The development of Arduino-based low-cost wireless modular device for brainwave acquisition

Pratama, Sra Harke; Rahmadhani, Andri; Bramana, Andrian; Oktivasari, Prihatin; Handayani, Nita; Haryanto, Freddy; Suprijadi,; Khotimah, Siti Nurul

doi:10.1088/1742-6596/1248/1/012035

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Instead of applying a commercial EEG electrode system, Pratama et al prepared dry-active electrodes with pre-amplifier modules INA118 on their own, as depicted in Figure 7 [ 126 ]. In this way, they could produce a low-cost system from easily available components.…”

Section: Eeg Measurementsmentioning

confidence: 99%

See 1 more Smart Citation

Measuring Biosignals with Single Circuit Boards

Ehrmann¹,

Błachowicz

Homburg

et al. 2022

Bioengineering

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To measure biosignals constantly, using textile-integrated or even textile-based electrodes and miniaturized electronics, is ideal to provide maximum comfort for patients or athletes during monitoring. While in former times, this was usually solved by integrating specialized electronics into garments, either connected to a handheld computer or including a wireless data transfer option, nowadays increasingly smaller single circuit boards are available, e.g., single-board computers such as Raspberry Pi or microcontrollers such as Arduino, in various shapes and dimensions. This review gives an overview of studies found in the recent scientific literature, reporting measurements of biosignals such as ECG, EMG, sweat and other health-related parameters by single circuit boards, showing new possibilities offered by Arduino, Raspberry Pi etc. in the mobile long-term acquisition of biosignals. The review concentrates on the electronics, not on textile electrodes about which several review papers are available.

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Section: Eeg Measurementsmentioning

confidence: 99%

“… Electrode design ( left panel) and implementation ( right panel). From [ 126 ], originally published under a CC-BY license. …”

Section: Figurementioning

confidence: 99%

Measuring Biosignals with Single Circuit Boards

Ehrmann¹,

Błachowicz

Homburg

et al. 2022

Bioengineering

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show abstract

“…With the Arduino Open-Hardware electronics platform [4], microcontrollers (MCUs) have become a comparably easy-to-use tool for rapid prototyping [5], and they have provided grounds for a wide range of open-source solutions [6]. Their modularity qualifies them as a teaching platform [7,8], and they made their way into research labs [9] in experiments as part of the front-end [10,11], the back-end [12,13] and automation control [14,15], as well as multilevel machine interfaces [16,17], specifically the Internet-of-Things [18,19]. With the upsurge of miniaturized sensor technologies, their interface capabilities provide direct and easy solutions [20] for monitoring platforms [21,22] -particularly in the case of independently operating systems with limitations on power consumption as is often the case for environmental sciences [23][24][25] and nuclear science [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Arduino-Based Readout Electronics for Nuclear and Particle Physics

Köhli,

Weimar,

Schmidt

et al. 2024

Sensors

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Open Hardware-based microcontrollers, especially the Arduino platform, have become a comparably easy-to-use tool for rapid prototyping and implementing creative solutions. Such devices in combination with dedicated front-end electronics can offer low-cost alternatives for student projects, slow control and independently operating small-scale instrumentation. The capabilities can be extended to data taking and signal analysis at mid-level rates. Two detector realizations are presented, which cover the readouts of proportional counter tubes and of scintillators or wavelength-shifting fibers with silicon photomultipliers (SiPMs). The SiPMTrigger realizes a small-scale design for coincidence readout of SiPMs as a trigger or veto detector. It consists of a custom mixed signal front-end board featuring signal amplification, discrimination and a coincidence unit for rates of up to 200 kHz. The nCatcher transforms an Arduino Nano to a proportional counter readout with pulse shape analysis: time over threshold measurement and a 10-bit analog-to-digital converter for pulse heights. The device is suitable for low-to-medium-rate environments up to 5 kHz, where a good signal-to-noise ratio is crucial. We showcase the monitoring of thermal neutrons. For data taking and slow control, a logger board is presented that features an SD card and GSM/LoRa interface.

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Comparison of Wet and Dry EEG Electrodes Based On Brain Signals Characterization In Temporal and Anterior Frontal Areas Using Audio Stimulation

Afif

Pratama

Haryanto

et al. 2020

J. Phys.: Conf. Ser.

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Electroencephalography (EEG) electrodes are divided into two types: dry and wet. In wet electrodes, a gel or saline liquid is used to increase the conductivity value, however, it tends to dry over time. On the contrary, gel or saline liquid is not required on the dry electrode. The use of certain types of electrode in EEG influences Power Spectral Density (PSD) of brain signals. Therefore, this study compared the EEG signal produced by Emotiv using the two different types of electrodes and a specific stimulus. This study was conducted using Emotiv EPOC (wet) and Insight (dry). The Temporal (T7 and T8) and Anterior Frontal (AF3 and AF4) electrodes from both devices were used to be compared. An audio stimulus that contains a story was chosen followed by a small task related to the story. The data acquisition was performed in 36 minutes to maintain the stability of the two types of electrodes. Three minutes of recordings in resting condition were conducted on both before and after given audio stimulus. Audio stimulation followed by a small task was performed for thirty minutes. The data analysis has consisted of EEG data pre-processing using centering and filtering the data, followed by PSD calculation using the Welch periodogram with Hanning window. The result showed that the decrement percentage of PSD after stimulation from wet was higher than dry electrodes.

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The development of Arduino-based low-cost wireless modular device for brainwave acquisition

Cited by 4 publications

References 14 publications

Measuring Biosignals with Single Circuit Boards

Measuring Biosignals with Single Circuit Boards

Arduino-Based Readout Electronics for Nuclear and Particle Physics

Comparison of Wet and Dry EEG Electrodes Based On Brain Signals Characterization In Temporal and Anterior Frontal Areas Using Audio Stimulation

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