Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces
Abstract
:1. Introduction
1.1. Acquisition Methods
1.2. Communication Approaches
1.3. Process
1.4. Feature Domains
- Discriminative features;
- Statistical features;
- Data-driven adaptive features.
2. Review’s Approach
2.1. Structure of the Work
2.2. Research of Scientific Works
- Which are the most used feature extraction methods in Motor Imagery BCIs?
- Which methods are robust?
- Which approaches have high robustness and high accuracy as well?
- The paper describes a feature extraction method of the MI-BCI field;
- The method described in the paper aims to extract robust brain activity features;
- The presentation of the numerical results of the metrics is necessary.
- Toolboxes are described;
- The paper is not related to the BCI field;
- The paper does not describe a feature extraction method;
- The proceedings were not accessible.
2.3. Research Early Statistics
3. Related Work
4. Efficient and Robust Approaches
4.1. Feature Extraction Methods
4.2. Feature Domains Distribution
4.3. Efficient Approaches
4.3.1. Accuracy
4.3.2. High Robustness
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AAR | Adaptive Autoregressive |
AICA-WT | Automatic Independent Component Analysis with Wavelet Transform |
AR | Autoregressive |
BCI | Brain Computer Interface |
CNN | Convolution Neural Network |
CSP | Common Spatial Patterns |
CWT | Continuous Wavelet Transform |
DCNN | Deep Convolution Neural Network |
dFC | dynamic Functional Connectivity |
DWT | Discrete Wavelet Transform |
ECoG | Electrocorticography |
EEG | Electroencephalography |
EMD | Empirical Mode Decomposition |
EOG | Electrooculogram |
ERD | Event-Related Desynchronization |
ERP | Event-Related Potentials |
ERS | Event-Related Synchronization |
EWT | Empirical Wavelet Transform |
FAWT | Flexible Analytic Wavelet Transform |
FB-TRCSP | Frequency Bank Tikhonov Regularization Common Spatial Patterns |
FBCSP | Frequency Bank Common Spatial Patterns |
FDCSP | Frequency Domain Common Spatial Patterns |
FDF | Frequency Domain Features |
FFNN | Feedforward Neural Network |
FFT | Fast Fourier Transform |
MEG | Magnetoencephalography |
MFCC | Mel-Frequency Cepstral Coefficients |
kNN | k-Nearest Neighbors |
LCD | Local Characteristic-scale Decomposition |
Log-BP | Logarithmic Band Power |
MEWT | Multivariate Empirical Wavelet Transform |
MI | Motor Imagery |
PSD | Power Spectral Density |
RF | Random Forest |
RFB | Reactive Frequency Band |
SGRM | sparse group representation model |
SDI | Successive Decomposition Index |
SRDA | Spectral Regression Discriminant Analysis |
SSAEP | Steady State Auditory Evoked Potentials |
SSSEP | Steady State Somatosensory Evoked Potentials |
SSVEP | Steady State Visually Evoked Potentials |
STFT | Short-Time-Fourier Transform |
SVM | Support Vector Machine |
TFDF | Time-Frequency Domain Features |
TFP | Transfer function perturbationm |
TDF | Time Domain Features |
WDPSD | Weighted Difference of Power Spectral Density |
WT | Wavelet Transform |
