Comparison of logistic regression, support vector machines, and deep learning classifiers for predicting memory encoding success using human intracranial EEG recordings-Reference-Cited by-同舟云学术

Comparison of logistic regression, support vector machines, and deep learning classifiers for predicting memory encoding success using human intracranial EEG recordings

Published:2018-10-23 Issue:6 Volume:15 Page:066028
ISSN:1741-2560
Container-title:Journal of Neural Engineering
language:
Short-container-title:J. Neural Eng.

Author:

Arora Akshay,Lin Jui-Jui,Gasperian Alec,Maldjian Joseph,Stein Joel,Kahana Michael,Lega Bradley^ORCID

Funder

U.S. Department of Defense

Publisher

IOP Publishing

Subject

Cellular and Molecular Neuroscience,Biomedical Engineering

Link

http://iopscience.iop.org/article/10.1088/1741-2552/aae131/pdf

Reference45 articles.

1. Identification of memory reactivation during sleep by EEG classification

2. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

3. Nonlinear dimension reduction for EEG-based epileptic seizure detection

Cited by 33 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. FSTA-Net: Motor Imagery EEG Decoding Based on Frequency–Spatial–Time Features;IEEE Sensors Journal;2024-08-01

2. Construction of Prediction Model Integrating the Radiomics Features of Segmented Clots for Risk Stratification in Acute Pulmonary Embolism;2024-04-22

3. Emerging Trends in EEG Signal Processing: A Systematic Review;SN Computer Science;2024-04-09

4. A Multivariate analysis on evoked components of Chinese semantic congruity: an OP-MEG study with EEG;Cerebral Cortex;2024-04-01

5. Analysis of Machine Learning Models Using Proposed EEG Vowel Dataset;2024-03-27