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Biomedical signal processing

A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface


One of the major limitations of current electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the long calibration time. Due to a high level of noise and non-stationarity inherent in EEG signals, a calibration model trained using the limited number of train data may not yield an accurate BCI model. To address this problem, this paper proposes a novel subject-to-subject transfer learning framework that improves the classification accuracy using limited training data.

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Authors:
Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh
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9 May 2019 - 7:04am
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ICASSP POSTER.pdf

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[1] Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh, "A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4188. Accessed: Jul. 23, 2019.
@article{4188-19,
url = {http://sigport.org/4188},
author = {Joshua Giles; Kai Keng Ang; Lyudmila S. Mihaylova; Mahnaz Arvaneh },
publisher = {IEEE SigPort},
title = {A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface},
year = {2019} }
TY - EJOUR
T1 - A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface
AU - Joshua Giles; Kai Keng Ang; Lyudmila S. Mihaylova; Mahnaz Arvaneh
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4188
ER -
Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh. (2019). A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface. IEEE SigPort. http://sigport.org/4188
Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh, 2019. A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface. Available at: http://sigport.org/4188.
Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh. (2019). "A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface." Web.
1. Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh. A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4188

Seizure Detection Using Least EEG Channels by Deep Convolutional Neural Network


This work aims to develop an end-to-end solution for seizure onset detection. We design the SeizNet, a Convolutional Neural Network for seizure detection. To compare SeizNet with traditional machine learning approach, a baseline classifier is implemented using spectrum band power features with Support Vector Machines (BPsvm). We explore the possibility to use the least number of channels for accurate seizure detection by evaluating SeizNet and BPsvm approaches using all channels and two channels settings respectively.

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Authors:
Mustafa Talha Avcu, Zhuo Zhang, Derrick Wei Shih Chan
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9 May 2019 - 6:22am
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[1] Mustafa Talha Avcu, Zhuo Zhang, Derrick Wei Shih Chan, "Seizure Detection Using Least EEG Channels by Deep Convolutional Neural Network", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4184. Accessed: Jul. 23, 2019.
@article{4184-19,
url = {http://sigport.org/4184},
author = {Mustafa Talha Avcu; Zhuo Zhang; Derrick Wei Shih Chan },
publisher = {IEEE SigPort},
title = {Seizure Detection Using Least EEG Channels by Deep Convolutional Neural Network},
year = {2019} }
TY - EJOUR
T1 - Seizure Detection Using Least EEG Channels by Deep Convolutional Neural Network
AU - Mustafa Talha Avcu; Zhuo Zhang; Derrick Wei Shih Chan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4184
ER -
Mustafa Talha Avcu, Zhuo Zhang, Derrick Wei Shih Chan. (2019). Seizure Detection Using Least EEG Channels by Deep Convolutional Neural Network. IEEE SigPort. http://sigport.org/4184
Mustafa Talha Avcu, Zhuo Zhang, Derrick Wei Shih Chan, 2019. Seizure Detection Using Least EEG Channels by Deep Convolutional Neural Network. Available at: http://sigport.org/4184.
Mustafa Talha Avcu, Zhuo Zhang, Derrick Wei Shih Chan. (2019). "Seizure Detection Using Least EEG Channels by Deep Convolutional Neural Network." Web.
1. Mustafa Talha Avcu, Zhuo Zhang, Derrick Wei Shih Chan. Seizure Detection Using Least EEG Channels by Deep Convolutional Neural Network [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4184

Short-Segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks


This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of two-dimensional time-frequency feature maps based on Mel-frequency cepstral coefficients. We further develop a time-frequency CNN ensemble (TF-ECNN) combining the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities.

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Authors:
Chee-Ming Ting, Sh-Hussain Salleh, Hernando Ombao
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9 May 2019 - 3:40am
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ICASSP19Poster-May7.pdf

