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Biomedical signal processing

Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise

Christian El Hajj

The extraction of multi-exponential decay parameters from multi-temporal images corrupted with Rician noise and with limited time samples proves to be a challenging problem frequently encountered in clinical and food MRI studies. This work aims at proposing a method for the estimation of multiexponential transverse relaxation times from noisy magnitude MRI images. A spatially regularized Maximum-Likelihood estimator accounting for the Rician distribution of the noise is introduced.

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Authors:
Christian El Hajj
EL HAJJ Christian, MOUSSAOUI Saïd, COLLEWET Guylaine, MUSSE Maja.
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20 September 2019 - 11:12am
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[1] EL HAJJ Christian, MOUSSAOUI Saïd, COLLEWET Guylaine, MUSSE Maja., "Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4783. Accessed: Oct. 17, 2019.
@article{4783-19,
url = {http://sigport.org/4783},
author = {EL HAJJ Christian; MOUSSAOUI Saïd; COLLEWET Guylaine; MUSSE Maja. },
publisher = {IEEE SigPort},
title = {Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise},
year = {2019} }
TY - EJOUR
T1 - Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise
AU - EL HAJJ Christian; MOUSSAOUI Saïd; COLLEWET Guylaine; MUSSE Maja.
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4783
ER -
EL HAJJ Christian, MOUSSAOUI Saïd, COLLEWET Guylaine, MUSSE Maja.. (2019). Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise. IEEE SigPort. http://sigport.org/4783
EL HAJJ Christian, MOUSSAOUI Saïd, COLLEWET Guylaine, MUSSE Maja., 2019. Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise. Available at: http://sigport.org/4783.
EL HAJJ Christian, MOUSSAOUI Saïd, COLLEWET Guylaine, MUSSE Maja.. (2019). "Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise." Web.
1. EL HAJJ Christian, MOUSSAOUI Saïd, COLLEWET Guylaine, MUSSE Maja.. Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4783

DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT

Zeyu Pan

Depression is a common mental disorder, which greatly affects the patients' daily life and work. Current depression detection relies almost exclusively on the clinical interview and structured questionnaire, consuming a lot of medical resources and risking a range of subjective biases. Our goal is to achieve a convenient and objective depression detection system, which can assist clinicians in their diagnosis of clinical depression.

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Authors:
Zeyu Pan
Zeyu Pan, Huimin Ma, Lin Zhang, Yahui Wang
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11 September 2019 - 2:29am
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[1] Zeyu Pan, Huimin Ma, Lin Zhang, Yahui Wang, "DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4589. Accessed: Oct. 17, 2019.
@article{4589-19,
url = {http://sigport.org/4589},
author = {Zeyu Pan; Huimin Ma; Lin Zhang; Yahui Wang },
publisher = {IEEE SigPort},
title = {DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT},
year = {2019} }
TY - EJOUR
T1 - DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT
AU - Zeyu Pan; Huimin Ma; Lin Zhang; Yahui Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4589
ER -
Zeyu Pan, Huimin Ma, Lin Zhang, Yahui Wang. (2019). DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT. IEEE SigPort. http://sigport.org/4589
Zeyu Pan, Huimin Ma, Lin Zhang, Yahui Wang, 2019. DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT. Available at: http://sigport.org/4589.
Zeyu Pan, Huimin Ma, Lin Zhang, Yahui Wang. (2019). "DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT." Web.
1. Zeyu Pan, Huimin Ma, Lin Zhang, Yahui Wang. DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4589

ECG Reconstruction via PPG: A Pilot Study

Qiang ZHU
Chau-Wai Wong
Min Wu

In this paper, the relation between electrocardiogram (ECG) and photoplethysmogram (PPG) signals is studied, and the waveform of ECG is inferred via the PPG signals. In order to address this inverse problem, a transform is proposed to map the discrete cosine transform (DCT) coefficients of each PPG cycle to those of the corresponding ECG cycle. The resulting DCT coefficients of the ECG cycle are inversely transformed to obtain the reconstructed ECG waveform. The proposed method is evaluated on a benchmark dataset of subjects with a variety of combinations of age and weight.

