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

Yıl 2022, Cilt: 12 Sayı: 1, 182 - 193, 01.03.2022
https://doi.org/10.21597/jist.998055

Öz

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, analog-digital 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.

Destekleyen Kurum

BALIKESİR UNİVERSİTESİ BAP

Proje Numarası

2017/169

Teşekkür

This study was supported by the Balıkesir University Research Fund (project number 2017/169). Authors would like to thank to Turkish Academy of Sciences (TUBA) for partial support.

Kaynakça

  • Balamurugan B, Mullai M, Soundararajan S, Selvakanmani S, Arun D, 2020. Brain–computer interface for assessment of mental efforts in e-learning using the nonmarkovian queueing model. Computer Applications in Engineering Education 29(2):394–410.
  • Bozomitu RG, Păsărică A, Tărniceriu D, Rotariu C, 2019. Development of an Eye Tracking-Based Human-Computer Interface for Real-Time Applications. Sensors 19(16):3630.
  • Cavanagh JF, Napolitano A, Wu C, Mueen A, 2017. The Patient Repository for EEG Data + Computational Tools (PRED+CT). Frontiers in neuroinformatics 11:67.
  • Chang W-D, 2019. Electrooculograms for Human–Computer Interaction: A Review. Sensors 19(12):2690.
  • Collins T, Woolley SI, Rawson NC, Haroon L, 2016. Final-year projects using open source OpenEEG. Computer Applications in Engineering Education 24(1):156–164.
  • Farooq U, Ghani U, Usama SA, Neelum YS, 2019. {EMG} control of a 3D printed myo electric prosthetic hand. {IOP} Conference Series: Materials Science and Engineering 635:12022.
  • Gordleeva SY, Lobov SA, Grigorev NA, Savosenkov AO, Shamshin MO, Lukoyanov M V, Khoruzhko MA, Kazantsev VB, 2020. Real-Time EEG–EMG Human–Machine Interface-Based Control System for a Lower-Limb Exoskeleton. IEEE Access 8:84070–84081.
  • Hooda N, Das R, Kumar N, 2020. Fusion of EEG and EMG signals for classification of unilateral foot movements. Biomedical Signal Processing and Control 60:101990.
  • Hu J, Wang C, Wu M, Du Y, He Y, She J, 2015. Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system. Neurocomputing 151:278–287.
  • Kashou AH, Ko W-Y, Attia ZI, Cohen MS, Friedman PA, Noseworthy PA, 2020. A comprehensive artificial intelligence–enabled electrocardiogram interpretation program. Cardiovascular Digital Health Journal 1(2):62–70.
  • Li K, Ramkumar S, Thimmiaraja J, Diwakaran S, 2020. Optimized artificial neural network based performance analysis of wheelchair movement for ALS patients. Artificial Intelligence in Medicine 102:101754.
  • Martínez-Cerveró J, Ardali MK, Jaramillo-Gonzalez A, Wu S, Tonin A, Birbaumer N, Chaudhary U, 2020. Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification. Sensors 20(9):2443.
  • Montironi MA, Qian B, Cheng HH, 2017. Development and application of the ChArduino toolkit for teaching how to program Arduino boards through the C/C++ interpreter Ch. Computer Applications in Engineering Education 25(6):1053–1065.
  • Panganiban EB, Paglinawan AC, Chung WY, Paa GLS, 2021. ECG diagnostic support system (EDSS): A deep learning neural network based classification system for detecting ECG abnormal rhythms from a low-powered wearable biosensors. Sensing and Bio-Sensing Research 31:100398.
  • Papazoglou PM, 2018. A hybrid simulation platform for learning microprocessors. Computer Applications in Engineering Education 26(3):655–674.
  • Perenc I, Jaworski T, Duch P, 2019. Teaching programming using dedicated Arduino Educational Board. Computer Applications in Engineering Education 27(4):943–954.
  • Ramakrishnan J, Mavaluru D, Sakthivel RS, Alqahtani AS, Mubarakali A, Retnadhas M, 2020. Brain–computer interface for amyotrophic lateral sclerosis patients using deep learning network. Neural Computing and Applications 2020(5):1–15.
  • Teng G, He Y, Zhao H, Liu D, Xiao J, Ramkumar S, 2020. Design and Development of Human Computer Interface Using Electrooculogram with Deep Learning. Artificial Intelligence in Medicine 102:101765.
  • Usakli AB, Gurkan S, 2010. Design of a Novel Efficient Human–Computer Interface: An Electrooculagram Based Virtual Keyboard. IEEE Transactions on Instrumentation and Measurement 59(8):2099–2108.
  • Usakli AB, Susac A, Gurkan S, 2011. Fast face recognition: Eye blink as a reliable behavioral response. Neuroscience Letters 504(1):49–52.
  • Usakli AB, Gurkan S, Gurkan G, Kaya A, 2018. A novel EOG-based wireless rapid communication device for people with motor neuron diseases. Journal of Medical Engineering & Technology 42(6):420–425.
  • Uyanik I, Catalbas B, 2018. A low-cost feedback control systems laboratory setup via Arduino–Simulink interface. Computer Applications in Engineering Education 26(3):718–726.
  • Zhang P, 2010. Human–machine interfaces. Advanced Industrial Control Technology 2010(1):527–555.
Yıl 2022, Cilt: 12 Sayı: 1, 182 - 193, 01.03.2022
https://doi.org/10.21597/jist.998055

