Table 17 Comparison between the proposed approach and state-of-the art methods in this area with different datasets.
From: Segmentation-enhanced approach for emotion detection from EEG signals using the fuzzy C-mean and SVM
References | Methodology | Performance | Datasets |
|---|---|---|---|
Li et al.7 | Heterogeneity and correlation between multimodal signals | Accuracy: 95.89% | DEAP dataset |
Samal and Hashmi9 | Ensemble median empirical mode decomposition | Accuracy: 78% | DEAP dataset |
Turker et al.13 | Tetromino, DWT, mRMR, and weightless majority voting methods | Accuracy:99% | DEAP dataset |
Xu et al.14 | Direct channel selection method based on the mRMR feature selection algorithm | Accuracy: 80.83% | DEAP dataset |
Mei-yu et al.15 | TQWT-feature extraction method with machine learning | Accuracy: 95.33% | SEED dataset |
Fernandes et al.17 | Graph convolutional neural networks | Accuracy: 89.97% | SEED dataset |
Lim and Teo18 | SVM and association rule | Accuracy: 90% | Collected dataset |
Proposed approach | FCM and SVM | Accuracy: 97.66% | Collected dataset |
