ENIC: Ensemble and Nature Inclined Classification with Sparse Depiction based Deep and Transfer Learning for Biosignal Classification

Sunil Kumar Prabhakar, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


The electrical activities of the brain are recorded and measured with Electroencephalography (EEG) by means of placing the electrodes on the scalp of the brain. It is quite a famous and versatile methodology utilized in both clinical and academic research activities. In this work, sparse depiction is initially incorporated to the EEG signals by means of using K-Singular Value Decomposition (K-SVD) algorithm and the features are extracted by means of using Self-Organizing Map (SOM) technique. The extracted features are initially classified with Extreme Learning Machine (ELM) and the proposed classification versions of ELM such as Ensemble ELM model and Nature Inclined ELM Model. The proposed ensemble ELM model makes use of the combination of Modified Adaboost. RT based on wavelet thresholding with ELM. The proposed Nature Inclined ELM makes use of the combination of some famous swarm intelligence algorithms such as Genetic Algorithm based ELM (GA-ELM), Particle Swarm Optimization based ELM (PSO-ELM), Ant Colony Optimization based ELM (ACO-ELM), Artificial Bee Colony based ELM (ABC-ELM) and Glowworm Swarm Optimization based ELM (GSO-ELM). The​ extracted features are also classified with deep learning methodology by means of utilizing an incidental Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN). Another famous methodology using Non-negative Matrix Factorization (NMF) and Affinity Propagation Congregation based Mutual Information (APCMI) with transfer learning techniques is also proposed and implemented once the sparse modelling is done and the results are analysed. The proposed methodology is implemented for two EEG datasets such as epilepsy dataset and schizophrenia dataset and a comprehensive analysis is done with very promising results.

Original languageEnglish
Article number108416
JournalApplied Soft Computing
Publication statusPublished - 2022 Mar

Bibliographical note

Funding Information:
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by Korea government (No. 2017-0-00451 , Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning ; No. 2019-0-00079 , Artificial Intelligence Graduate School Program (Korea University ))

Publisher Copyright:
© 2022 Elsevier B.V.


  • Deep learning
  • EEG
  • ELM
  • K-SVD
  • SOM
  • Sparse
  • Swarm intelligence
  • Transfer learning

ASJC Scopus subject areas

  • Software


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