PMID- 29209190
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20201001
IS  - 1662-5188 (Print)
IS  - 1662-5188 (Electronic)
IS  - 1662-5188 (Linking)
VI  - 11
DP  - 2017
TI  - Classification of EEG Signals Based on Pattern Recognition Approach.
PG  - 103
LID - 10.3389/fncom.2017.00103 [doi]
LID - 103
AB  - Feature extraction is an important step in the process of electroencephalogram 
      (EEG) signal classification. The authors propose a "pattern recognition" approach 
      that discriminates EEG signals recorded during different cognitive conditions. 
      Wavelet based feature extraction such as, multi-resolution decompositions into 
      detailed and approximate coefficients as well as relative wavelet energy were 
      computed. Extracted relative wavelet energy features were normalized to zero mean 
      and unit variance and then optimized using Fisher's discriminant ratio (FDR) and 
      principal component analysis (PCA). A high density EEG dataset validated the 
      proposed method (128-channels) by identifying two classifications: (1) EEG 
      signals recorded during complex cognitive tasks using Raven's Advance Progressive 
      Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). 
      Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), 
      Multi-layer Perceptron (MLP), and Naive Bayes (NB) were then employed. Outcomes 
      yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of 
      low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed 
      coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for 
      detailed coefficients (D5) deriving from the sub-band range (3.90-7.81 Hz). 
      Accuracy rates for MLP and NB classifiers were comparable at 97.11-89.63% and 
      91.60-81.07% for A5 and D5 coefficients, respectively. In addition, the proposed 
      approach was also applied on public dataset for classification of two cognitive 
      tasks and achieved comparable classification results, i.e., 93.33% accuracy with 
      KNN. The proposed scheme yielded significantly higher classification performances 
      using machine learning classifiers compared to extant quantitative feature 
      extraction. These results suggest the proposed feature extraction method reliably 
      classifies EEG signals recorded during cognitive tasks with a higher degree of 
      accuracy.
FAU - Amin, Hafeez Ullah
AU  - Amin HU
AD  - Centre for Intelligent Signal and Imaging Research (CISIR), Department of 
      Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri 
      Iskandar, Malaysia.
FAU - Mumtaz, Wajid
AU  - Mumtaz W
AD  - Centre for Intelligent Signal and Imaging Research (CISIR), Department of 
      Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri 
      Iskandar, Malaysia.
FAU - Subhani, Ahmad Rauf
AU  - Subhani AR
AD  - Centre for Intelligent Signal and Imaging Research (CISIR), Department of 
      Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri 
      Iskandar, Malaysia.
FAU - Saad, Mohamad Naufal Mohamad
AU  - Saad MNM
AD  - Centre for Intelligent Signal and Imaging Research (CISIR), Department of 
      Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri 
      Iskandar, Malaysia.
FAU - Malik, Aamir Saeed
AU  - Malik AS
AD  - Centre for Intelligent Signal and Imaging Research (CISIR), Department of 
      Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri 
      Iskandar, Malaysia.
LA  - eng
PT  - Journal Article
DEP - 20171121
PL  - Switzerland
TA  - Front Comput Neurosci
JT  - Frontiers in computational neuroscience
JID - 101477956
PMC - PMC5702353
OTO - NOTNLM
OT  - electroencephalogram (EEG)
OT  - feature extraction
OT  - feature selection
OT  - machine learning classifiers
EDAT- 2017/12/07 06:00
MHDA- 2017/12/07 06:01
PMCR- 2017/01/01
CRDT- 2017/12/07 06:00
PHST- 2017/05/02 00:00 [received]
PHST- 2017/11/01 00:00 [accepted]
PHST- 2017/12/07 06:00 [entrez]
PHST- 2017/12/07 06:00 [pubmed]
PHST- 2017/12/07 06:01 [medline]
PHST- 2017/01/01 00:00 [pmc-release]
AID - 10.3389/fncom.2017.00103 [doi]
PST - epublish
SO  - Front Comput Neurosci. 2017 Nov 21;11:103. doi: 10.3389/fncom.2017.00103. 
      eCollection 2017.