scholarly journals EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Suwicha Jirayucharoensak ◽  
Setha Pan-Ngum ◽  
Pasin Israsena
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Principal Component ◽  
Learning Task ◽  
Eeg Signals ◽  
Learning Network ◽  
Deep Learning Network ◽  
High Level

Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers.

A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals

2018 ◽  
Vol 99 ◽  
pp. 24-37 ◽  
Author(s):  
Κostas Μ. Tsiouris ◽  
Vasileios C. Pezoulas ◽  
Michalis Zervakis ◽  
Spiros Konitsiotis ◽  
Dimitrios D. Koutsouris ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Epileptic Seizures ◽  
Eeg Signals ◽  
Short Term ◽  
Term Memory ◽  
Learning Network ◽  
Long Short Term Memory ◽  
Deep Learning Network

scholarly journals Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Guido de Jong ◽  
Elmar Bijlsma ◽  
Jene Meulstee ◽  
Myrte Wennen ◽  
Erik van Lindert ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Normal Brain ◽  
Healthy Controls ◽  
Learning Network ◽  
Deep Learning Algorithm ◽  
Anterior Plagiocephaly ◽  
Deep Learning Network ◽  
Shape Data ◽  
Cranial Shape

Abstract Craniosynostosis is a condition in which cranial sutures fuse prematurely, causing problems in normal brain and skull growth in infants. To limit the extent of cosmetic and functional problems, swift diagnosis is needed. The goal of this study is to investigate if a deep learning algorithm is capable of correctly classifying the head shape of infants as either healthy controls, or as one of the following three craniosynostosis subtypes; scaphocephaly, trigonocephaly or anterior plagiocephaly. In order to acquire cranial shape data, 3D stereophotographs were made during routine pre-operative appointments of scaphocephaly (n = 76), trigonocephaly (n = 40) and anterior plagiocephaly (n = 27) patients. 3D Stereophotographs of healthy infants (n = 53) were made between the age of 3–6 months. The cranial shape data was sampled and a deep learning network was used to classify the cranial shape data as either: healthy control, scaphocephaly patient, trigonocephaly patient or anterior plagiocephaly patient. For the training and testing of the deep learning network, a stratified tenfold cross validation was used. During testing 195 out of 196 3D stereophotographs (99.5%) were correctly classified. This study shows that trained deep learning algorithms, based on 3D stereophotographs, can discriminate between craniosynostosis subtypes and healthy controls with high accuracy.

scholarly journals VISUAL TRACKING UTILIZING OBJECT CONCEPT FROM DEEP LEARNING NETWORK

2017 ◽  
Vol IV-1/W1 ◽  
pp. 125-132 ◽  
Author(s):  
C. Xiao ◽  
A. Yilmaz ◽  
S. Lia
Keyword(s):  
Deep Learning ◽  
Visual Tracking ◽  
Large Scale ◽  
Target Position ◽  
Appearance Model ◽  
Learning Network ◽  
Concept Model ◽  
Deep Learning Network ◽  
High Level ◽  
The Given

Despite having achieved good performance, visual tracking is still an open area of research, especially when target undergoes serious appearance changes which are not included in the model. So, in this paper, we replace the appearance model by a concept model which is learned from large-scale datasets using a deep learning network. The concept model is a combination of high-level semantic information that is learned from myriads of objects with various appearances. In our tracking method, we generate the target’s concept by combining the learned object concepts from classification task. We also demonstrate that the last convolutional feature map can be used to generate a heat map to highlight the possible location of the given target in new frames. Finally, in the proposed tracking framework, we utilize the target image, the search image cropped from the new frame and their heat maps as input into a localization network to find the final target position. Compared to the other state-of-the-art trackers, the proposed method shows the comparable and at times better performance in real-time.

scholarly journals Speech Emotion Recognition From 3D Log-Mel Spectrograms With Deep Learning Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 125868-125881 ◽  
Author(s):  
Hao Meng ◽  
Tianhao Yan ◽  
Fei Yuan ◽  
Hongwei Wei
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Learning Network ◽  
Deep Learning Network

Detecting Fatigue Status of Pilots Based on Deep Learning Network Using EEG Signals

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Edmond Q. Wu ◽  
Ping-Yu Deng ◽  
Xu-Yi Qu ◽  
Zhiri Tang ◽  
Wen-Ming Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Eeg Signals ◽  
Learning Network ◽  
Deep Learning Network

scholarly journals Orientation-Encoding CNN for Point Cloud Classification and Segmentation

2021 ◽  
Vol 3 (3) ◽  
pp. 601-614
Author(s):  
Hongbin Lin ◽  
Wu Zheng ◽  
Xiuping Peng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Feature Learning ◽  
Point Clouds ◽  
Point Sets ◽  
Learning Network ◽  
Rule Structure ◽  
Visual Tasks ◽  
Deep Learning Network ◽  
Point Cloud Classification

With the introduction of effective and general deep learning network frameworks, deep learning based methods have achieved remarkable success in various visual tasks. However, there are still tough challenges in applying them to convolutional neural networks due to the lack of a potential rule structure of point clouds. Therefore, by taking the original point clouds as the input data, this paper proposes an orientation-encoding (OE) convolutional module and designs a convolutional neural network for effectively extracting local geometric features of point sets. By searching for the same number of points in 8 directions and arranging them in order in 8 directions, the OE convolution is then carried out according to the number of points in the direction, which realizes the effective feature learning of the local structure of the point sets. Further experiments on diverse datasets show that the proposed method has competitive performance on classification and segmentation tasks of point sets.

scholarly journals A bagging dynamic deep learning network for diagnosing COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhijun Zhang ◽  
Bozhao Chen ◽  
Jiansheng Sun ◽  
Yamei Luo
Keyword(s):  
Deep Learning ◽  
Chest Radiography ◽  
Learning Algorithm ◽  
Dynamic Learning ◽  
X Ray ◽  
Learning Network ◽  
Feature Extractor ◽  
In Series ◽  
Image Set ◽  
Deep Learning Network

AbstractCOVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagging dynamic deep learning network (B-DDLN) for diagnosing COVID-19 by intelligently recognizing its symptoms in X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, a bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects the feature extractor and bagging classifier in series. Experimental results verify that the proposed B-DDLN achieves 98.8889% testing accuracy, which shows the best diagnosis performance among the existing state-of-the-art methods on the open image set. It also provides evidence for further detection and treatment.

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