Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata
Abstract Brain–computer interfacing (BCI) has been the most researched technology in neuroprosthesis in the last two decades. Feature extractors and classifiers play an important role in BCI research for the generation of suitable control signals to drive an assistive device. Due to the high dimensi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bhattacharyya, Saugat [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
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| Anmerkung: |
© International Federation for Medical and Biological Engineering 2013 |
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| Übergeordnetes Werk: |
Enthalten in: Medical & biological engineering & computing - Springer Berlin Heidelberg, 1977, 52(2013), 2 vom: 29. Okt., Seite 131-139 |
|---|---|
| Übergeordnetes Werk: |
volume:52 ; year:2013 ; number:2 ; day:29 ; month:10 ; pages:131-139 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11517-013-1123-9 |
|---|
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OLC203869186X |
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| 520 | |a Abstract Brain–computer interfacing (BCI) has been the most researched technology in neuroprosthesis in the last two decades. Feature extractors and classifiers play an important role in BCI research for the generation of suitable control signals to drive an assistive device. Due to the high dimensionality of feature vectors in practical BCI systems, implantation of efficient feature selection algorithms has been an integral area of research in the past decade. This article proposes an efficient feature selection technique, realized by means of an evolutionary algorithm, which attempts to overcome some of the shortcomings of several state-of-the-art approaches in this field. The outlined scheme produces a subset of salient features which improves the classification accuracy while maintaining a trade-off with the computational speed of the complete scheme. For this purpose, an efficient memetic algorithm has also been proposed for the optimization purpose. Extensive experimental validations have been conducted on two real-world datasets to establish the efficacy of our approach. We have compared our approach to existing algorithms and have established the superiority of our algorithm to the rest. | ||
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| 700 | 1 | |a Tibarewala, D. N. |4 aut | |
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10.1007/s11517-013-1123-9 doi (DE-627)OLC203869186X (DE-He213)s11517-013-1123-9-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Bhattacharyya, Saugat verfasserin aut Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2013 Abstract Brain–computer interfacing (BCI) has been the most researched technology in neuroprosthesis in the last two decades. Feature extractors and classifiers play an important role in BCI research for the generation of suitable control signals to drive an assistive device. Due to the high dimensionality of feature vectors in practical BCI systems, implantation of efficient feature selection algorithms has been an integral area of research in the past decade. This article proposes an efficient feature selection technique, realized by means of an evolutionary algorithm, which attempts to overcome some of the shortcomings of several state-of-the-art approaches in this field. The outlined scheme produces a subset of salient features which improves the classification accuracy while maintaining a trade-off with the computational speed of the complete scheme. For this purpose, an efficient memetic algorithm has also been proposed for the optimization purpose. Extensive experimental validations have been conducted on two real-world datasets to establish the efficacy of our approach. We have compared our approach to existing algorithms and have established the superiority of our algorithm to the rest. Brain computer interfacing Feature selection Motor imagery Memetic algorithm Differential evolution Learning automata Power spectral density Sengupta, Abhronil aut Chakraborti, Tathagatha aut Konar, Amit aut Tibarewala, D. N. aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 52(2013), 2 vom: 29. Okt., Seite 131-139 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:52 year:2013 number:2 day:29 month:10 pages:131-139 https://doi.org/10.1007/s11517-013-1123-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_32 GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 52 2013 2 29 10 131-139 |
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10.1007/s11517-013-1123-9 doi (DE-627)OLC203869186X (DE-He213)s11517-013-1123-9-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Bhattacharyya, Saugat verfasserin aut Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2013 Abstract Brain–computer interfacing (BCI) has been the most researched technology in neuroprosthesis in the last two decades. Feature extractors and classifiers play an important role in BCI research for the generation of suitable control signals to drive an assistive device. Due to the high dimensionality of feature vectors in practical BCI systems, implantation of efficient feature selection algorithms has been an integral area of research in the past decade. This article proposes an efficient feature selection technique, realized by means of an evolutionary algorithm, which attempts to overcome some of the shortcomings of several state-of-the-art approaches in this field. The outlined scheme produces a subset of salient features which improves the classification accuracy while maintaining a trade-off with the computational speed of the complete scheme. For this purpose, an efficient memetic algorithm has also been proposed for the optimization purpose. Extensive experimental validations have been conducted on two real-world datasets to establish the efficacy of our approach. We have compared our approach to existing algorithms and have established the superiority of our algorithm to the rest. Brain computer interfacing Feature selection Motor imagery Memetic algorithm Differential evolution Learning automata Power spectral density Sengupta, Abhronil aut Chakraborti, Tathagatha aut Konar, Amit aut Tibarewala, D. N. aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 52(2013), 2 vom: 29. Okt., Seite 131-139 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:52 year:2013 number:2 day:29 month:10 pages:131-139 https://doi.org/10.1007/s11517-013-1123-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_32 GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 52 2013 2 29 10 131-139 |
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10.1007/s11517-013-1123-9 doi (DE-627)OLC203869186X (DE-He213)s11517-013-1123-9-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Bhattacharyya, Saugat verfasserin aut Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2013 Abstract Brain–computer interfacing (BCI) has been the most researched technology in neuroprosthesis in the last two decades. Feature extractors and classifiers play an important role in BCI research for the generation of suitable control signals to drive an assistive device. Due to the high dimensionality of feature vectors in practical BCI systems, implantation of efficient feature selection algorithms has been an integral area of research in the past decade. This article proposes an efficient feature selection technique, realized by means of an evolutionary algorithm, which attempts to overcome some of the shortcomings of several state-of-the-art approaches in this field. The outlined scheme produces a subset of salient features which improves the classification accuracy while maintaining a trade-off with the computational speed of the complete scheme. For this purpose, an efficient memetic algorithm has also been proposed for the optimization purpose. Extensive experimental validations have been conducted on two real-world datasets to establish the efficacy of our approach. We have compared our approach to existing algorithms and have established the superiority of our algorithm to the rest. Brain computer interfacing Feature selection Motor imagery Memetic algorithm Differential evolution Learning automata Power spectral density Sengupta, Abhronil aut Chakraborti, Tathagatha aut Konar, Amit aut Tibarewala, D. N. aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 52(2013), 2 vom: 29. Okt., Seite 131-139 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:52 year:2013 number:2 day:29 month:10 pages:131-139 https://doi.org/10.1007/s11517-013-1123-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_32 GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 52 2013 2 29 10 131-139 |