Öz

Proje Numarası

2017/169

Kaynakça

  • Balamurugan B, Mullai M, Soundararajan S, Selvakanmani S, Arun D, 2020. Brain–computer interface for assessment of mental efforts in e-learning using the nonmarkovian queueing model. Computer Applications in Engineering Education 29(2):394–410.
  • Bozomitu RG, Păsărică A, Tărniceriu D, Rotariu C, 2019. Development of an Eye Tracking-Based Human-Computer Interface for Real-Time Applications. Sensors 19(16):3630.
  • Cavanagh JF, Napolitano A, Wu C, Mueen A, 2017. The Patient Repository for EEG Data + Computational Tools (PRED+CT). Frontiers in neuroinformatics 11:67.
  • Chang W-D, 2019. Electrooculograms for Human–Computer Interaction: A Review. Sensors 19(12):2690.
  • Collins T, Woolley SI, Rawson NC, Haroon L, 2016. Final-year projects using open source OpenEEG. Computer Applications in Engineering Education 24(1):156–164.
  • Farooq U, Ghani U, Usama SA, Neelum YS, 2019. {EMG} control of a 3D printed myo electric prosthetic hand. {IOP} Conference Series: Materials Science and Engineering 635:12022.
  • Gordleeva SY, Lobov SA, Grigorev NA, Savosenkov AO, Shamshin MO, Lukoyanov M V, Khoruzhko MA, Kazantsev VB, 2020. Real-Time EEG–EMG Human–Machine Interface-Based Control System for a Lower-Limb Exoskeleton. IEEE Access 8:84070–84081.
  • Hooda N, Das R, Kumar N, 2020. Fusion of EEG and EMG signals for classification of unilateral foot movements. Biomedical Signal Processing and Control 60:101990.
  • Hu J, Wang C, Wu M, Du Y, He Y, She J, 2015. Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system. Neurocomputing 151:278–287.
  • Kashou AH, Ko W-Y, Attia ZI, Cohen MS, Friedman PA, Noseworthy PA, 2020. A comprehensive artificial intelligence–enabled electrocardiogram interpretation program. Cardiovascular Digital Health Journal 1(2):62–70.
  • Li K, Ramkumar S, Thimmiaraja J, Diwakaran S, 2020. Optimized artificial neural network based performance analysis of wheelchair movement for ALS patients. Artificial Intelligence in Medicine 102:101754.
  • Martínez-Cerveró J, Ardali MK, Jaramillo-Gonzalez A, Wu S, Tonin A, Birbaumer N, Chaudhary U, 2020. Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification. Sensors 20(9):2443.
  • Montironi MA, Qian B, Cheng HH, 2017. Development and application of the ChArduino toolkit for teaching how to program Arduino boards through the C/C++ interpreter Ch. Computer Applications in Engineering Education 25(6):1053–1065.
  • Panganiban EB, Paglinawan AC, Chung WY, Paa GLS, 2021. ECG diagnostic support system (EDSS): A deep learning neural network based classification system for detecting ECG abnormal rhythms from a low-powered wearable biosensors. Sensing and Bio-Sensing Research 31:100398.
  • Papazoglou PM, 2018. A hybrid simulation platform for learning microprocessors. Computer Applications in Engineering Education 26(3):655–674.
  • Perenc I, Jaworski T, Duch P, 2019. Teaching programming using dedicated Arduino Educational Board. Computer Applications in Engineering Education 27(4):943–954.
  • Ramakrishnan J, Mavaluru D, Sakthivel RS, Alqahtani AS, Mubarakali A, Retnadhas M, 2020. Brain–computer interface for amyotrophic lateral sclerosis patients using deep learning network. Neural Computing and Applications 2020(5):1–15.
  • Teng G, He Y, Zhao H, Liu D, Xiao J, Ramkumar S, 2020. Design and Development of Human Computer Interface Using Electrooculogram with Deep Learning. Artificial Intelligence in Medicine 102:101765.
  • Usakli AB, Gurkan S, 2010. Design of a Novel Efficient Human–Computer Interface: An Electrooculagram Based Virtual Keyboard. IEEE Transactions on Instrumentation and Measurement 59(8):2099–2108.
  • Usakli AB, Susac A, Gurkan S, 2011. Fast face recognition: Eye blink as a reliable behavioral response. Neuroscience Letters 504(1):49–52.
  • Usakli AB, Gurkan S, Gurkan G, Kaya A, 2018. A novel EOG-based wireless rapid communication device for people with motor neuron diseases. Journal of Medical Engineering & Technology 42(6):420–425.
  • Uyanik I, Catalbas B, 2018. A low-cost feedback control systems laboratory setup via Arduino–Simulink interface. Computer Applications in Engineering Education 26(3):718–726.
  • Zhang P, 2010. Human–machine interfaces. Advanced Industrial Control Technology 2010(1):527–555.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Elektrik Elektronik Mühendisliği / Electrical Electronic Engineering
Yazarlar

