Deep Learning Model for Analyzing EEG Signal Analysis

To analyze the physiological information within the acquired EEG signal is very cumbersome due to the possibility of several factors, viz. noise and artifacts, complexity of brain dynamics, and inter-subject variability. To address these issues, this paper compares a U-shaped encoder-decoder network...

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Vydáno v:	IEEE access Ročník 13; s. 91034 - 91045
Hlavní autoři:	Gupta, Varun, Kumar, Vivek, Prince, Singh, Saurabh, Lee, Young-Seok, Ra, In-Ho
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy band power Bat-based UNET signal analysis (BUSA) Brain Brain modeling Classification Classification algorithms Convolutional neural networks correlation dimension Datasets Deep learning EEG datasets Electrodes Electroencephalography Emotion recognition Encoders-Decoders Feature extraction Feature selection Machine learning Noise reduction Performance evaluation Signal analysis U-shaped encoder-decoder network (UNET)
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
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