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Cognitive information processing (MLR-COGP)

Temporal Interframe Pattern Analysis for Static and Dynamic Hand Gesture Recognition


Hand gesture, a common non-verbal language, is being studied for Human Computer Interaction. Hand gestures can be categorized as static hand gestures and dynamic hand gestures. In recent years, effective approaches have been applied to hand gesture recognition.

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Authors:
Lijun Yin, Tianyang Wang
Submitted On:
12 September 2019 - 12:13pm
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eposter_Paper_2652_Kaoning_Hu.pdf

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[1] Lijun Yin, Tianyang Wang, "Temporal Interframe Pattern Analysis for Static and Dynamic Hand Gesture Recognition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4605. Accessed: Sep. 21, 2019.
@article{4605-19,
url = {http://sigport.org/4605},
author = {Lijun Yin; Tianyang Wang },
publisher = {IEEE SigPort},
title = {Temporal Interframe Pattern Analysis for Static and Dynamic Hand Gesture Recognition},
year = {2019} }
TY - EJOUR
T1 - Temporal Interframe Pattern Analysis for Static and Dynamic Hand Gesture Recognition
AU - Lijun Yin; Tianyang Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4605
ER -
Lijun Yin, Tianyang Wang. (2019). Temporal Interframe Pattern Analysis for Static and Dynamic Hand Gesture Recognition. IEEE SigPort. http://sigport.org/4605
Lijun Yin, Tianyang Wang, 2019. Temporal Interframe Pattern Analysis for Static and Dynamic Hand Gesture Recognition. Available at: http://sigport.org/4605.
Lijun Yin, Tianyang Wang. (2019). "Temporal Interframe Pattern Analysis for Static and Dynamic Hand Gesture Recognition." Web.
1. Lijun Yin, Tianyang Wang. Temporal Interframe Pattern Analysis for Static and Dynamic Hand Gesture Recognition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4605

OVERT SPEECH RETRIEVAL FROM NEUROMAGNETIC SIGNALS USING WAVELETS AND ARTIFICIAL NEURAL NETWORKS


Speech production involves the synchronization of neural activity between the speech centers of the brain and the oralmotor system, allowing for the conversion of thoughts into

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Authors:
Debadatta Dash, Paul Ferrari, Saleem Malik, Jun Wang
Submitted On:
22 November 2018 - 2:18pm
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Dash_oral_GlobalSIP_MEG.pdf

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[1] Debadatta Dash, Paul Ferrari, Saleem Malik, Jun Wang, "OVERT SPEECH RETRIEVAL FROM NEUROMAGNETIC SIGNALS USING WAVELETS AND ARTIFICIAL NEURAL NETWORKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3719. Accessed: Sep. 21, 2019.
@article{3719-18,
url = {http://sigport.org/3719},
author = {Debadatta Dash; Paul Ferrari; Saleem Malik; Jun Wang },
publisher = {IEEE SigPort},
title = {OVERT SPEECH RETRIEVAL FROM NEUROMAGNETIC SIGNALS USING WAVELETS AND ARTIFICIAL NEURAL NETWORKS},
year = {2018} }
TY - EJOUR
T1 - OVERT SPEECH RETRIEVAL FROM NEUROMAGNETIC SIGNALS USING WAVELETS AND ARTIFICIAL NEURAL NETWORKS
AU - Debadatta Dash; Paul Ferrari; Saleem Malik; Jun Wang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3719
ER -
Debadatta Dash, Paul Ferrari, Saleem Malik, Jun Wang. (2018). OVERT SPEECH RETRIEVAL FROM NEUROMAGNETIC SIGNALS USING WAVELETS AND ARTIFICIAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/3719
Debadatta Dash, Paul Ferrari, Saleem Malik, Jun Wang, 2018. OVERT SPEECH RETRIEVAL FROM NEUROMAGNETIC SIGNALS USING WAVELETS AND ARTIFICIAL NEURAL NETWORKS. Available at: http://sigport.org/3719.
Debadatta Dash, Paul Ferrari, Saleem Malik, Jun Wang. (2018). "OVERT SPEECH RETRIEVAL FROM NEUROMAGNETIC SIGNALS USING WAVELETS AND ARTIFICIAL NEURAL NETWORKS." Web.
1. Debadatta Dash, Paul Ferrari, Saleem Malik, Jun Wang. OVERT SPEECH RETRIEVAL FROM NEUROMAGNETIC SIGNALS USING WAVELETS AND ARTIFICIAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3719

Cognitive Analysis of Working Memory Load from EEG, by a Deep Recurrent Neural Network


One of the common modalities for observing mental activity is electroencephalogram (EEG) signals. However, EEG recording is highly susceptible to various sources of noise and to inter-subject differences. In order to solve these problems, we present a deep recurrent neural network (RNN) architecture to learn robust features and predict the levels of the cognitive load from EEG recordings. Using a deep learning approach, we first transform the EEG time series into a sequence of multispectral images which carries spatial information.

