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Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Interests: deep learning; computer vision; audio source separation; music information retrieval
Special Issues, Collections and Topics in MDPI journals

Dr. Ilias Theodorakopoulos
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Guest Editor

Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Interests: deep learning; machine learning; manifold learning; image analysis; biomedical signal processing; biomedical image analysis; pattern recognition

Special Issue Information

Dear Colleagues,

Deep learning has been a major revolution in modern information processing. All major application areas have been affected positively by this breakthrough, including image, video and signal processing. Deep learning has rendered traditional approaches that employ man-made features obsolete by allowing neural networks to extract optimized features through learning. Current networks, featuring large networks with millions of parameters, can address many image, video and signal processing problems with top performance. The use of GPUs for training these networks is detrimental. In addition, extensions of traditional learning strategies, such as contrastive, semi-supervised learning and teacher-student models, have addressed the requirement for large amounts of annotated data.

The aim of this Special Issue is to present and highlight the newest trends in deep learning for image, video and signal processing applications. These may include, but are not limited to, the following topics:

Object detection;
Semantic/instance segmentation;
Image fusion;
Image/video spatial/temporal inpainting;
Generative image/video processing;
Image/video classification;
Document image processing;
Image/video processing for autonomous driving;
Audio processing/classification;
Audio source separation;
Contrastive/semi-supervised learning;
Knowledge distillation methods.

Dr. Nikolaos Mitianoudis
Dr. Ilias Theodorakopoulos
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

image classification
semantic/instance segmentation
semi-supervised learning
contrastive learning
generative image/video creation
audio source separation
knowledge distillation

Published Papers (1 paper)

Research

Open AccessArticle

A Deep Neural Network for Working Memory Load Prediction from EEG Ensemble Empirical Mode Decomposition

Sriniketan Sridhar

Anibal Romney

and

Vidya Manian

Information 2023, 14(9), 473; https://doi.org/10.3390/info14090473 - 25 Aug 2023

Viewed by 475

Abstract

Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) are frequently associated with working memory (WM) dysfunction, which is also observed in various neural psychiatric disorders, including depression, schizophrenia, and ADHD. Early detection of WM dysfunction is essential to predict the onset of MCI and AD. Artificial Intelligence (AI)-based algorithms are increasingly used to identify biomarkers for detecting subtle changes in loaded WM. This paper presents an approach using electroencephalograms (EEG), time-frequency signal processing, and a Deep Neural Network (DNN) to predict WM load in normal and MCI-diagnosed subjects. EEG signals were recorded using an EEG cap during working memory tasks, including block tapping and N-back visuospatial interfaces. The data were bandpass-filtered, and independent components analysis was used to select the best electrode channels. The Ensemble Empirical Mode Decomposition (EEMD) algorithm was then applied to the EEG signals to obtain the time-frequency Intrinsic Mode Functions (IMFs). The EEMD and DNN methods perform better than traditional machine learning methods as well as Convolutional Neural Networks (CNN) for the prediction of WM load. Prediction accuracies were consistently higher for both normal and MCI subjects, averaging 97.62%. The average Kappa score for normal subjects was 94.98% and 92.49% for subjects with MCI. Subjects with MCI showed higher values for beta and alpha oscillations in the frontal region than normal subjects. The average power spectral density of the IMFs showed that the IMFs (p = 0.0469 for normal subjects and p = 0.0145 for subjects with MCI) are robust and reliable features for WM load prediction. Full article

(This article belongs to the Special Issue Deep Learning for Image, Video and Signal Processing)

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