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Brainwaves | Hz/V |
---|---|
Delta () | ≤4 Hz, 100 V |
Theta () | 4–8 Hz, <100 V |
Alpha () | 8–13 Hz, <50 V |
Beta () | 13–30 Hz, <30 V |
Gamma () | ≥30 Hz, ≤10 V |
Methods | Citations | Percentages |
---|---|---|
CSP-based | [40,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61] | 44.26% |
PSD methods | [52,62,63,64,65,66,67,68,69] | 21.42% |
Wavelet Transform-based | [48,53,54,64,70,71,72] | 16.65% |
Deep learning | [73,74,75,76] | 9.52% |
Riemmannian | [58,59,77] | 7.14% |
Boltzmann | [68,75] | 4.76% |
LCD | [45,78] | 4.76% |
dFC | [79] | 2.38% |
MFCC | [67] | 2.38% |
log-BP | [48] | 2.38% |
TPF | [80] | 2.38% |
RFB | [81] | 2.38% |
HHT | [82] | 2.38% |
EMD | [63] | 2.38% |
Feature Extraction | Classifier | Kappa | Avg. Accuracy | Dataset | Work |
---|---|---|---|---|---|
EMD-FFT | SVM | 0.9244 | 95.89% | BCI III Dataset 1 | [63] |
RJSPCA | SVM-based | 0.916 | 78% | BCI III Dataset 1 | [46] |
MEWT | FFNN | 0.95 | 99.55% | BCI III IVa | [71] |
CWT | DCNN | 0.9869 | 99.35% | BCI III IVa | [90] |
STFT | DCNN | 0.9798 | 98.7% | BCI III IVa | [90] |
SDI | FFNN | 0.9693 | 97.46% | BCI III IVa | [64] |
SDI | SVM | 0.915 | 93.05% | BCI III IVa | [64] |
FAWT | Subspace kNN | 0.917 | 95.97% | BCI III IVa | [70] |
CSP | SGRM | 0.54 | 77.7% | BCI III IVa | [47] |
MEWT | FFNN | 0.95 | 99.52% | BCI III IVb | [71] |
SDI | FFNN | 0.978 | 97.5% | BCI III V | [64] |
MEWT | FFNN | 0.894 | 91.8% | BCI III V | [71] |
CNN | LSTM | 0.55 | 77.44% | BCI IV 2a | [73] |
CSP/LCD | SRDA | 0.73 | 79.7% | BCI IV 2a | [45] |
CSP | SVM | 0.92 | 96.4% | BCI IV 2b | [51] |
CSP | Bootstrap | 0.92 | 96.5% | BCI IV 2b | [51] |
CNN | LSTM | 0.544 | 65.88% | BCI IV 2b | [73] |
CSP | SGRM | 0.57 | 78.2% | BCI IV 2b | [47] |
Dataset | Channels | Classes | Subjects |
---|---|---|---|
BCI III Dataset 1 | 64 ECoG | 2 | Continuous EEG |
BCI III IVa | 118 EEG | 2 | 5 |
BCI III IVb | 118 EEG | 2 | Continuous EEG |
BCI III V | 32 EEG | 3 | Continuous EEG |
BCI IV 2a | 22 EEG/3 EOG | 4 | 9 |
BCI IV 2b | 3 bipolar EEG/3 EOG | 2 | 9 |
Domain | Classes | Avg. Accuracy | K-Value | Dataset | Work |
---|---|---|---|---|---|
Frequency domain | 2 | 95.89% | 0.9244 | BCI III Dataset 1 | [63] |
Time-Frequency domain | 2 | 99.35% | 0.987 | BCI III IVa | [90] |
Time-Frequency domain | 2 | 99.52% | 0.95 | BCI III IVb | [71] |
Multi-domain | 3 | 97.5% | 0.978 | BCI III V | [64] |
Spatial domain | 4 | 79.7% | 0.73 | BCI IV 2a | [45] |
Spatial domain | 2 | 96.5% | 0.92 | BCI IV 2b | [51] |
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Moumgiakmas, S.S.; Papakostas, G.A. Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces. Computers 2022, 11, 61. https://doi.org/10.3390/computers11050061
Moumgiakmas SS, Papakostas GA. Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces. Computers. 2022; 11(5):61. https://doi.org/10.3390/computers11050061
Chicago/Turabian StyleMoumgiakmas, Seraphim S., and George A. Papakostas. 2022. "Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces" Computers 11, no. 5: 61. https://doi.org/10.3390/computers11050061