EEG data acquisition system 32 channels based on Raspberry Pi with relative power ratio and brain symmetry index features

Henry Hendarwin; Sastra Kusuma Wijaya; Prawito Prajitno; Nurhadi Ibrahim; Hendra Saputra Gani; Wahyu Apriadi

doi:10.1063/1.5139376

EEG data acquisition system 32 channels based on Raspberry Pi with relative power ratio and brain symmetry index features

Henry Hendarwin, Sastra Kusuma Wijaya, Prawito Prajitno, Nurhadi Ibrahim, Hendra Saputra Gani, Wahyu Apriadi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

An inhouse Electroencephalography (EEG) data acquisition system based on Raspberry Pi and Analog Front End (AFE) ADS1299 EEGFE-PDK has been developed. The system displayed a relative power ratio (RPR) and brain symmetry index (BSI) in real-time. The features of the AFE are simultaneous sampling for eight channels, 24 bits resolution, low power, and low noise of < 5 mW and < 1 μV, respectively. The system consists of 4 units AFE in daisy chain configuration. The communication between AFE and Raspberry Pi is programmable using registers of RDATA format accessed via a serial peripheral interface (SPI) and programmed using C. The data acquired were processed using MATLAB. These data were transferred using Local Area Networking (LAN) filtered based on 5^th order Butterworth and processed in a personal computer (PC). The RPR was calculated using Fast Fourier Transforms (FFT) and Power Spectral Density (PSD). The BSI was calculated using Welch method. The acquired and processed data would be sent to High Definition Multiple Interface (HDMI) if needed by users. This system has been evaluated using EEG simulator (NETECH MiniSim EEG), which is generate sinusoidal electrical signal with frequency 2 Hz, 5 Hz, and voltage amplitude 30, 50 μV, with error average less than 6%.

Original language	English
Title of host publication	4th Biomedical Engineering''s Recent Progress in Biomaterials, Drugs Development, Health, and Medical Devices
Subtitle of host publication	Proceedings of the International Symposium of Biomedical Engineering, ISBE 2019
Editors	Kenny Lischer, Tomy Abuzairi, Siti Fauziyah Rahman, Misri Gozan
Publisher	American Institute of Physics Inc.
ISBN (Electronic)	9780735419445
DOIs	https://doi.org/10.1063/1.5139376
Publication status	Published - 10 Dec 2019
Event	4th International Symposium of Biomedical Engineering�s Recent Progress in Biomaterials, Drugs Development, Health, and Medical Devices, ISBE 2019 - Padang, West Sumatera, Indonesia Duration: 22 Jul 2019 → 24 Jul 2019

Publication series

Name	AIP Conference Proceedings
Volume	2193
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	4th International Symposium of Biomedical Engineering�s Recent Progress in Biomaterials, Drugs Development, Health, and Medical Devices, ISBE 2019
Country/Territory	Indonesia
City	Padang, West Sumatera
Period	22/07/19 → 24/07/19

Keywords

ADS1299
BSI
Daisy Chain
EEG
Raspberry Pi
RPR

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10.1063/1.5139376

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Hendarwin, H., Wijaya, S. K., Prajitno, P., Ibrahim, N., Gani, H. S., & Apriadi, W. (2019). EEG data acquisition system 32 channels based on Raspberry Pi with relative power ratio and brain symmetry index features. In K. Lischer, T. Abuzairi, S. F. Rahman, & M. Gozan (Eds.), 4th Biomedical Engineering''s Recent Progress in Biomaterials, Drugs Development, Health, and Medical Devices: Proceedings of the International Symposium of Biomedical Engineering, ISBE 2019 Article 050003 (AIP Conference Proceedings; Vol. 2193). American Institute of Physics Inc.. https://doi.org/10.1063/1.5139376

Hendarwin, Henry ; Wijaya, Sastra Kusuma ; Prajitno, Prawito et al. / EEG data acquisition system 32 channels based on Raspberry Pi with relative power ratio and brain symmetry index features. 4th Biomedical Engineering''s Recent Progress in Biomaterials, Drugs Development, Health, and Medical Devices: Proceedings of the International Symposium of Biomedical Engineering, ISBE 2019. editor / Kenny Lischer ; Tomy Abuzairi ; Siti Fauziyah Rahman ; Misri Gozan. American Institute of Physics Inc., 2019. (AIP Conference Proceedings).

