Multi-Kernel Temporal and Spatial Convolution for EEG-Based Emotion Classification

Multi-Kernel Temporal and Spatial Convolution for EEG-Based Emotion Classification

Deep learning using an end-to-end convolutional neural network (ConvNet) has been applied to several electroencephalography (EEG)-based brain–computer interface tasks to extract feature maps and classify the target output. However, the EEG analysis remains challenging since it requires consideration...

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Bibliographic Details
Main Authors: Taweesak Emsawas, Takashi Morita, Tsukasa Kimura, Ken-ichi Fukui, Masayuki Numao
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Sensors
Subjects:
brain–computer interface (BCI)
electroencephalography (EEG)
emotion classification
machine learning
convolutional neural network (ConvNet)
Online Access:https://www.mdpi.com/1424-8220/22/21/8250

Internet

https://www.mdpi.com/1424-8220/22/21/8250

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