A 16-channel bluetooth enabled wearable EEG platform with dry-contact electrodes for brain computer interface

Nathan, Viswam; Wu, Jian; Zong, Chengzhi; Zou, Yuan; Dehzangi, Omid; Reagor, Mary; Jafari, Roozbeh

doi:10.1145/2534088.2534098

Cited by 14 publications

(8 citation statements)

References 9 publications

(7 reference statements)

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“…It incorporates two daisy-chained TI ADS1299 analog front ends for EEG, a TI MSP430 microcontroller and a BlueRadios dual mode Bluetooth radio for wireless communication of the data to a PC or any mobile device [23]. …”

Section: Methodsmentioning

confidence: 99%

Automatic Identification of Artifact-Related Independent Components for Artifact Removal in EEG Recordings

Zou

Nathan

Jafari

2016

IEEE J. Biomed. Health Inform.

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Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from Independent Component Analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event related potential (ERP)-related independent components (ICs). However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g. identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by non-biological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature based clustering algorithm used to identify artifacts which have physiological origins and 2) the electrode-scalp impedance information employed for identifying non-biological artifacts. The results on EEG data collected from 10 subjects show that our algorithm can effectively detect, separate, and remove both physiological and non-biological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods.

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Section: Methodsmentioning

confidence: 99%

Automatic Identification of Artifact-Related Independent Components for Artifact Removal in EEG Recordings

Zou

Nathan

Jafari

2016

IEEE J. Biomed. Health Inform.

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“…There are no general solutions for choosing the host processor for interfacing the AFE. In the literature [10][11][12][13][14][15][16][17][18][19][20][21], depending on the use case and required computational performance, the host processor ranges from microcontrollers to embedded microprocessors with 1 GHz or higher frequency. It is inappropriate to choose high-performing CPU for such battery-powered EEG recording devices.…”

Section: Analog Front-endmentioning

confidence: 99%

Development of a Modular Board for EEG Signal Acquisition

Uktveris

Jusas²

2018

2018 5th International Conference on Mathematics and Computers in Sciences and Industry (MCSI)

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The increased popularity of brain-computer interfaces (BCIs) has created a new demand for miniaturized and low-cost electroencephalogram (EEG) acquisition devices for entertainment, rehabilitation, and scientific needs. The lack of scientific analysis for such system design, modularity, and unified validation tends to suppress progress in this field and limit supply for new low-cost device availability. To eliminate this problem, this paper presents the design and evaluation of a compact, modular, battery powered, conventional EEG signal acquisition board based on an ADS1298 analog front-end chip. The introduction of this novel, vertically stackable board allows the EEG scaling problem to be solved by effectively reconfiguring hardware for small or more demanding applications. The ability to capture 16 to 64 EEG channels at sample rates from 250 Hz to 1000 Hz and to transfer raw EEG signal over a Bluetooth or Wi-Fi interface was implemented. Furthermore, simple but effective assessment techniques were used for system evaluation. While conducted tests confirm the validity of the system against official datasheet specifications and for real-world applications, the proposed quality verification methods can be further employed for analyzing other similar EEG devices in the future. With 6.59 microvolts peak-to-peak input referred noise and a −97 dB common mode rejection ratio in 0-70 Hz band, the proposed design can be qualified as a low-cost precision cEEG research device.

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“…1, was used for data acquisition [7]. This includes an MSP430 microcontroller and a TI ADS1299 AFE with very low inputreferred noise to preserve the original signal as much as possible.…”

Section: B Data Acquisition Platformmentioning

confidence: 99%

“…Global considerations in the ECG signal acquisition path: 1) 10 activities; 2) 3 electrode placements, including wrist/chest-1/chest-2 ECG (S/R/B represent signal/reference/bias electrodes); 3) 2 electrode types; 4) 2 coupling methods for the signal and reference electrodes; 5) the customized bio-potential acquisition board [7], providing two methods to drive the bias electrode for common-mode rejection (CMR): the open-loop and closed-loop CMR methods …”

Section: Placementsmentioning

confidence: 99%

An ECG dataset representing real-world signal characteristics for wearable computers

Zhang¹,

Zahed²,

Nathan

et al. 2015

2015 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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We present an ECG dataset collected in realworld scenarios for wearable devices that includes over 260 recordings of 90-210 seconds that provide guidance for designers to evaluate signal acquisition circuit and system solutions. Several variations on the signal acquisition path are demonstrated, including various sources of interference (baseline wander, motion artifacts, and power line interference), signal path variations (electrode type, coupling method, and common-mode rejection method), and electrode placements (wrist and chest). Based on detailed analysis of signal characteristics under different scenarios, analog front-end (AFE) design recommendations are proposed.

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A 16-channel bluetooth enabled wearable EEG platform with dry-contact electrodes for brain computer interface

Cited by 14 publications

References 9 publications

Automatic Identification of Artifact-Related Independent Components for Artifact Removal in EEG Recordings

Automatic Identification of Artifact-Related Independent Components for Artifact Removal in EEG Recordings

Development of a Modular Board for EEG Signal Acquisition

An ECG dataset representing real-world signal characteristics for wearable computers

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