@inproceedings{ed91cdf12ccc495da3588c58f7bc6eca,

title = "EEG data acquisition system 32 channels based on Raspberry Pi with relative power ratio and brain symmetry index features",

abstract = "An inhouse Electroencephalography (EEG) data acquisition system based on Raspberry Pi and Analog Front End (AFE) ADS1299 EEGFE-PDK has been developed. The system displayed a relative power ratio (RPR) and brain symmetry index (BSI) in real-time. The features of the AFE are simultaneous sampling for eight channels, 24 bits resolution, low power, and low noise of < 5 mW and < 1 μV, respectively. The system consists of 4 units AFE in daisy chain configuration. The communication between AFE and Raspberry Pi is programmable using registers of RDATA format accessed via a serial peripheral interface (SPI) and programmed using C. The data acquired were processed using MATLAB. These data were transferred using Local Area Networking (LAN) filtered based on 5th order Butterworth and processed in a personal computer (PC). The RPR was calculated using Fast Fourier Transforms (FFT) and Power Spectral Density (PSD). The BSI was calculated using Welch method. The acquired and processed data would be sent to High Definition Multiple Interface (HDMI) if needed by users. This system has been evaluated using EEG simulator (NETECH MiniSim EEG), which is generate sinusoidal electrical signal with frequency 2 Hz, 5 Hz, and voltage amplitude 30, 50 μV, with error average less than 6%.",

keywords = "ADS1299, BSI, Daisy Chain, EEG, Raspberry Pi, RPR",

author = "Henry Hendarwin and Wijaya, {Sastra Kusuma} and Prawito Prajitno and Nurhadi Ibrahim and Gani, {Hendra Saputra} and Wahyu Apriadi",

note = "Publisher Copyright: {\textcopyright} 2019 Author(s).; 4th International Symposium of Biomedical Engineering�s Recent Progress in Biomaterials, Drugs Development, Health, and Medical Devices, ISBE 2019 ; Conference date: 22-07-2019 Through 24-07-2019",

year = "2019",

month = dec,

day = "10",

doi = "10.1063/1.5139376",

language = "English",

series = "AIP Conference Proceedings",

publisher = "American Institute of Physics Inc.",

editor = "Kenny Lischer and Tomy Abuzairi and Rahman, {Siti Fauziyah} and Misri Gozan",

booktitle = "4th Biomedical Engineering''s Recent Progress in Biomaterials, Drugs Development, Health, and Medical Devices",

address = "United States",

}

Hendarwin, H, Wijaya, SK, Prajitno, P , Ibrahim, N, Gani, HS & Apriadi, W 2019, EEG data acquisition system 32 channels based on Raspberry Pi with relative power ratio and brain symmetry index features. in K Lischer, T Abuzairi, SF Rahman & M Gozan (eds), 4th Biomedical Engineering''s Recent Progress in Biomaterials, Drugs Development, Health, and Medical Devices: Proceedings of the International Symposium of Biomedical Engineering, ISBE 2019., 050003, AIP Conference Proceedings, vol. 2193, American Institute of Physics Inc., 4th International Symposium of Biomedical Engineering�s Recent Progress in Biomaterials, Drugs Development, Health, and Medical Devices, ISBE 2019, Padang, West Sumatera, Indonesia, 22/07/19. https://doi.org/10.1063/1.5139376

