International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Review on Automatic Ocular Artifacts Removal in EEG Using Deep Learning (Combinations of CNNs and Long Short-Term Memory (LSTM)

Abstract

Electroencephalography (EEG) is very tedious for analysis of the dynamic behaviour of human brain. Practically, the analysis of the biomedical signal is no simple as those signals are very dynamic with respect to time, researcher have to make many computations on the different static and dynamic parameters which consumes much time. Accuracy of the signal from object depends on the experimental environment setup and environmental conditions. Enhanced research is conducted on automated EEG signal analysis using artificial intelligence and computer-aided technologies. This would make fast and accurate results. The main objective of this research is to remove unwanted and noisy signals which are mixed in the original signal generated by human brain using deep learning (DL) architectures. We can use the databases available on Kaggle, Web of science which are made free for testing purpose. All datasets and samples will be collected, then analysed and will be processed with different neural architectures and compared. DL in biomedical signal processing is efficient in various research applications. It is very helpful diagnosing the common neurological disorders diagnosis.

Country : India

1 Ms. Swati M. Shelar2 Dr. Yuvraj Parkale

  1. Shivnagar Vidya Prasarak Mandal’s College of Engineering, Malegaon (Bk), Baramati, India
  2. Shivnagar Vidya Prasarak Mandal’s College of Engineering, Malegaon (Bk), Baramati, India

IRJIET, Volume 7, Special Issue of ICRTET- 2023 pp. 51-56

IRJIET.ICRTET13

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