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[1] Chee-Ming Ting, Sh-Hussain Salleh, Hernando Ombao, "Short-Segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4162. Accessed: Jul. 23, 2019.
@article{4162-19,
url = {http://sigport.org/4162},
author = {Chee-Ming Ting; Sh-Hussain Salleh; Hernando Ombao },
publisher = {IEEE SigPort},
title = {Short-Segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks},
year = {2019} }
TY - EJOUR
T1 - Short-Segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks
AU - Chee-Ming Ting; Sh-Hussain Salleh; Hernando Ombao
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4162
ER -
Chee-Ming Ting, Sh-Hussain Salleh, Hernando Ombao. (2019). Short-Segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks. IEEE SigPort. http://sigport.org/4162
Chee-Ming Ting, Sh-Hussain Salleh, Hernando Ombao, 2019. Short-Segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks. Available at: http://sigport.org/4162.
Chee-Ming Ting, Sh-Hussain Salleh, Hernando Ombao. (2019). "Short-Segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks." Web.
1. Chee-Ming Ting, Sh-Hussain Salleh, Hernando Ombao. Short-Segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4162

Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition

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Authors:
Yongjie Zhu, Xueqiao Li, Tapani Ristaniemi, Fengyu Cong
Submitted On:
8 May 2019 - 2:54am
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Brain_Networks_Tensor_Decomposition

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[1] Yongjie Zhu, Xueqiao Li, Tapani Ristaniemi, Fengyu Cong, "Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4029. Accessed: Jul. 23, 2019.
@article{4029-19,
url = {http://sigport.org/4029},
author = {Yongjie Zhu; Xueqiao Li; Tapani Ristaniemi; Fengyu Cong },
publisher = {IEEE SigPort},
title = {Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition},
year = {2019} }
TY - EJOUR
T1 - Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition
AU - Yongjie Zhu; Xueqiao Li; Tapani Ristaniemi; Fengyu Cong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4029
ER -
Yongjie Zhu, Xueqiao Li, Tapani Ristaniemi, Fengyu Cong. (2019). Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition. IEEE SigPort. http://sigport.org/4029
Yongjie Zhu, Xueqiao Li, Tapani Ristaniemi, Fengyu Cong, 2019. Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition. Available at: http://sigport.org/4029.
Yongjie Zhu, Xueqiao Li, Tapani Ristaniemi, Fengyu Cong. (2019). "Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition." Web.
1. Yongjie Zhu, Xueqiao Li, Tapani Ristaniemi, Fengyu Cong. Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4029

SPEECH ARTIFACT REMOVAL FROM EEG RECORDINGS OF SPOKEN WORD PRODUCTION WITH TENSOR DECOMPOSITION


Research about brain activities involving spoken word production is considerably underdeveloped because of the undiscovered characteristics of speech artifacts, which contaminate electroencephalogram (EEG) signals and prevent the inspection of the underlying cognitive processes. To fuel further EEG research with speech production, a method using three-mode tensor decomposition (time x space x frequency) is proposed to perform speech artifact removal. Tensor decomposition enables simultaneous inspection of multiple modes, which suits the multi-way nature of EEG data.

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Authors:
Holy Lovenia, Hiroki Tanaka, Sakriani Sakti, Ayu Purwarianti, Satoshi Nakamura
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1 May 2019 - 4:29am
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Holy Lovenia - ICASSP Poster (A0).pdf

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[1] Holy Lovenia, Hiroki Tanaka, Sakriani Sakti, Ayu Purwarianti, Satoshi Nakamura, "SPEECH ARTIFACT REMOVAL FROM EEG RECORDINGS OF SPOKEN WORD PRODUCTION WITH TENSOR DECOMPOSITION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3902. Accessed: Jul. 23, 2019.
@article{3902-19,
url = {http://sigport.org/3902},
author = {Holy Lovenia; Hiroki Tanaka; Sakriani Sakti; Ayu Purwarianti; Satoshi Nakamura },
publisher = {IEEE SigPort},
title = {SPEECH ARTIFACT REMOVAL FROM EEG RECORDINGS OF SPOKEN WORD PRODUCTION WITH TENSOR DECOMPOSITION},
year = {2019} }
TY - EJOUR
T1 - SPEECH ARTIFACT REMOVAL FROM EEG RECORDINGS OF SPOKEN WORD PRODUCTION WITH TENSOR DECOMPOSITION
AU - Holy Lovenia; Hiroki Tanaka; Sakriani Sakti; Ayu Purwarianti; Satoshi Nakamura
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3902
ER -
Holy Lovenia, Hiroki Tanaka, Sakriani Sakti, Ayu Purwarianti, Satoshi Nakamura. (2019). SPEECH ARTIFACT REMOVAL FROM EEG RECORDINGS OF SPOKEN WORD PRODUCTION WITH TENSOR DECOMPOSITION. IEEE SigPort. http://sigport.org/3902
Holy Lovenia, Hiroki Tanaka, Sakriani Sakti, Ayu Purwarianti, Satoshi Nakamura, 2019. SPEECH ARTIFACT REMOVAL FROM EEG RECORDINGS OF SPOKEN WORD PRODUCTION WITH TENSOR DECOMPOSITION. Available at: http://sigport.org/3902.
Holy Lovenia, Hiroki Tanaka, Sakriani Sakti, Ayu Purwarianti, Satoshi Nakamura. (2019). "SPEECH ARTIFACT REMOVAL FROM EEG RECORDINGS OF SPOKEN WORD PRODUCTION WITH TENSOR DECOMPOSITION." Web.
1. Holy Lovenia, Hiroki Tanaka, Sakriani Sakti, Ayu Purwarianti, Satoshi Nakamura. SPEECH ARTIFACT REMOVAL FROM EEG RECORDINGS OF SPOKEN WORD PRODUCTION WITH TENSOR DECOMPOSITION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3902

IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN


In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. In this paper, we approach

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Authors:
Panagiotis Tsinganos, Bruno Cornelis, Jan Cornelis, Bart Jansen, Athanassios Skodras
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7 May 2019 - 12:59pm
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[1] Panagiotis Tsinganos, Bruno Cornelis, Jan Cornelis, Bart Jansen, Athanassios Skodras, "IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3898. Accessed: Jul. 23, 2019.
@article{3898-19,
url = {http://sigport.org/3898},
author = {Panagiotis Tsinganos; Bruno Cornelis; Jan Cornelis; Bart Jansen; Athanassios Skodras },
publisher = {IEEE SigPort},
title = {IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN},
year = {2019} }
TY - EJOUR
T1 - IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN
AU - Panagiotis Tsinganos; Bruno Cornelis; Jan Cornelis; Bart Jansen; Athanassios Skodras
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3898
ER -
Panagiotis Tsinganos, Bruno Cornelis, Jan Cornelis, Bart Jansen, Athanassios Skodras. (2019). IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN. IEEE SigPort. http://sigport.org/3898
Panagiotis Tsinganos, Bruno Cornelis, Jan Cornelis, Bart Jansen, Athanassios Skodras, 2019. IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN. Available at: http://sigport.org/3898.
Panagiotis Tsinganos, Bruno Cornelis, Jan Cornelis, Bart Jansen, Athanassios Skodras. (2019). "IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN." Web.
1. Panagiotis Tsinganos, Bruno Cornelis, Jan Cornelis, Bart Jansen, Athanassios Skodras. IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3898

Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation


Neural spiking responses are generated by both extrinsic covariates such as sensory variables and intrinsic covariates such as those rep-resenting the state of a system. Although the external covariates can be directly controlled or measured; the internal factors are hard, if not impossible, to control or even observe. This study provides a statistical framework that enables characterization of the unobserved factors controlling neuronal response variability induced by behavior, with the model parameters fitted directly to real spiking data.

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Authors:
Kaiser Niknam, Amir Akbarian, Behrad Noudoost, Neda Nategh
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26 November 2018 - 11:32pm
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Niknam

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[1] Kaiser Niknam, Amir Akbarian, Behrad Noudoost, Neda Nategh, "Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3803. Accessed: Jul. 23, 2019.
@article{3803-18,
url = {http://sigport.org/3803},
author = {Kaiser Niknam; Amir Akbarian; Behrad Noudoost; Neda Nategh },
publisher = {IEEE SigPort},
title = {Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation},
year = {2018} }
TY - EJOUR
T1 - Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation
AU - Kaiser Niknam; Amir Akbarian; Behrad Noudoost; Neda Nategh
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3803
ER -
Kaiser Niknam, Amir Akbarian, Behrad Noudoost, Neda Nategh. (2018). Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation. IEEE SigPort. http://sigport.org/3803
Kaiser Niknam, Amir Akbarian, Behrad Noudoost, Neda Nategh, 2018. Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation. Available at: http://sigport.org/3803.
Kaiser Niknam, Amir Akbarian, Behrad Noudoost, Neda Nategh. (2018). "Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation." Web.
1. Kaiser Niknam, Amir Akbarian, Behrad Noudoost, Neda Nategh. Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3803

StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures


A novel non-stationarity visualization tool known as StationPlot is developed for deciphering the chaotic behavior of a dynamical time series. A family of analytic measures enumerating geometrical aspects of the non-stationarity & degree of variability is formulated by convex hull geometry (CHG) on StationPlot. In the Euclidean space, both trend-stationary (TS) & difference-stationary (DS) perturbations are comprehended by the asymmetric structure of StationPlot's region of interest (ROI).

