Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural network
IntroductionIn this study, we classified electroencephalography (EEG) data of imagined speech using signal decomposition and multireceptive convolutional neural network. The imagined speech EEG with five vowels /a/, /e/, /i/, /o/, and /u/, and mute (rest) sounds were obtained from ten study particip...
Main Authors: | Hyeong-jun Park, Boreom Lee |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2023-08-01
|
Series: | Frontiers in Human Neuroscience |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2023.1186594/full |
Similar Items
-
Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition
by: Andres Soler, et al.
Published: (2020-02-01) -
Classification of Vowels from Imagined Speech with Convolutional Neural Networks
by: Markus-Oliver Tamm, et al.
Published: (2020-06-01) -
Exploring the Intrinsic Features of EEG Signals via Empirical Mode Decomposition for Depression Recognition
by: Jian Shen, et al.
Published: (2023-01-01) -
Imagined Speech Classification Using EEG and Deep Learning
by: Mokhles M. Abdulghani, et al.
Published: (2023-05-01) -
Automated Multiclass Classification of Spontaneous EEG Activity in Alzheimer’s Disease and Mild Cognitive Impairment
by: Saúl J. Ruiz-Gómez, et al.
Published: (2018-01-01)