Evaluation of different signal processing methods in time and frequency domain for brain-computer interface applications

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

Brain-computer interface (BCI) has been widely introduced in many medical applications. One of the main challenges in BCI is to run the signal processing algorithms in real-time which is challenging and usually comes with high processing unit costs. BCIs based on motor imagery task are introduced for severe neurological diseases especially locked-in patients. A common concept is to detect one’s movement intention and use it to control external devices such as wheelchair or rehabilitation devices. In real-time BCI, running the signal processing algorithms might not always be possible due to the complexity of the algorithms. Moreover, the speed of the affordable computational units is not usually enough for those applications. This study evaluated a range of feature extraction methods which are commonly used for such realtime BCI applications. Electroencephalogram (EEG) and Electrooculogram (EOG) data available through IEEE Brain Initiative repository was used to investigate the performance of different feature extraction methods including template matching, statistical moments, selective bandpower, and fast Fourier transform (FFT) power spectrum. The support vector machine (SVM) was used for classification. The result indicates that there is not a significant difference when utilizing different feature extraction methods in terms of movement prediction although there is a vast difference in the computational time needed to extract these features. The results suggest that computational time could be considered as the primary parameter when choosing the feature extraction methods as there is no significant difference between the results when different features extraction methods are used.

Conference

Conference40th International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated titleEMBC 2018
CountryUnited States
CityHonolulu, Hawaii
Period17/07/1821/07/18
Internet address

Fingerprint

Brain computer interface
Feature extraction
Signal processing
Template matching
Wheelchairs
Medical applications
Electroencephalography
Power spectrum
Patient rehabilitation
Fast Fourier transforms
Support vector machines
Brain
Processing
Costs

Keywords

  • signal processing
  • brain-computer interfaces
  • BCI systems
  • medicine
  • electroencephalogram
  • EEG

Cite this

Arnin, J., Kahani, D., Lakany, H., & Conway, B. A. (2018). Evaluation of different signal processing methods in time and frequency domain for brain-computer interface applications. Paper presented at 40th International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, Hawaii, United States.
Arnin, J. ; Kahani, D. ; Lakany, H. ; Conway, B. A. / Evaluation of different signal processing methods in time and frequency domain for brain-computer interface applications. Paper presented at 40th International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, Hawaii, United States.4 p.
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abstract = "Brain-computer interface (BCI) has been widely introduced in many medical applications. One of the main challenges in BCI is to run the signal processing algorithms in real-time which is challenging and usually comes with high processing unit costs. BCIs based on motor imagery task are introduced for severe neurological diseases especially locked-in patients. A common concept is to detect one’s movement intention and use it to control external devices such as wheelchair or rehabilitation devices. In real-time BCI, running the signal processing algorithms might not always be possible due to the complexity of the algorithms. Moreover, the speed of the affordable computational units is not usually enough for those applications. This study evaluated a range of feature extraction methods which are commonly used for such realtime BCI applications. Electroencephalogram (EEG) and Electrooculogram (EOG) data available through IEEE Brain Initiative repository was used to investigate the performance of different feature extraction methods including template matching, statistical moments, selective bandpower, and fast Fourier transform (FFT) power spectrum. The support vector machine (SVM) was used for classification. The result indicates that there is not a significant difference when utilizing different feature extraction methods in terms of movement prediction although there is a vast difference in the computational time needed to extract these features. The results suggest that computational time could be considered as the primary parameter when choosing the feature extraction methods as there is no significant difference between the results when different features extraction methods are used.",
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author = "J. Arnin and D. Kahani and H. Lakany and Conway, {B. A.}",
note = "{\circledC} 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; 40th International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 ; Conference date: 17-07-2018 Through 21-07-2018",
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Arnin, J, Kahani, D, Lakany, H & Conway, BA 2018, 'Evaluation of different signal processing methods in time and frequency domain for brain-computer interface applications' Paper presented at 40th International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, Hawaii, United States, 17/07/18 - 21/07/18, .

Evaluation of different signal processing methods in time and frequency domain for brain-computer interface applications. / Arnin, J.; Kahani, D.; Lakany, H.; Conway, B. A.

2018. Paper presented at 40th International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, Hawaii, United States.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Evaluation of different signal processing methods in time and frequency domain for brain-computer interface applications

AU - Arnin, J.

AU - Kahani, D.

AU - Lakany, H.

AU - Conway, B. A.

N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2018/7/21

Y1 - 2018/7/21

N2 - Brain-computer interface (BCI) has been widely introduced in many medical applications. One of the main challenges in BCI is to run the signal processing algorithms in real-time which is challenging and usually comes with high processing unit costs. BCIs based on motor imagery task are introduced for severe neurological diseases especially locked-in patients. A common concept is to detect one’s movement intention and use it to control external devices such as wheelchair or rehabilitation devices. In real-time BCI, running the signal processing algorithms might not always be possible due to the complexity of the algorithms. Moreover, the speed of the affordable computational units is not usually enough for those applications. This study evaluated a range of feature extraction methods which are commonly used for such realtime BCI applications. Electroencephalogram (EEG) and Electrooculogram (EOG) data available through IEEE Brain Initiative repository was used to investigate the performance of different feature extraction methods including template matching, statistical moments, selective bandpower, and fast Fourier transform (FFT) power spectrum. The support vector machine (SVM) was used for classification. The result indicates that there is not a significant difference when utilizing different feature extraction methods in terms of movement prediction although there is a vast difference in the computational time needed to extract these features. The results suggest that computational time could be considered as the primary parameter when choosing the feature extraction methods as there is no significant difference between the results when different features extraction methods are used.

AB - Brain-computer interface (BCI) has been widely introduced in many medical applications. One of the main challenges in BCI is to run the signal processing algorithms in real-time which is challenging and usually comes with high processing unit costs. BCIs based on motor imagery task are introduced for severe neurological diseases especially locked-in patients. A common concept is to detect one’s movement intention and use it to control external devices such as wheelchair or rehabilitation devices. In real-time BCI, running the signal processing algorithms might not always be possible due to the complexity of the algorithms. Moreover, the speed of the affordable computational units is not usually enough for those applications. This study evaluated a range of feature extraction methods which are commonly used for such realtime BCI applications. Electroencephalogram (EEG) and Electrooculogram (EOG) data available through IEEE Brain Initiative repository was used to investigate the performance of different feature extraction methods including template matching, statistical moments, selective bandpower, and fast Fourier transform (FFT) power spectrum. The support vector machine (SVM) was used for classification. The result indicates that there is not a significant difference when utilizing different feature extraction methods in terms of movement prediction although there is a vast difference in the computational time needed to extract these features. The results suggest that computational time could be considered as the primary parameter when choosing the feature extraction methods as there is no significant difference between the results when different features extraction methods are used.

KW - signal processing

KW - brain-computer interfaces

KW - BCI systems

KW - medicine

KW - electroencephalogram

KW - EEG

M3 - Paper

ER -

Arnin J, Kahani D, Lakany H, Conway BA. Evaluation of different signal processing methods in time and frequency domain for brain-computer interface applications. 2018. Paper presented at 40th International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, Hawaii, United States.