EEG data acquisition system 32 channels based on Raspberry Pi with relative power ratio and brain symmetry index features. / Hendarwin, Henry; Wijaya, Sastra Kusuma; Prajitno, Prawito et al.
4th Biomedical Engineering''s Recent Progress in Biomaterials, Drugs Development, Health, and Medical Devices: Proceedings of the International Symposium of Biomedical Engineering, ISBE 2019. ed. / Kenny Lischer; Tomy Abuzairi; Siti Fauziyah Rahman; Misri Gozan. American Institute of Physics Inc., 2019. 050003 (AIP Conference Proceedings; Vol. 2193).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hendarwin, Henry

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AU - Prajitno, Prawito

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AU - Gani, Hendra Saputra

AU - Apriadi, Wahyu

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N2 - An inhouse Electroencephalography (EEG) data acquisition system based on Raspberry Pi and Analog Front End (AFE) ADS1299 EEGFE-PDK has been developed. The system displayed a relative power ratio (RPR) and brain symmetry index (BSI) in real-time. The features of the AFE are simultaneous sampling for eight channels, 24 bits resolution, low power, and low noise of < 5 mW and < 1 μV, respectively. The system consists of 4 units AFE in daisy chain configuration. The communication between AFE and Raspberry Pi is programmable using registers of RDATA format accessed via a serial peripheral interface (SPI) and programmed using C. The data acquired were processed using MATLAB. These data were transferred using Local Area Networking (LAN) filtered based on 5th order Butterworth and processed in a personal computer (PC). The RPR was calculated using Fast Fourier Transforms (FFT) and Power Spectral Density (PSD). The BSI was calculated using Welch method. The acquired and processed data would be sent to High Definition Multiple Interface (HDMI) if needed by users. This system has been evaluated using EEG simulator (NETECH MiniSim EEG), which is generate sinusoidal electrical signal with frequency 2 Hz, 5 Hz, and voltage amplitude 30, 50 μV, with error average less than 6%.

AB - An inhouse Electroencephalography (EEG) data acquisition system based on Raspberry Pi and Analog Front End (AFE) ADS1299 EEGFE-PDK has been developed. The system displayed a relative power ratio (RPR) and brain symmetry index (BSI) in real-time. The features of the AFE are simultaneous sampling for eight channels, 24 bits resolution, low power, and low noise of < 5 mW and < 1 μV, respectively. The system consists of 4 units AFE in daisy chain configuration. The communication between AFE and Raspberry Pi is programmable using registers of RDATA format accessed via a serial peripheral interface (SPI) and programmed using C. The data acquired were processed using MATLAB. These data were transferred using Local Area Networking (LAN) filtered based on 5th order Butterworth and processed in a personal computer (PC). The RPR was calculated using Fast Fourier Transforms (FFT) and Power Spectral Density (PSD). The BSI was calculated using Welch method. The acquired and processed data would be sent to High Definition Multiple Interface (HDMI) if needed by users. This system has been evaluated using EEG simulator (NETECH MiniSim EEG), which is generate sinusoidal electrical signal with frequency 2 Hz, 5 Hz, and voltage amplitude 30, 50 μV, with error average less than 6%.

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KW - Daisy Chain

KW - EEG

KW - Raspberry Pi

KW - RPR

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Hendarwin H, Wijaya SK, Prajitno P , Ibrahim N, Gani HS, Apriadi W. EEG data acquisition system 32 channels based on Raspberry Pi with relative power ratio and brain symmetry index features. In Lischer K, Abuzairi T, Rahman SF, Gozan M, editors, 4th Biomedical Engineering''s Recent Progress in Biomaterials, Drugs Development, Health, and Medical Devices: Proceedings of the International Symposium of Biomedical Engineering, ISBE 2019. American Institute of Physics Inc. 2019. 050003. (AIP Conference Proceedings). doi: 10.1063/1.5139376

EEG data acquisition system 32 channels based on Raspberry Pi with relative power ratio and brain symmetry index features

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