Table 5.
Related studies about EEG application and their limitations
| Objective | Features | Algorithm | References | Results (%) |
|---|---|---|---|---|
| Predicting relapse | Spectral power | Logistic Regression | Bauer (2001) | 75 |
| Spectral power with Hjorth’s features | Discriminant Analysis and ANN | Winterer et al. (1998) | 83–85 | |
| P300 | Discriminant Function Analysis | Wan et al. (2010) | 63.9 | |
| Screening alcoholics | Spectral power and coherence | Locally Weight Regression | Guntaka and Tcheslavski (2013) | 66.45 |
| ERP’s components | ANN | Lopes et al. (2004) | 71 | |
| ERP’s components | Learning Vector Quantization | Lopes et al. (2005) | 80 | |
| Gamma Visual Evoked Potential (VEP) power | Least square Support Vector Machine (SVM) | Shooshtari and Setarehdan (2010) | 82.98 | |
| Raw EEG in F4 and P8 | Hidden Markov Model | Zhong and Ghosh (2002) | 90.50 | |
| Gamma VEP | MLP – BP with Elliptic filter | Kanna et al. (2005) | 91 | |
| Approximate Entropy (ApEn), Sample Entropy (SampEn), Largest Lyapunov Exponent (LLE), (high order spectra) HOS | SVM | Acharya et al. (2012) | 91.70 | |
| HOS | Fuzzy Sugeno Classifier | Faust et al. (2013b) | 92.40 | |
| ERP’s components | Random Forest | Kuncheva and Rodríguez (2013) | 94.50 | |
| Multi gamma band VEP | MLP | Rangaswamy et al. (2007) | 94.55 | |
| Yule Walker coefficient | Artificial NN | Ek et al. (2013) | 95.00 | |
| Wavelet Relative Power | K-nearest Neighbor | Faust et al. (2013a, b) | 95.80 | |
| Horizontal Visibility Graph Entropy | K-nearest Neighbor | Zhu et al. (2014) | 95.80 | |
| Gamma VEP | PCA | Ong et al. (2005) | 95.83 | |
| Gamma VEP | MLP | Palaniappan et al. (2002) | 96.10 | |
| Gamma VEP | LDA | Palaniappan (2005) | 97.40 | |
| Gamma VEP | KNN | Palaniappan (2006) | 98.71 | |
| Spectral power using Haar wavelet | Multilayer Perceptron Network (MLP) | Kousarrizi et al. (2009) | 98.83 | |
| Spectral Entropy | Probabilistic Neural Network | Padmanabhapillai et al. (2006) | 99.00 | |
| VEP energy in occipital | KNN OR Support Vector Data Description | Zúquete et al. (2010) | 99.20 | |
| Mean and variance of signals | Bayes with KNN and PCA (claim to classify AA) | Yazdani and Setarehdan (2007) | 100 | |
| Classify epileptic and alcoholic | Recurrence Quantification Analysis (RQA) | Gaussian mixture model (GMM) | Ng et al. (2012) | 98.6 |