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Authors:
Vassilis Athitsos, Nityananda Pradhan, Arabinda Mishra, K.R.Rao
Submitted On:
13 April 2018 - 1:42am
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[1] Vassilis Athitsos, Nityananda Pradhan, Arabinda Mishra, K.R.Rao, "Cognitive Analysis of Working Memory Load from EEG, by a Deep Recurrent Neural Network", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2607. Accessed: Sep. 21, 2019.
@article{2607-18,
url = {http://sigport.org/2607},
author = {Vassilis Athitsos; Nityananda Pradhan; Arabinda Mishra; K.R.Rao },
publisher = {IEEE SigPort},
title = {Cognitive Analysis of Working Memory Load from EEG, by a Deep Recurrent Neural Network},
year = {2018} }
TY - EJOUR
T1 - Cognitive Analysis of Working Memory Load from EEG, by a Deep Recurrent Neural Network
AU - Vassilis Athitsos; Nityananda Pradhan; Arabinda Mishra; K.R.Rao
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2607
ER -
Vassilis Athitsos, Nityananda Pradhan, Arabinda Mishra, K.R.Rao. (2018). Cognitive Analysis of Working Memory Load from EEG, by a Deep Recurrent Neural Network. IEEE SigPort. http://sigport.org/2607
Vassilis Athitsos, Nityananda Pradhan, Arabinda Mishra, K.R.Rao, 2018. Cognitive Analysis of Working Memory Load from EEG, by a Deep Recurrent Neural Network. Available at: http://sigport.org/2607.
Vassilis Athitsos, Nityananda Pradhan, Arabinda Mishra, K.R.Rao. (2018). "Cognitive Analysis of Working Memory Load from EEG, by a Deep Recurrent Neural Network." Web.
1. Vassilis Athitsos, Nityananda Pradhan, Arabinda Mishra, K.R.Rao. Cognitive Analysis of Working Memory Load from EEG, by a Deep Recurrent Neural Network [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2607

CONVOLUTIONAL NEURAL NETWORK APPROACH FOR EEG-BASED EMOTION RECOGNITION USING BRAIN CONNECTIVITY AND ITS SPATIAL INFORMATION


Emotion recognition based on electroencephalography (EEG) has received attention as a way to implement human-centric
services. However, there is still much room for improvement, particularly in terms of the recognition accuracy. In this paper, we propose a novel deep learning approach using convolutional neural networks (CNNs) for EEG-based emotion recognition. In particular, we employ brain connectivity features that have not been used with deep learning models in

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Authors:
Seong-Eun Moon, Soobeom Jang, Jong-Seok Lee
Submitted On:
13 April 2018 - 1:17am
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ICASSP2018_presentation_moon_v.1.pdf

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[1] Seong-Eun Moon, Soobeom Jang, Jong-Seok Lee, "CONVOLUTIONAL NEURAL NETWORK APPROACH FOR EEG-BASED EMOTION RECOGNITION USING BRAIN CONNECTIVITY AND ITS SPATIAL INFORMATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2606. Accessed: Sep. 21, 2019.
@article{2606-18,
url = {http://sigport.org/2606},
author = {Seong-Eun Moon; Soobeom Jang; Jong-Seok Lee },
publisher = {IEEE SigPort},
title = {CONVOLUTIONAL NEURAL NETWORK APPROACH FOR EEG-BASED EMOTION RECOGNITION USING BRAIN CONNECTIVITY AND ITS SPATIAL INFORMATION},
year = {2018} }
TY - EJOUR
T1 - CONVOLUTIONAL NEURAL NETWORK APPROACH FOR EEG-BASED EMOTION RECOGNITION USING BRAIN CONNECTIVITY AND ITS SPATIAL INFORMATION
AU - Seong-Eun Moon; Soobeom Jang; Jong-Seok Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2606
ER -
Seong-Eun Moon, Soobeom Jang, Jong-Seok Lee. (2018). CONVOLUTIONAL NEURAL NETWORK APPROACH FOR EEG-BASED EMOTION RECOGNITION USING BRAIN CONNECTIVITY AND ITS SPATIAL INFORMATION. IEEE SigPort. http://sigport.org/2606
Seong-Eun Moon, Soobeom Jang, Jong-Seok Lee, 2018. CONVOLUTIONAL NEURAL NETWORK APPROACH FOR EEG-BASED EMOTION RECOGNITION USING BRAIN CONNECTIVITY AND ITS SPATIAL INFORMATION. Available at: http://sigport.org/2606.
Seong-Eun Moon, Soobeom Jang, Jong-Seok Lee. (2018). "CONVOLUTIONAL NEURAL NETWORK APPROACH FOR EEG-BASED EMOTION RECOGNITION USING BRAIN CONNECTIVITY AND ITS SPATIAL INFORMATION." Web.
1. Seong-Eun Moon, Soobeom Jang, Jong-Seok Lee. CONVOLUTIONAL NEURAL NETWORK APPROACH FOR EEG-BASED EMOTION RECOGNITION USING BRAIN CONNECTIVITY AND ITS SPATIAL INFORMATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2606