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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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
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Zusammenfassung: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 (UNET) and Bat-based UNET signal analysis (BUSA) techniques to classify depression rates in the Electroencephalogram (EEG) datasets. The main objective of including these two techniques is to reveal their effectiveness. It comprises pre-processing, feature extraction, feature selection, and classification stages. The framework excels at noise reduction during pre-processing, enhancing dataset integrity. Feature extraction leverages band power and correlation dimension to extract crucial features. Furthermore, feature selection optimizes classification accuracy by refining the fitness function of bats in the classification layer. The performance of UNET and BUSA are compared based on the following performance evaluating parameters viz. accuracy (Acc), Area Under the Curve (AUC), precision (P), and recall (R) (or sensitivity (Se)). The results indicated that the BUSA technique outperforms the UNET technique.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3563760