Advanced International Journal for Research

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GRAN: An Explainable Generative–Refinement–Annotation Framework for EEG Signal Restoration

Author(s) Mr. Mohammed Roshan Ibrahim, Mr. Prem Shivanand Jogdhankar, Mr. Jawahar M V, Dr. M Krishnamurthy
Country India
Abstract Abstract The use of electroencephalogram (EEG) signals is essential in clinical diagnostics and brain-computer interface (BCI) systems, but is often undermined by physiological and environmental artifacts. Conventional artifact removal methods, such as Independent Component Analysis (ICA) and wavelet-based filtering, can usually involve manual effort and can also potentially corrupt underlying neural patterns. In this paper, the authors present a new three-step pipeline, Generative-Refinement-Annotation (GRAN), a three-step automated EEG signal restoration framework that includes explainability. The Generative module trains a control artifact, i.e. eye blinks, muscle contamination, baseline drift to augment training data. The Refinement module uses a one-dimensional convolutional denoising auto-encoder with skip connectivity to reconstructions of clean signals, using corrupted inputs. The Annotation module generates comprehensible difference maps that indicate the time and space areas where corrections were made, thus making it more transparent to clinical validation. The experimental analysis on BCI Competition IV Dataset 2a shows significant improvements whereby the average SNR improvement of 8.47 dB, Pearson correlation of 0.891 and RMSE reduction of 76.1 percent are achieved over the conventional baselines, which provides an effective and understandable solution to EEG preprocessing.
Keywords EEG signal processing; artifact removal; denoising autoencoder; explainable AI; brain-computer interface; convolutional neural network
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-04-24
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E-ISSN 3048-7641
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