A Practical Mobile Dry EEG System for Human Computer Interfaces

Yu, Miao; Wang, Yijun; Wang, Yu-Te; Jung, Tzyy-Ping; Kerth, Trevor; Cao, Yuchen

doi:10.1007/978-3-642-39454-6_69

Cited by 44 publications

(26 citation statements)

References 11 publications

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“…EEG was recorded using the Cognionics HD-72 dry wireless EEG headset (Cognionics, Inc., San Diego 1 ). The same 64-channel EEG system was used by Mullen et al ( 2013 ) and shares the same underlying technology as the 32-channel system reported in Chi et al ( 2013 ) and Lin et al ( 2014 ).…”

Section: Methodsmentioning

confidence: 99%

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Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight

Callan

Durantin

Terzibas

2015

Front. Syst. Neurosci.

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Application of neuro-augmentation technology based on dry-wireless EEG may be considerably beneficial for aviation and space operations because of the inherent dangers involved. In this study we evaluate classification performance of perceptual events using a dry-wireless EEG system during motion platform based flight simulation and actual flight in an open cockpit biplane to determine if the system can be used in the presence of considerable environmental and physiological artifacts. A passive task involving 200 random auditory presentations of a chirp sound was used for evaluation. The advantage of this auditory task is that it does not interfere with the perceptual motor processes involved with piloting the plane. Classification was based on identifying the presentation of a chirp sound vs. silent periods. Evaluation of Independent component analysis (ICA) and Kalman filtering to enhance classification performance by extracting brain activity related to the auditory event from other non-task related brain activity and artifacts was assessed. The results of permutation testing revealed that single trial classification of presence or absence of an auditory event was significantly above chance for all conditions on a novel test set. The best performance could be achieved with both ICA and Kalman filtering relative to no processing: Platform Off (83.4% vs. 78.3%), Platform On (73.1% vs. 71.6%), Biplane Engine Off (81.1% vs. 77.4%), and Biplane Engine On (79.2% vs. 66.1%). This experiment demonstrates that dry-wireless EEG can be used in environments with considerable vibration, wind, acoustic noise, and physiological artifacts and achieve good single trial classification performance that is necessary for future successful application of neuro-augmentation technology based on brain-machine interfaces.

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Section: Methodsmentioning

confidence: 99%

“…Total noise within the EEG band (1–50 Hz) is 0.7 microVolts RMS. The signal quality of the sensors, electrodes, and the data acquisition circuitry has been shown to be comparable to wet electrode based EEG systems with a correlation between simultaneously recorded evoked potentials of r > 0.9 (Chi et al, 2013 ; Mullen et al, 2013 ).…”

Section: Methodsmentioning

confidence: 99%

Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight

Callan

Durantin

Terzibas

2015

Front. Syst. Neurosci.

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“…This makes them impractical for everyday use in the patient's natural environment, and limits the possibilities of comfortable measurements for extended durations. A first step towards achieving mobile EEG recordings is the development of wireless EEG headsets [1], [2]. Several wireless headsets have appeared on the market, but these are still too heavy and too bulky to wear them continuously in everyday life, and offer only limited autonomy since the wireless transmission of all EEG channels to a computer or smartphone consumes a substantial amount of energy.…”

Section: Introductionmentioning

confidence: 99%

Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis

Somers

Bertrand

2016

J. Neural Eng.

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Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysisJournal of Neural Engineering, vol. 13, no. 6, pp. 066008, 2016. Archived versionAuthor manuscript: the content is identical to the content of the published paper, but without the final typesetting by the publisher Abstract-Objective: Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. Approach: We apply a distributed canonical correlation analysis (CCA-)based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. Main Results: While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Significance: Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.

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“…Although some commercial systems, like Cognionics 32-channel headset, have a greater number of channels [27], brain family has a higher sampling frequency and the same bit resolution. In fact, compared with any other system listed in table 4, brain family devices may be set at a higher sampling frequency.…”

Section: Discussionmentioning

confidence: 99%

Open Source EEG Platform with Reconfigurable Features for Multiple-Scenarios

López¹,

Gonzalez²,

Bohórquez³

et al. 2018

IJEEI

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Electroencephalogram (EEG) acquisition systems are widely used as diagnostic and research tools. This document shows the implementation of a reconfigurable family of three affordable 8-channels, 24 bits of resolution, EEG acquisition systems intended for a wide variety of research purposes. The three devices offer a modular design and upgradability, permitting changes in the firmware and software. Due to the nature of the Analog Front-End (AFE) used, no high-pass analog filters were implemented, allowing the capture of very low frequency components. Two systems of the family, called "RF-Brain" and "Bluetooth-Brain", were designed to be light and wireless, planned for experimentation where movement of the subject cannot be restricted. The sample rate in these systems can be configured up to 2000 samples per second (SPS) for the RF-Brain and 250 SPS for the Bluetooth-Brain when the 8 channels are used. If fewer channels are required, the sampling frequency can be higher (up to 4 kSPS or 2 kSPS for 1 channel for RF-Brain and Bluetooth-Brain respectively). The third system, named "USB-Brain", is a wired device designed for purposes requiring high sampling frequency acquisition and general purpose ports, with sampling rates up to 4 kSPS.

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A Practical Mobile Dry EEG System for Human Computer Interfaces

Cited by 44 publications

References 11 publications

Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight

Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight

Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis

Open Source EEG Platform with Reconfigurable Features for Multiple-Scenarios

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