Poyraz Alper Öner 0000-0001-5490-4599

Serkan Gürkan 0000-0003-2229-3361

Mustafa Karapınar 0000-0002-1953-6804

Seydi Doğan 0000-0001-9785-4990

Proje Numarası 2017/169
Yayımlanma Tarihi 1 Mart 2022
Gönderilme Tarihi 20 Eylül 2021
Kabul Tarihi 11 Ekim 2021
Yayımlandığı Sayı Yıl 2022 Cilt: 12 Sayı: 1

Kaynak Göster

APA Öner, P. A., Gürkan, S., Karapınar, M., Doğan, S. (2022). Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications. Journal of the Institute of Science and Technology, 12(1), 182-193. https://doi.org/10.21597/jist.998055
AMA Öner PA, Gürkan S, Karapınar M, Doğan S. Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications. Iğdır Üniv. Fen Bil Enst. Der. Mart 2022;12(1):182-193. doi:10.21597/jist.998055
Chicago Öner, Poyraz Alper, Serkan Gürkan, Mustafa Karapınar, ve Seydi Doğan. “Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications”. Journal of the Institute of Science and Technology 12, sy. 1 (Mart 2022): 182-93. https://doi.org/10.21597/jist.998055.
EndNote Öner PA, Gürkan S, Karapınar M, Doğan S (01 Mart 2022) Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications. Journal of the Institute of Science and Technology 12 1 182–193.
IEEE P. A. Öner, S. Gürkan, M. Karapınar, ve S. Doğan, “Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications”, Iğdır Üniv. Fen Bil Enst. Der., c. 12, sy. 1, ss. 182–193, 2022, doi: 10.21597/jist.998055.
ISNAD Öner, Poyraz Alper vd. “Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications”. Journal of the Institute of Science and Technology 12/1 (Mart 2022), 182-193. https://doi.org/10.21597/jist.998055.
JAMA Öner PA, Gürkan S, Karapınar M, Doğan S. Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications. Iğdır Üniv. Fen Bil Enst. Der. 2022;12:182–193.
MLA Öner, Poyraz Alper vd. “Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications”. Journal of the Institute of Science and Technology, c. 12, sy. 1, 2022, ss. 182-93, doi:10.21597/jist.998055.
Vancouver Öner PA, Gürkan S, Karapınar M, Doğan S. Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications. Iğdır Üniv. Fen Bil Enst. Der. 2022;12(1):182-93.