Improving EEG classification of alcoholic and control subjects using DWT-CNN-BiGRU with various noise filtering techniques
Electroencephalogram (EEG) signal analysis plays a vital role in diagnosing and monitoring alcoholism, where accurate classification of individuals into alcoholic and control groups is essential. However, the inherent noise and complexity of EEG signals pose significant challenges. This study invest...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2025.1618050/full |
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| author | Nidhi Patel Jaiprakash Verma Swati Jain |
| author_facet | Nidhi Patel Jaiprakash Verma Swati Jain |
| author_sort | Nidhi Patel |
| collection | DOAJ |
| description | Electroencephalogram (EEG) signal analysis plays a vital role in diagnosing and monitoring alcoholism, where accurate classification of individuals into alcoholic and control groups is essential. However, the inherent noise and complexity of EEG signals pose significant challenges. This study investigates the impact of three signal denoising techniques' Discrete Wavelet Transform(DWT), Discrete Fourier Transform(DFT), and Discrete Cosine Transform (DCT) Non EEG signal classification performance. The motivation behind this study is to identify the most effective preprocessing method for enhancing deep learning model performance in this domain. A novel DWT-CNN-BiGRU model is proposed, which leverages CNN layers for spatial feature extraction and BiGRU layers for capturing temporal dependencies. Experimental results show that the DWT-based approach, combined with standard scaling, achieves the highest accuracy of 94%, with a precision of 0.94, a recall of 0.95, and an F1-score of 0.94. Compared to the baseline DWT-CNN-BiLSTM model, the proposed method provides a modest yet meaningful improvement of approximately 17% in classification accuracy. These findings highlight the superiority of DWT as a preprocessing method and validate the proposed model's effectiveness for EEG-based classification, contributing to the development of more reliable medical diagnostic tools. |
| format | Article |
| id | doaj-art-1634261dd2fb495bb5a2f2a6687a8ae7 |
| institution | Kabale University |
| issn | 1662-5196 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj-art-1634261dd2fb495bb5a2f2a6687a8ae72025-08-20T03:47:04ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962025-08-011910.3389/fninf.2025.16180501618050Improving EEG classification of alcoholic and control subjects using DWT-CNN-BiGRU with various noise filtering techniquesNidhi PatelJaiprakash VermaSwati JainElectroencephalogram (EEG) signal analysis plays a vital role in diagnosing and monitoring alcoholism, where accurate classification of individuals into alcoholic and control groups is essential. However, the inherent noise and complexity of EEG signals pose significant challenges. This study investigates the impact of three signal denoising techniques' Discrete Wavelet Transform(DWT), Discrete Fourier Transform(DFT), and Discrete Cosine Transform (DCT) Non EEG signal classification performance. The motivation behind this study is to identify the most effective preprocessing method for enhancing deep learning model performance in this domain. A novel DWT-CNN-BiGRU model is proposed, which leverages CNN layers for spatial feature extraction and BiGRU layers for capturing temporal dependencies. Experimental results show that the DWT-based approach, combined with standard scaling, achieves the highest accuracy of 94%, with a precision of 0.94, a recall of 0.95, and an F1-score of 0.94. Compared to the baseline DWT-CNN-BiLSTM model, the proposed method provides a modest yet meaningful improvement of approximately 17% in classification accuracy. These findings highlight the superiority of DWT as a preprocessing method and validate the proposed model's effectiveness for EEG-based classification, contributing to the development of more reliable medical diagnostic tools.https://www.frontiersin.org/articles/10.3389/fninf.2025.1618050/fullalcoholismdeep learningmachine learningnoise filtering for EEG databrain computer interface |
| spellingShingle | Nidhi Patel Jaiprakash Verma Swati Jain Improving EEG classification of alcoholic and control subjects using DWT-CNN-BiGRU with various noise filtering techniques Frontiers in Neuroinformatics alcoholism deep learning machine learning noise filtering for EEG data brain computer interface |
| title | Improving EEG classification of alcoholic and control subjects using DWT-CNN-BiGRU with various noise filtering techniques |
| title_full | Improving EEG classification of alcoholic and control subjects using DWT-CNN-BiGRU with various noise filtering techniques |
| title_fullStr | Improving EEG classification of alcoholic and control subjects using DWT-CNN-BiGRU with various noise filtering techniques |
| title_full_unstemmed | Improving EEG classification of alcoholic and control subjects using DWT-CNN-BiGRU with various noise filtering techniques |
| title_short | Improving EEG classification of alcoholic and control subjects using DWT-CNN-BiGRU with various noise filtering techniques |
| title_sort | improving eeg classification of alcoholic and control subjects using dwt cnn bigru with various noise filtering techniques |
| topic | alcoholism deep learning machine learning noise filtering for EEG data brain computer interface |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2025.1618050/full |
| work_keys_str_mv | AT nidhipatel improvingeegclassificationofalcoholicandcontrolsubjectsusingdwtcnnbigruwithvariousnoisefilteringtechniques AT jaiprakashverma improvingeegclassificationofalcoholicandcontrolsubjectsusingdwtcnnbigruwithvariousnoisefilteringtechniques AT swatijain improvingeegclassificationofalcoholicandcontrolsubjectsusingdwtcnnbigruwithvariousnoisefilteringtechniques |