research-grade systems (tailored to scientific research) and consumer-oriented systems (targeted primarily for everyday applications) – not all of these features are necessarily present in a given system. Thus, the degree of the systems’ mobility can vary, such that some devices need to be carried within a backpack while others are fully head-mounted ( Bateson et al, 2017 ). Fig.…”
Mobile electroencephalography (mobile EEG) represents a next-generation neuroscientific technology – to study real-time brain activity – that is relatively inexpensive, non-invasive and portable. Mobile EEG leverages state-of-the-art hardware alongside established advantages of traditional EEG and recent advances in signal processing. In this review, we propose that mobile EEG could open unprecedented possibilities for studying neurodevelopmental disorders. We first present a brief overview of recent developments in mobile EEG technologies, emphasising the proliferation of studies in several neuroscientific domains. As these developments have yet to be exploited by neurodevelopmentalists, we then identify three research opportunities: 1) increase in the ease and flexibility of brain data acquisition in neurodevelopmental populations; 2) integration into powerful developmentally-informative research designs; 3) development of innovative non-stationary EEG-based paradigms. Critically, we address key challenges that should be considered to fully realise the potential of mobile EEG for neurodevelopmental research and for understanding developmental psychopathology more broadly, and suggest future research directions.
“…The average accuracy of seizure diaries is <50%, and this complicates diagnosis and management of epilepsy. 1,2 Recent progress in the development of wearable electroencephalography (EEG) [3][4][5][6] and non-EEG seizure detection devices was reviewed in a number of papers, 3,[7][8][9][10] all revealing the unmet need for devices that could chronically monitor epileptic brain activity. Implantable subscalp EEG devices meet this need by detecting electrographic seizures, which has been shown to be a robust objective measure that correlates to clinical symptoms.…”
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“…In measurements from ambulatory or mobile EEG systems (MEEG), the artefacts are often even larger and more frequent than for a static system. However, there is increasing interest in the measurement of ERPs while individuals engage in the physical activities associated with normal living [1].…”
This paper presents a method that aims to separate auditory event-related potentials (ERP) from noise. In practice, ERPs would be approximated by weighted sums of Principal Component (PCA) basis signals calculated from clean data. Projection of measured signals onto the PCA subspace greatly reduces noise. A second step uses Kalman filtering to optimally combine the PCA filtered signal with the ERP expected before measurement. Much of the power of the proposed algorithm comes from exploiting apriori cross-channel information in the form of a PCA weight covariance matrix. In this paper the performance of the method is quantified using synthetic multi-channel ERP signals to which known amounts of synthetic noise is added to all the channels. The use of synthetic data means and signal and noise are known and so signal-to-noise improvement can be determined. For a wide range of initial SNRs, PCA filtering increases SNR by 10 dB while Kalman filtering yields another 10 dB improvement.
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