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Authors:
Qiang ZHU
Chau-Wai Wong
Min Wu
Qiang Zhu, Xin Tian, Chau-Wai Wong, Min Wu
Submitted On:
22 May 2019 - 5:16pm
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[1] Qiang Zhu, Xin Tian, Chau-Wai Wong, Min Wu, "ECG Reconstruction via PPG: A Pilot Study", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4558. Accessed: Oct. 17, 2019.
@article{4558-19,
url = {http://sigport.org/4558},
author = {Qiang Zhu; Xin Tian; Chau-Wai Wong; Min Wu },
publisher = {IEEE SigPort},
title = {ECG Reconstruction via PPG: A Pilot Study},
year = {2019} }
TY - EJOUR
T1 - ECG Reconstruction via PPG: A Pilot Study
AU - Qiang Zhu; Xin Tian; Chau-Wai Wong; Min Wu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4558
ER -
Qiang Zhu, Xin Tian, Chau-Wai Wong, Min Wu. (2019). ECG Reconstruction via PPG: A Pilot Study. IEEE SigPort. http://sigport.org/4558
Qiang Zhu, Xin Tian, Chau-Wai Wong, Min Wu, 2019. ECG Reconstruction via PPG: A Pilot Study. Available at: http://sigport.org/4558.
Qiang Zhu, Xin Tian, Chau-Wai Wong, Min Wu. (2019). "ECG Reconstruction via PPG: A Pilot Study." Web.
1. Qiang Zhu, Xin Tian, Chau-Wai Wong, Min Wu. ECG Reconstruction via PPG: A Pilot Study [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4558

BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG

Takehiro Kono

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Authors:
Takehiro Kono
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17 May 2019 - 6:04am
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[1] , "BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4546. Accessed: Oct. 17, 2019.
@article{4546-19,
url = {http://sigport.org/4546},
author = { },
publisher = {IEEE SigPort},
title = {BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG},
year = {2019} }
TY - EJOUR
T1 - BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4546
ER -
. (2019). BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG. IEEE SigPort. http://sigport.org/4546
, 2019. BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG. Available at: http://sigport.org/4546.
. (2019). "BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG." Web.
1. . BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4546

BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG

Takehiro Kono

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Authors:
Takehiro Kono
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17 May 2019 - 6:04am
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[1] , "BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4545. Accessed: Oct. 17, 2019.
@article{4545-19,
url = {http://sigport.org/4545},
author = { },
publisher = {IEEE SigPort},
title = {BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG},
year = {2019} }
TY - EJOUR
T1 - BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4545
ER -
. (2019). BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG. IEEE SigPort. http://sigport.org/4545
, 2019. BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG. Available at: http://sigport.org/4545.
. (2019). "BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG." Web.
1. . BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4545

Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy

subhankar chattoraj

Histopathological images (HI) encrypt resolution dependent heterogeneous textures & diverse color distribution variability, manifesting in micro-structural surface tissue convolutions & inherently high coherency of cancerous cells posing significant challenges to breast cancer (BC) multi-classification.

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Authors:
subhankar chattoraj
Sawon Pratiher, Subhankar Chattoraj
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11 May 2019 - 2:13am
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[1] Sawon Pratiher, Subhankar Chattoraj, "Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4436. Accessed: Oct. 17, 2019.
@article{4436-19,
url = {http://sigport.org/4436},
author = {Sawon Pratiher; Subhankar Chattoraj },
publisher = {IEEE SigPort},
title = {Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy},
year = {2019} }
TY - EJOUR
T1 - Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy
AU - Sawon Pratiher; Subhankar Chattoraj
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4436
ER -
Sawon Pratiher, Subhankar Chattoraj. (2019). Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy. IEEE SigPort. http://sigport.org/4436
Sawon Pratiher, Subhankar Chattoraj, 2019. Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy. Available at: http://sigport.org/4436.
Sawon Pratiher, Subhankar Chattoraj. (2019). "Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy." Web.
1. Sawon Pratiher, Subhankar Chattoraj. Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4436

DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS

Pramit Saha

Speech-related Brain Computer Interfaces (BCI) aim primarily at finding an alternative vocal communication pathway for
people with speaking disabilities. As a step towards full decoding of imagined speech from active thoughts, we present a
BCI system for subject-independent classification of phonological categories exploiting a novel deep learning based

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Authors:
Pramit Saha
Pramit Saha, Muhammad Abdul Mageed, Sidney Fels
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10 May 2019 - 8:57am
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[1] Pramit Saha, Muhammad Abdul Mageed, Sidney Fels, "DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4323. Accessed: Oct. 17, 2019.
@article{4323-19,
url = {http://sigport.org/4323},
author = {Pramit Saha; Muhammad Abdul Mageed; Sidney Fels },
publisher = {IEEE SigPort},
title = {DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS},
year = {2019} }
TY - EJOUR
T1 - DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS
AU - Pramit Saha; Muhammad Abdul Mageed; Sidney Fels
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4323
ER -
Pramit Saha, Muhammad Abdul Mageed, Sidney Fels. (2019). DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS. IEEE SigPort. http://sigport.org/4323
Pramit Saha, Muhammad Abdul Mageed, Sidney Fels, 2019. DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS. Available at: http://sigport.org/4323.
Pramit Saha, Muhammad Abdul Mageed, Sidney Fels. (2019). "DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS." Web.
1. Pramit Saha, Muhammad Abdul Mageed, Sidney Fels. DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4323

Waveform Modeling by Adaptive Weighted Hermite Functions

Mario Huemer
Carl Boeck

Modern medical science demands sophisticated signal representation methods in order to cope with the increasing amount of data. Important criteria for these methods are mainly low computational and storage costs, whereas the underlying mathematical model should still be interpretable and meaningful for the data analyst. One of the most promising models fulfilling these criteria is based on Hermite functions, however having some important limitations for specific biomedical wave shapes.

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Authors:
Mario Huemer
Carl Boeck
Péter Kovács, Carl Böck, Tamás Dósza, Jens Meier, Mario Huemer
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10 May 2019 - 9:31am
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[1] Péter Kovács, Carl Böck, Tamás Dósza, Jens Meier, Mario Huemer, "Waveform Modeling by Adaptive Weighted Hermite Functions", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4320. Accessed: Oct. 17, 2019.
@article{4320-19,
url = {http://sigport.org/4320},
author = {Péter Kovács; Carl Böck; Tamás Dósza; Jens Meier; Mario Huemer },
publisher = {IEEE SigPort},
title = {Waveform Modeling by Adaptive Weighted Hermite Functions},
year = {2019} }
TY - EJOUR
T1 - Waveform Modeling by Adaptive Weighted Hermite Functions
AU - Péter Kovács; Carl Böck; Tamás Dósza; Jens Meier; Mario Huemer
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4320
ER -
Péter Kovács, Carl Böck, Tamás Dósza, Jens Meier, Mario Huemer. (2019). Waveform Modeling by Adaptive Weighted Hermite Functions. IEEE SigPort. http://sigport.org/4320
Péter Kovács, Carl Böck, Tamás Dósza, Jens Meier, Mario Huemer, 2019. Waveform Modeling by Adaptive Weighted Hermite Functions. Available at: http://sigport.org/4320.
Péter Kovács, Carl Böck, Tamás Dósza, Jens Meier, Mario Huemer. (2019). "Waveform Modeling by Adaptive Weighted Hermite Functions." Web.
1. Péter Kovács, Carl Böck, Tamás Dósza, Jens Meier, Mario Huemer. Waveform Modeling by Adaptive Weighted Hermite Functions [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4320

TV-DCT: Method to Impute Gene Expression Data Using DCT Based Sparsity and Total Variation Denoising

