Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection
Emotion detection is an important research issue in electroencephalogram (EEG). Signal preprocessing and feature selection are parts of feature engineering, which determines the performance of emotion detection and reduces the training time of the deep learning models. To select the efficient featur...
Main Authors: | Gen Li, Jason J. Jung |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-10-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/10/21/7677 |
Similar Items
-
Preprocessing Acoustic Emission Signal of Broken Wires in Bridge Cables
by: Guangming Li, et al.
Published: (2022-07-01) -
Survey of emotion recognition methods using EEG information
by: Chaofei Yu, et al.
Published: (2022-01-01) -
Generalized Weighted Gauss-Seide Iterative Algorithm based on Preprocessing in MIMO System
by: Chuan-sheng SHI, et al.
Published: (2022-04-01) -
METHODS OF PREPROCESSING SPEECH SIGNALS
by: Valeriy V. Kozlov, et al.
Published: (2024-01-01) -
Towards Effective Emotion Detection: A Comprehensive Machine Learning Approach on EEG Signals
by: Ietezaz Ul Hassan, et al.
Published: (2023-11-01)