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- Title
Design and Enactment Evaluation of Adaptive Artifacts Removal from EEG Signal Records.
- Authors
Perumal, Ramu Kaliya; Raju, S. V. S. Rama Krishnam; Chandanan, Amit Kumar; Manasa, Dasari; Pandey, Rajeev; Kumar, Modugu Dileep; Vishwanath, Neerugatti Varipallay; Kashyap, Tanuja
- Abstract
Various factors, such as electrical power lines, EOG or ECG interference, contribute to artefacts in Electroencephalogram (EEG) data, complicating EEG analysis and clinical interpretation. Developing specialized filters to mitigate these artefacts is crucial. Artefacts from eye movements and blinks have been extensively studied, prompting the development of an FLM optimization-based learning technique for a Neural Network (NN)-enhanced adaptive filtering model to address them. Initially, Firefly (FF) and LM adaptive filter algorithms analyze EEG data to determine optimal weights. These weights are then incorporated into the NN for adaptive filtering. The resulting technique effectively eliminates artefacts. Performance evaluation, based on Signal to Noise Ratio (SNR), Root Mean Square Error (RMSE), Mean Square Error (MSE), and computing time, compares the proposed method with conventional approaches. Results demonstrate a significant 92% improvement in SNR, indicating the efficiency of the proposed technique. This advancement holds promise for enhancing EEG data quality and facilitating more accurate clinical assessments.
- Subjects
STANDARD deviations; ELECTRIC power; SIGNAL-to-noise ratio; ADAPTIVE filters; ELECTRIC lines; BLINKING (Physiology)
- Publication
Revue d'Intelligence Artificielle, 2024, Vol 38, Issue 4, p1353
- ISSN
0992-499X
- Publication type
Article
- DOI
10.18280/ria.380429