Final‐year projects using open source OpenEEG

Collins, Tim; Woolley, Sandra; Rawson, Nicholas C.; Haroon, Luth

doi:10.1002/cae.21682

Cited by 5 publications

(4 citation statements)

References 20 publications

(18 reference statements)

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“…As a result of the measurements conducted via 15 channels, it is observed that the EEG signals can be measured successfully, the obtained EEG signals are compatible with those given in the literature, and the moment of fist-clenching can be detected. Results clearly show that the obtained signals can be used in HCI and BCI systems (Hu et al, 2015;Collins et al, 2016;Cavanagh et al, 2017;Gordleeva et al, 2020;Hooda et al, 2020).…”

Section: Measurement and Evaluation Of Eeg Signalsmentioning

confidence: 87%

“…Although there have been given EEG, EMG, EOG, EEG-EMG, EOG-EMG-based biosignal data capture applications developed for commercial or academic purposes in the literature, there isn't any study that allows the simultaneous evaluation of EEG, EMG, EOG and ECG biosignals together (Collins et al, 2016;Balamurugan et al, 2020;Teng et al, 2020). Researchers working in the field of biosignal processing and HCI may also prefer to work with data they have purchased from database banks or accessed from open-source databases, due to difficulties and costs of obtaining biosignals from different sources (Cavanagh et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications

ÖNER¹,

Gürkan

KARAPINAR³

et al. 2022

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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A multi-channel measurement system used to measure electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG) and electrooculogram (EOG) biosignals has been designed and prototyped. The designed system has 16 configurable measurement channels. Of the 16 channels the developed system has, 8 have been designed for EEG, 2 for EMG, 2 for EOG, 1 for ECG measurements, the remaining 3 have been reserved as backup channels. In circuit design, biosignal amplifier design principles have been applied by taking into account the characteristics of the biosignal to be measured for each channel, such as bandwidth, frequency, amplitude, noise level. Modules such as instrumentation amplifier, filter, DC suppression unit, amplifier, DC level determination unit, analogdigital converter, optical isolation unit, power supply have been designed to perform biosignal measurements through these channels. Biosignals measured by the developed system can be shifted to the desired threshold level with the help of the analog output reference voltage, converted to digital data 10-bit resolution and transferred to the computer environment in real time. The data transferred to the computer can be used in C#, Excel, MATLAB, and LabVIEW platforms. The novelty of the developed system is that any of the four desired biosignal types can be measured from any channel. In addition, another feature of the system is that it can work with real-time data without being dependent on the databases serving for human-computer interface applications. In experimental studies with some researchers for the performance tests of the system, ECG, EEG, EMG and EOG signals have been recorded with different module configurations, and signal processing stages were carried out to be used for human-computer applications.

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Section: Measurement and Evaluation Of Eeg Signalsmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications

ÖNER¹,

Gürkan

KARAPINAR³

et al. 2022

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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“…Collins et al introduced the OpenEEG project to 3rd and 4th year engineering students to teach hardware and software systems used in EEG signal acquisition and increase interest in brain-computer interfaces (BCI). When surveyed, OpenEEG increased student interest in EEG signals [6]. Suzuki et al created an experiment to measure ECG signals in undergraduate biomedical laboratories.…”

Section: Introductionmentioning

confidence: 99%

A computer‐aided educational device and toolbox for biomedical signal processing at the undergraduate level

Tepe

Savaştaer

2022

Comp Applic In Engineering

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Engineering education requires both a theoretical and practical basis; some aspects are better understood through practical examples. Providing practical experience in biomedical engineering is of great importance in terms of understanding concepts and assisting the retention of learning. Hands‐on experience can be particularly beneficial in supporting the theoretical explanation of the acquisition, processing, and displaying of biomedical signals. This study has developed an expansion board that can be plugged into Raspberry Pi boards to capture electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG) biomedical signals with a single‐channel amplifier circuit. A toolbox has been written in the Python programming language to; capture, process, filter, apply fast fourier transform, and display these signals. The device and laboratory applications were introduced in the curriculum of a final year module, “Introduction to Biomedical Engineering” in the Department of Electrical and Electronics Engineering at Gazi University. The pedagogical value of the application was assessed by administering a questionnaire to the students. Results show the use of measurement and display of biomedical signals increased the curiosity and motivation of the students in Biomedical Engineering.

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“…These platforms have gained popularity in other engineering fields for developing both applications and educational frameworks. Formally, academic educational research related to Robotics [10][11], Architecture [12], Electronics [13][14] and Control engineering [15][16] similarly prove an increasing record of academic activity in several other engineering fields with particular educational targets.…”

Section: Introductionmentioning

confidence: 99%

From physical to digital in structural engineering classrooms using digital fabrication

Chacón

Codony

Toledo

2017

Comp Applic In Engineering

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This is the peer reviewed version of the following article: [ Chacón R, Codony D, Toledo Á. From physical to digital in structural engineering classrooms using digital fabrication. Comput appl eng educ. 2017;25:927–937. https://doi.org/10.1002/cae.21845 ], which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/cae.21845/full. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.In this paper, a set of digital artifacts related to simple examples of structural engineering are presented. The artifacts are real-time applications and visualizations of typical problems students from the architecture, engineering, and construction (AEC) schools are acquainted with. The real-time nature of the examples allow a high level of interaction between humans and the classic visualization of results, namely, bending and shear force diagrams, internal stresses distributions, and contour plots. These artifacts may provide in AEC a twofold educational target: (i) for users, to provide visual understanding in real time of typical problems that must be understood in classic lectures of structural engineering; (ii) for developers, to provide meaningful applications of applied digital fabrication using sensors, microcontrollers, and GUI's and their potential in the development of tools related to Structural Health Monitoring (SHM) and the Internet of Things (IoT) among students of the AEC sector.Peer ReviewedPostprint (author's final draft

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Final‐year projects using open source OpenEEG

Cited by 5 publications

References 20 publications

Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications

Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications

A computer‐aided educational device and toolbox for biomedical signal processing at the undergraduate level

From physical to digital in structural engineering classrooms using digital fabrication

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