Table 2.
Summary of recent decoding algorithms for BCIs
| Method | References | Paradigm | Dataset | Score | Contribution | |
|---|---|---|---|---|---|---|
| Accuracy (%) | AUC | |||||
| Decomposition methods | Chang et al. (2019) | In-house(10,) | An automatic artifact removal approach | |||
| Zhang et al. (2018d) | MI |
BCI Competition 3 Dataset 3a(3, 4 classes) BCI Competition 4 Dataset 2a(9, 4 classes) BCI Competition 4 Dataset 2b(9, 2 classes) |
88 83 84 |
A joint sparse optimization of filter bands and time windows with temporal smoothness constraint | ||
| Jin et al. (2020) | MI |
BCI Competition 3 Dataset 4a(5, 2 classes) BCI Competition 4 Dataset 1(4, 2 classes) |
86 70 |
A fusion framework based on the Dempster-Shafer theory | ||
| Wong et al. (2020a) | SSVEP | Tsinghua Dataset(35, 40 classes) | 82 | A new learning scheme utilizing multiple stimuli for SSVEP-based BCIs | ||
| Xiao et al. (2019) |
aVEP P300 RSVP mVEP |
In-house(12, 2 classes) EPFL Dataset(8, 2 classes) In-house(12, 2 classes) BNCI2015010(11, 2 classes) BNCI2015007(11, 2 classes) |
0.73 0.77 0.82 0.80 0.73 |
Discriminative canonical pattern matching (DCPM) for ERP-based BCIs | ||
| Gurve et al. (2020) | MI | In-house(10, 2 classes) | 96 | Channel selection method with Non-Negative Matrix Factorization (NMF) | ||
| Riemannian geometry | Xu et al. (2020a) | MI |
BCI Competition 4 Dataset 2a(9, 2 classes) Cho2017(49, 2 classes) MunichMI(10, 2 classes) PhysionetMI(109, 2 classes) Shin2017A(25, 2 classes) Weibo2014(10, 2 classes) Zhou2016(4, 2 classes) |
86 73 88 67 66 82 90 |
A dimension reduction method for Riemannian methods which reduces the time cost of computation | |
| Chu et al. (2020) | MI | In-house(12, 6 classes) | 80 | Partial least squares regression with the tangent features | ||
| Deep learning | Lawhern et al. (2018), Waytowich et al. (2018) |
MI MRCP P300 ErrPs SSVEP |
BCI Competition 4 Dataset 2a(9, 4 classes) In-house(13, 2 classes) In-house(15, 2 classes) Kaggle BCI Challenge(26, 2 classes) SCCN CCA Dataset(10, 12 classes) |
67 80 |
0.8 0.92 0.82 |
EEGNet with depth-wise separable convolution |
| Dai et al. (2020) | MI |
BCI Competition 4 Dataset 2a(9, 2 classes) BCI Competition 4 Dataset 2b(9, 2 classes) |
91 87 |
A hybrid-scale CNN architecture with a data augmentation method | ||
| Ravi et al. (2020) | SSVEP |
In-house(21, 7 classes) SCCN CCA Dataset(10, 12 classes) |
92 92 |
A CNN network with complex spectrum features as inputs | ||
| Xing et al. (2020) | SSVEP | In-house(23, 4 classes) | 91 | A comparing network architecture based on CNN inspired by template matching | ||
| Ma et al. (2020) | MI | BCI Competition 4 Dataset 2a(9, 2 classes) | 96 | Band selection and PSD feature extraction as preprocessing steps for inputs | ||
| Transfer Learning | Rodrigues et al. (2018) |
MI P300 SSVEP |
PhysionetMI(109, 2 classes) Cho2017(50, 2 classes) BCI Competition 4 Dataset 2a(9, 4 classes) BNCI2014002(15, 2 classes) BNCI2015001(13, 2 classes) MunichMI(10, 2 classes) In-house(24, 2 classes) SSVEP(12, 3 classes) |
0.67 0.66 0.79 0.73 0.65 0.73 0.75 0.82 |
Riemannian Procrustes Analysis (RPA) with translation, scaling, and rotation transformations | |
| Zhang and Wu (2020) |
MI RSVP ErrPs |
BCI Competition 4 Dataset 1(7, 2 classes) BCI Competition 4 Dataset 2a(9, 2 classes) PhysionetRSVP(11, 2 classes) Kaggle BCI Challenge(16, 2 classes) |
83 76 68 66 |
Manifold embedded knowledge transfer (MEKT) inspired by TCA and JDA | ||
| Li et al. (2020) | P300 |
BNCI2014008(8, 2 classes) In-house(10, 2 classes) |
0.83 0.83 |
Use xDAWN for feature extraction and RIemannian mean method for aligning | ||
| Chiang et al. (2020) | SSVEP | In-house(10, 40 classes) | 77 | A least-squares transformation (LST)-based transfer learning framework for SSVEP BCIs | ||
Scores in transfer learning were reported in the cross-subject scenario with leave-one-subject-out cross-validation, while others were reported in the within-subject scenario with k-fold cross-validation. The first number in the bracket of datasets is the number of subjects and the second is the number of classes