A 9.2-g Fully-Flexible Wireless Ambulatory EEG Monitoring and Diagnostics Headband With Analog Motion Artifact Detection and Compensation

Dabbaghian, Alireza; Yousefi, Tayebeh; Fatmi, Syyeda Zainab; Shafia, Pooria; Kassiri, Hossein

doi:10.1109/tbcas.2019.2936327

Cited by 37 publications

(12 citation statements)

References 19 publications

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“…where Z path R Body + Z ESI + Z E . This formula emphasizes the impact of Z ESI variation, which directly changes the amplitude of the output signal due to gain distortion (Song et al, 2014;Dabbaghian et al, 2019).…”

Section: Understanding Of Electroencephalography Motion Artifactmentioning

confidence: 99%

“…To attain low cutoff frequency, a GΩ range resistance is required. In CMOS applications, resistor-emulating (Dätwyler, 2021), (E) reverse-curve arch-shaped dry electrode (Lee et al, 2015), (F) bristle-type electrode (Grozea et al, 2011), (G) microneedle electrode (Ren et al, 2020a)), (H) semi-wet electrode (Wang et al, 2016), and (I) flexible EEG system (Dabbaghian et al, 2019). circuits such as pseudo-resistors, switched-capacitor resistors, and switched-resistor resistors are adopted (Xu et al, 2017).…”

Section: Active Electrode and Analog Circuitsmentioning

confidence: 99%

“…FIGURE 5 | Types of electrodes: (A) patch-type wet electrode, (B) dry electrode, (C) dry electrode with fingers (Liao et al, 2009), (D) flexible dry electrode(Dätwyler, 2021), (E) reverse-curve arch-shaped dry electrode(Lee et al, 2015), (F) bristle-type electrode(Grozea et al, 2011), (G) microneedle electrode(Ren et al, 2020a)), (H) semi-wet electrode(Wang et al, 2016), and (I) flexible EEG system(Dabbaghian et al, 2019).…”

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confidence: 99%

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Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

et al. 2021

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Removal of motion artifacts is a critical challenge, especially in wearable electroencephalography (EEG) and photoplethysmography (PPG) devices that are exposed to daily movements. Recently, the significance of motion artifact removal techniques has increased since EEG-based brain–computer interfaces (BCI) and daily healthcare usage of wearable PPG devices were spotlighted. In this article, the development on EEG and PPG sensor systems is introduced. Then, understanding of motion artifact and its reduction methods implemented by hardware and/or software fashions are reviewed. Various electrode types, analog readout circuits, and signal processing techniques are studied for EEG motion artifact removal. In addition, recent in-ear EEG techniques with motion artifact reduction are also introduced. Furthermore, techniques compensating independent/dependent motion artifacts are presented for PPG.

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Section: Understanding Of Electroencephalography Motion Artifactmentioning

confidence: 99%

Section: Active Electrode and Analog Circuitsmentioning

confidence: 99%

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confidence: 99%

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Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

et al. 2021

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“…The amplitude of the recorded artifact spike is determined by several factors such as the distance between the recording and the stimulation sites, the gain of the amplifier, and the electrode impedance (Johnson et al, 2017;Pazhouhandeh et al, 2018;Jeon et al, 2019;Uehlin et al, 2020). The artifact is typically hundreds of millivolts in amplitude, and 10 to 100 times higher than the amplitude of the recorded neural signals (Dabbaghian et al, 2019;Lee and Je, 2020). Several stimulation artifact cancelation techniques have been reported previously.…”

Section: Key Issues In Neural Recording and Stimulation Circuits Stimulation Induced Artifact In The Closed-loop Systemmentioning

confidence: 99%

Advances in Neural Recording and Stimulation Integrated Circuits

Liu

Mao

et al. 2021

Front. Neurosci.

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In the past few decades, driven by the increasing demands in the biomedical field aiming to cure neurological diseases and improve the quality of daily lives of the patients, researchers began to take advantage of the semiconductor technology to develop miniaturized and power-efficient chips for implantable applications. The emergence of the integrated circuits for neural prosthesis improves the treatment process of epilepsy, hearing loss, retinal damage, and other neurological diseases, which brings benefits to many patients. However, considering the safety and accuracy in the neural prosthesis process, there are many research directions. In the process of chip design, designers need to carefully analyze various parameters, and investigate different design techniques. This article presents the advances in neural recording and stimulation integrated circuits, including (1) a brief introduction of the basics of neural prosthesis circuits and the repair process in the bionic neural link, (2) a systematic introduction of the basic architecture and the latest technology of neural recording and stimulation integrated circuits, (3) a summary of the key issues of neural recording and stimulation integrated circuits, and (4) a discussion about the considerations of neural recording and stimulation circuit architecture selection and a discussion of future trends. The overview would help the designers to understand the latest performances in many aspects and to meet the design requirements better.

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“…Benefiting from the rapid development of the sensor technologies and the miniaturization of the electronics, the wearable and implantable electronic devices have been widely used in biomedical research Yang et al (2019); Juteau and Gosselin (2020), motion detection Le et al (2019); Dabbaghian et al (2019) and health monitoring Ding et al (2021); Lee et al, 2020b. In such scenarios, the wireless sensor nodes (WSNs) are in charge of monitoring the physical parameters and transmitting the digitized data. It is highly demanded to prolong the usage time of the WSNs while subjecting to small-volume batteries.…”

Section: Introductionmentioning

confidence: 99%

Review of the Multi-Input Single-Inductor Multi-Output Energy Harvesting Interface Applied in Wearable Electronics

Gao

Wang

et al. 2021

Front.Electron.

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Along with the industrialization and popularization of the wearable electronics, an increasing number of the wireless sensor nodes (WSNs) are deployed. Nevertheless, the conventional battery-based power supply system has no longer satisfied the requirement of large-scale WSNs in terms of battery life, which emerges the energy harvesting (EH) technique. In order to combine various of energy sources and drive multi-loads, the multi-input single-inductor multi-output (MISIMO) EH interface applied to wearable electronics is spotlighted. In this mini-review article, the solutions for improving power conversion efficiency (PCE) and output quality in MISIMO EH interface are summarized. Furthermore, the future trends of MISIMO EH interface are also presented.

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A 9.2-g Fully-Flexible Wireless Ambulatory EEG Monitoring and Diagnostics Headband With Analog Motion Artifact Detection and Compensation

Cited by 37 publications

References 19 publications

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

Advances in Neural Recording and Stimulation Integrated Circuits

Review of the Multi-Input Single-Inductor Multi-Output Energy Harvesting Interface Applied in Wearable Electronics

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