Akanksha Farswan

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Authors:
Akanksha Farswan
Akanksha Farswan, Anubha Gupta
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10 May 2019 - 12:55am
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[1] Akanksha Farswan, Anubha Gupta, "TV-DCT: Method to Impute Gene Expression Data Using DCT Based Sparsity and Total Variation Denoising", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4268. Accessed: Oct. 17, 2019.
@article{4268-19,
url = {http://sigport.org/4268},
author = {Akanksha Farswan; Anubha Gupta },
publisher = {IEEE SigPort},
title = {TV-DCT: Method to Impute Gene Expression Data Using DCT Based Sparsity and Total Variation Denoising},
year = {2019} }
TY - EJOUR
T1 - TV-DCT: Method to Impute Gene Expression Data Using DCT Based Sparsity and Total Variation Denoising
AU - Akanksha Farswan; Anubha Gupta
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4268
ER -
Akanksha Farswan, Anubha Gupta. (2019). TV-DCT: Method to Impute Gene Expression Data Using DCT Based Sparsity and Total Variation Denoising. IEEE SigPort. http://sigport.org/4268
Akanksha Farswan, Anubha Gupta, 2019. TV-DCT: Method to Impute Gene Expression Data Using DCT Based Sparsity and Total Variation Denoising. Available at: http://sigport.org/4268.
Akanksha Farswan, Anubha Gupta. (2019). "TV-DCT: Method to Impute Gene Expression Data Using DCT Based Sparsity and Total Variation Denoising." Web.
1. Akanksha Farswan, Anubha Gupta. TV-DCT: Method to Impute Gene Expression Data Using DCT Based Sparsity and Total Variation Denoising [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4268

Motion Artefact Removal in Functional Near-InfraRed Spectroscopy Signals based on Robust Estimation

Mengmeng Wang

Functional Near-InfraRed Spectroscopy (fNIRS) has gained widespread acceptance as a non-invasive neuroimaging modality for monitoring functional brain activities. fNIRS uses light in the near infra-red spectrum (600-900 nm) to penetrate human brain tissues and estimates the oxygenation conditions based on the proportion of light absorbed. In order to get reliable results, artefacts and noise need to be separated from fNIRS physiological signals. This paper focuses on removing motion-related artefacts. A new motion artefact removal algorithm based on robust parameter estimation is proposed.

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Authors:
Mengmeng Wang
Mengmeng Wang, Abd-Krim Seghouane
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10 May 2019 - 12:35am
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[1] Mengmeng Wang, Abd-Krim Seghouane, "Motion Artefact Removal in Functional Near-InfraRed Spectroscopy Signals based on Robust Estimation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4266. Accessed: Oct. 17, 2019.
@article{4266-19,
url = {http://sigport.org/4266},
author = {Mengmeng Wang; Abd-Krim Seghouane },
publisher = {IEEE SigPort},
title = {Motion Artefact Removal in Functional Near-InfraRed Spectroscopy Signals based on Robust Estimation},
year = {2019} }
TY - EJOUR
T1 - Motion Artefact Removal in Functional Near-InfraRed Spectroscopy Signals based on Robust Estimation
AU - Mengmeng Wang; Abd-Krim Seghouane
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4266
ER -
Mengmeng Wang, Abd-Krim Seghouane. (2019). Motion Artefact Removal in Functional Near-InfraRed Spectroscopy Signals based on Robust Estimation. IEEE SigPort. http://sigport.org/4266
Mengmeng Wang, Abd-Krim Seghouane, 2019. Motion Artefact Removal in Functional Near-InfraRed Spectroscopy Signals based on Robust Estimation. Available at: http://sigport.org/4266.
Mengmeng Wang, Abd-Krim Seghouane. (2019). "Motion Artefact Removal in Functional Near-InfraRed Spectroscopy Signals based on Robust Estimation." Web.
1. Mengmeng Wang, Abd-Krim Seghouane. Motion Artefact Removal in Functional Near-InfraRed Spectroscopy Signals based on Robust Estimation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4266

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