Special Issue on Deep Learning for Electroencephalography (EEG) Data Analysis-Reference-Cited by-同舟云学术

Special Issue on Deep Learning for Electroencephalography (EEG) Data Analysis

Published:2023-10-19 Issue:20 Volume:13 Page:11475
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Prevete Roberto¹^ORCID,Isgrò Francesco¹^ORCID,Donnarumma Francesco²^ORCID

Affiliation:

1. Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy

2. Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), San Martino della Battaglia 44, 00185 Roma, Italy

Abstract

Brain–computer interfaces (BCI) have emerged as a groundbreaking and transformative technology enabling communication between humans and computers through neural systems, primarily electroencephalography (EEG) [...]

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Link

https://www.mdpi.com/2076-3417/13/20/11475/pdf

Reference6 articles.

1. Vergara-Sánchez, D.L., Calvo, H., and Moreno-Armendáriz, M.A. (2023). Distributional Representation of Cyclic Alternating Patterns for A-Phase Classification in Sleep EEG. Appl. Sci., 13.

2. Machida, K., Nambu, I., and Wada, Y. (2023). Transposed Convolution as Alternative Preprocessor for Brain-Computer Interface Using Electroencephalogram. Appl. Sci., 13.

3. Zheng, Y., Luo, Y., Shao, H., Zhang, L., and Li, L. (2023). DABaCLT: A Data Augmentation Bias-Aware Contrastive Learning Framework for Time Series Representation. Appl. Sci., 13.

4. Kim, H.G., Jeong, D.K., and Kim, J.Y. (2022). Emotional Stress Recognition Using Electroencephalogram Signals Based on a Three-Dimensional Convolutional Gated Self-Attention Deep Neural Network. Appl. Sci., 12.

5. Abdulrahman, A., Baykara, M., and Alakus, T.B. (2022). A novel approach for emotion recognition based on EEG signal using deep learning. Appl. Sci., 12.