Development of Real-Time EEG Application on an Ultra-Low-Power DSP

S. Cirit; J. Penders; M. Ashouei; J. Hulzink; M. de Nil; J. Huisken; U. G. Hofmann

Development of Real-Time EEG Application on an Ultra-Low-Power DSP

S. Cirit, J. Penders, M. Ashouei, J. Hulzink, M. de Nil, J. Huisken, U. G. Hofmann

Institute of Signal Processing

Abstract

Autonomous wireless devices for healthcare monitoring become a reality only if such devices are embedded with enough intelligent and processing capabilities to minimize the amount of data being transferred through the wireless network. Also, the embedded processing capability must be made energy efficient so that the device can operate on scavenged energy or very limited battery power. This paper reports the development of a real-time EEG (Electroencephalography) application based on DWT (Discrete Wavelet Transform) and its mapping and optimization on an Application Specific Instruction set Processor (ASIP). It shows the drastic energy reduction that can be achieved by cross-optimization of the algorithm and the ASIP architecture. Our results indicate that such cross-optimization can reduced the dynamic energy by more than 80%.

Original language	English
Pages	1-6
Number of pages	6
Publication status	Published - 01.07.2009
Event	2009 International Symposium on Code Generation and Optimization - Seattle, United States Duration: 22.03.2009 → 25.03.2009 http://cgo.org/cgo2009/

Conference

Conference	2009 International Symposium on Code Generation and Optimization
Abbreviated title	CGO 2009
Country/Territory	United States
City	Seattle
Period	22.03.09 → 25.03.09
Internet address	http://cgo.org/cgo2009/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.302.7650&rep=rep1&type=pdf

Cite this

Cirit, S., Penders, J., Ashouei, M., Hulzink, J., de Nil, M., Huisken, J., & Hofmann, U. G. (2009). Development of Real-Time EEG Application on an Ultra-Low-Power DSP. 1-6. Paper presented at 2009 International Symposium on Code Generation and Optimization , Seattle, Washington, United States. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.302.7650&rep=rep1&type=pdf

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title = "Development of Real-Time EEG Application on an Ultra-Low-Power DSP",

abstract = "Autonomous wireless devices for healthcare monitoring become a reality only if such devices are embedded with enough intelligent and processing capabilities to minimize the amount of data being transferred through the wireless network. Also, the embedded processing capability must be made energy efficient so that the device can operate on scavenged energy or very limited battery power. This paper reports the development of a real-time EEG (Electroencephalography) application based on DWT (Discrete Wavelet Transform) and its mapping and optimization on an Application Specific Instruction set Processor (ASIP). It shows the drastic energy reduction that can be achieved by cross-optimization of the algorithm and the ASIP architecture. Our results indicate that such cross-optimization can reduced the dynamic energy by more than 80%.",

author = "S. Cirit and J. Penders and M. Ashouei and J. Hulzink and {de Nil}, M. and J. Huisken and Hofmann, {U. G.}",

year = "2009",

month = jul,

day = "1",

language = "English",

pages = "1--6",

note = " 2009 International Symposium on Code Generation and Optimization , CGO 2009 ; Conference date: 22-03-2009 Through 25-03-2009",

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}

Cirit, S, Penders, J, Ashouei, M, Hulzink, J, de Nil, M, Huisken, J & Hofmann, UG 2009, 'Development of Real-Time EEG Application on an Ultra-Low-Power DSP', Paper presented at 2009 International Symposium on Code Generation and Optimization , Seattle, United States, 22.03.09 - 25.03.09 pp. 1-6. <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.302.7650&rep=rep1&type=pdf>

TY - CONF

T1 - Development of Real-Time EEG Application on an Ultra-Low-Power DSP

AU - Cirit, S.

AU - Penders, J.

AU - Ashouei, M.

AU - Hulzink, J.

AU - de Nil, M.

AU - Huisken, J.

AU - Hofmann, U. G.

PY - 2009/7/1

Y1 - 2009/7/1

N2 - Autonomous wireless devices for healthcare monitoring become a reality only if such devices are embedded with enough intelligent and processing capabilities to minimize the amount of data being transferred through the wireless network. Also, the embedded processing capability must be made energy efficient so that the device can operate on scavenged energy or very limited battery power. This paper reports the development of a real-time EEG (Electroencephalography) application based on DWT (Discrete Wavelet Transform) and its mapping and optimization on an Application Specific Instruction set Processor (ASIP). It shows the drastic energy reduction that can be achieved by cross-optimization of the algorithm and the ASIP architecture. Our results indicate that such cross-optimization can reduced the dynamic energy by more than 80%.

AB - Autonomous wireless devices for healthcare monitoring become a reality only if such devices are embedded with enough intelligent and processing capabilities to minimize the amount of data being transferred through the wireless network. Also, the embedded processing capability must be made energy efficient so that the device can operate on scavenged energy or very limited battery power. This paper reports the development of a real-time EEG (Electroencephalography) application based on DWT (Discrete Wavelet Transform) and its mapping and optimization on an Application Specific Instruction set Processor (ASIP). It shows the drastic energy reduction that can be achieved by cross-optimization of the algorithm and the ASIP architecture. Our results indicate that such cross-optimization can reduced the dynamic energy by more than 80%.

UR - https://www.semanticscholar.org/paper/Development-of-Real-Time-EEG-Application-on-an-DSP-Cirit-Penders/ac4384a383f9108f4455b8122847b31dd2966442

M3 - Conference Papers

SP - 1

EP - 6

T2 - 2009 International Symposium on Code Generation and Optimization

Y2 - 22 March 2009 through 25 March 2009

ER -

Development of Real-Time EEG Application on an Ultra-Low-Power DSP

Abstract

Conference

UN SDGs

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