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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE access Jg. 13; S. 91034 - 91045
Hauptverfasser:	Gupta, Varun, Kumar, Vivek, Prince, Singh, Saurabh, Lee, Young-Seok, Ra, In-Ho
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!