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Authors:
Sawon Pratiher, Subhankar Chattoraj, Rajdeep Mukherjee
Submitted On:
26 November 2018 - 2:05pm
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[1] Sawon Pratiher, Subhankar Chattoraj, Rajdeep Mukherjee, "StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3778. Accessed: Jul. 23, 2019.
@article{3778-18,
url = {http://sigport.org/3778},
author = {Sawon Pratiher; Subhankar Chattoraj; Rajdeep Mukherjee },
publisher = {IEEE SigPort},
title = {StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures},
year = {2018} }
TY - EJOUR
T1 - StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures
AU - Sawon Pratiher; Subhankar Chattoraj; Rajdeep Mukherjee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3778
ER -
Sawon Pratiher, Subhankar Chattoraj, Rajdeep Mukherjee. (2018). StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures. IEEE SigPort. http://sigport.org/3778
Sawon Pratiher, Subhankar Chattoraj, Rajdeep Mukherjee, 2018. StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures. Available at: http://sigport.org/3778.
Sawon Pratiher, Subhankar Chattoraj, Rajdeep Mukherjee. (2018). "StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures." Web.
1. Sawon Pratiher, Subhankar Chattoraj, Rajdeep Mukherjee. StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3778

POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS


Electroencephalography (EEG) has been widely used in human brain research. Several techniques in EEG relies on analyzing the topographical distribution of the data. One of the most common analysis is EEG microstate (EEG-ms). EEG-ms reflects the stable topographical representation of EEG signal lasting a few dozen milliseconds. EEG-ms were associated with resting state fMRI networks and were associated with mental processes and abnormalities.

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Authors:
Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka
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23 November 2018 - 8:20pm
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A presentation for polarity invariant transformation for EEG microstates analysis

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[1] Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka , "POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3761. Accessed: Jul. 23, 2019.
@article{3761-18,
url = {http://sigport.org/3761},
author = {Ahmad Mayeli; Hazem Refai; Martin Paulus; Jerzy Bodurka },
publisher = {IEEE SigPort},
title = {POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS},
year = {2018} }
TY - EJOUR
T1 - POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS
AU - Ahmad Mayeli; Hazem Refai; Martin Paulus; Jerzy Bodurka
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3761
ER -
Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka . (2018). POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS. IEEE SigPort. http://sigport.org/3761
Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka , 2018. POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS. Available at: http://sigport.org/3761.
Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka . (2018). "POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS." Web.
1. Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka . POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3761

Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos


Smartphone video-based measurement of heart rate typically uses photoplethysmography (PPG). Prior accuracy studies report low mean absolute errors for apps based on contact PPG on a fingertip, but substantial errors on a troubling percentage of measurements. In this study, we aimed to reduce the rate of substantial heart rate estimation errors by introducing a novel signal present in fingertip videos: fingertip contact surface area.

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Authors:
Sara Fridovich-Keil, Peter J. Ramadge
Submitted On:
27 November 2018 - 1:59am
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[1] Sara Fridovich-Keil, Peter J. Ramadge, "Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3753. Accessed: Jul. 23, 2019.
@article{3753-18,
url = {http://sigport.org/3753},
author = {Sara Fridovich-Keil; Peter J. Ramadge },
publisher = {IEEE SigPort},
title = {Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos},
year = {2018} }
TY - EJOUR
T1 - Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos
AU - Sara Fridovich-Keil; Peter J. Ramadge
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3753
ER -
Sara Fridovich-Keil, Peter J. Ramadge. (2018). Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos. IEEE SigPort. http://sigport.org/3753
Sara Fridovich-Keil, Peter J. Ramadge, 2018. Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos. Available at: http://sigport.org/3753.
Sara Fridovich-Keil, Peter J. Ramadge. (2018). "Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos." Web.
1. Sara Fridovich-Keil, Peter J. Ramadge. Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3753

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