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10.1007/s11517-013-1123-9 doi (DE-627)OLC203869186X (DE-He213)s11517-013-1123-9-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Bhattacharyya, Saugat verfasserin aut Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2013 Abstract Brain–computer interfacing (BCI) has been the most researched technology in neuroprosthesis in the last two decades. Feature extractors and classifiers play an important role in BCI research for the generation of suitable control signals to drive an assistive device. Due to the high dimensionality of feature vectors in practical BCI systems, implantation of efficient feature selection algorithms has been an integral area of research in the past decade. This article proposes an efficient feature selection technique, realized by means of an evolutionary algorithm, which attempts to overcome some of the shortcomings of several state-of-the-art approaches in this field. The outlined scheme produces a subset of salient features which improves the classification accuracy while maintaining a trade-off with the computational speed of the complete scheme. For this purpose, an efficient memetic algorithm has also been proposed for the optimization purpose. Extensive experimental validations have been conducted on two real-world datasets to establish the efficacy of our approach. We have compared our approach to existing algorithms and have established the superiority of our algorithm to the rest. Brain computer interfacing Feature selection Motor imagery Memetic algorithm Differential evolution Learning automata Power spectral density Sengupta, Abhronil aut Chakraborti, Tathagatha aut Konar, Amit aut Tibarewala, D. N. aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 52(2013), 2 vom: 29. Okt., Seite 131-139 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:52 year:2013 number:2 day:29 month:10 pages:131-139 https://doi.org/10.1007/s11517-013-1123-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_32 GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 52 2013 2 29 10 131-139 |
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Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata |
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Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata |
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Bhattacharyya, Saugat |
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Bhattacharyya, Saugat Sengupta, Abhronil Chakraborti, Tathagatha Konar, Amit Tibarewala, D. N. |
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automatic feature selection of motor imagery eeg signals using differential evolution and learning automata |
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Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata |
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Abstract Brain–computer interfacing (BCI) has been the most researched technology in neuroprosthesis in the last two decades. Feature extractors and classifiers play an important role in BCI research for the generation of suitable control signals to drive an assistive device. Due to the high dimensionality of feature vectors in practical BCI systems, implantation of efficient feature selection algorithms has been an integral area of research in the past decade. This article proposes an efficient feature selection technique, realized by means of an evolutionary algorithm, which attempts to overcome some of the shortcomings of several state-of-the-art approaches in this field. The outlined scheme produces a subset of salient features which improves the classification accuracy while maintaining a trade-off with the computational speed of the complete scheme. For this purpose, an efficient memetic algorithm has also been proposed for the optimization purpose. Extensive experimental validations have been conducted on two real-world datasets to establish the efficacy of our approach. We have compared our approach to existing algorithms and have established the superiority of our algorithm to the rest. © International Federation for Medical and Biological Engineering 2013 |
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Abstract Brain–computer interfacing (BCI) has been the most researched technology in neuroprosthesis in the last two decades. Feature extractors and classifiers play an important role in BCI research for the generation of suitable control signals to drive an assistive device. Due to the high dimensionality of feature vectors in practical BCI systems, implantation of efficient feature selection algorithms has been an integral area of research in the past decade. This article proposes an efficient feature selection technique, realized by means of an evolutionary algorithm, which attempts to overcome some of the shortcomings of several state-of-the-art approaches in this field. The outlined scheme produces a subset of salient features which improves the classification accuracy while maintaining a trade-off with the computational speed of the complete scheme. For this purpose, an efficient memetic algorithm has also been proposed for the optimization purpose. Extensive experimental validations have been conducted on two real-world datasets to establish the efficacy of our approach. We have compared our approach to existing algorithms and have established the superiority of our algorithm to the rest. © International Federation for Medical and Biological Engineering 2013 |
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Abstract Brain–computer interfacing (BCI) has been the most researched technology in neuroprosthesis in the last two decades. Feature extractors and classifiers play an important role in BCI research for the generation of suitable control signals to drive an assistive device. Due to the high dimensionality of feature vectors in practical BCI systems, implantation of efficient feature selection algorithms has been an integral area of research in the past decade. This article proposes an efficient feature selection technique, realized by means of an evolutionary algorithm, which attempts to overcome some of the shortcomings of several state-of-the-art approaches in this field. The outlined scheme produces a subset of salient features which improves the classification accuracy while maintaining a trade-off with the computational speed of the complete scheme. For this purpose, an efficient memetic algorithm has also been proposed for the optimization purpose. Extensive experimental validations have been conducted on two real-world datasets to establish the efficacy of our approach. We have compared our approach to existing algorithms and have established the superiority of our algorithm to the rest. © International Federation for Medical and Biological Engineering 2013 |
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Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata |
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