A Review of Online Classification Performance in Motor Imagery-Based Brain–Computer Interfaces for Stroke Neurorehabilitation
Abstract
:1. Introduction
2. Motor Imagery Classification Pipelines
2.1. Pre-Processing
2.2. Feature Extraction and Selection
2.3. Classification
3. Methods
3.1. Searching Criteria
3.2. Statistical Tests
4. Results
4.1. Comparison of the Algorithms Used for Classifying Motor Imagery
4.2. Influence of User’s Characteristics to the BCI Performance
4.3. Correlation of Classification Accuracy with Various Parameters of BCI Framework
5. Discussion
6. Limitations
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Author | Classifier | Performance | Feature Extraction | Number of Electrodes | Number of Subjects | Number of Sessions | Number of Trials | Feedback Modality | Participants | Years |
---|---|---|---|---|---|---|---|---|---|---|
Herman [75] | T2FLS | 69% | PSD | 2 | 6 | 7 | 160 | Screen | Healthy | - |
Prasad [19] | T2FLS | 69% | PSD | 2 | 5 | 12 | 160 | Screen | Patients | 59 |
Pan [76] | QDA | 67% | CSP+AR | 3 | 3 | 1 | 230 | Screen | Healthy | - |
Chen [53] | Autoencoder | 74% | CSP | 16 | 4 | 144 | 15 | Screen+ FES | Patients | 62 |
Xu [77] | LDA | 86% | WT+AR | 2 | 8 | 3 | 40 | Robotic | Healthy | 27 |
Irimia [78] | LDA | 95% | CSP | 45 | 2 | 10 | 240 | Screen+FES | Patients | 50 |
Zhao [79] | SVM | 74% | CSP | 41 | 5 | 1 | 40 | Screen | Healthy | - |
Irimia [80] | LDA | 87% | CSP | 64 | 5 | 18 | 160 | Screen+FES | Patients | 60 |
Tayeb [81] | CNN | 84% | FT | 3 | 20 | 2 | 90 | Robotic | Healthy | 31 |
Karacsony [82] | CNN | 72% | - | 16 | 10 | - | - | VR | Healthy | 25 |
Vidaurre [83] | LDA | 82% | CSP | 64 | 15 | 1 | 300 | Robotic | Healthy | - |
Raza [84] | CNN | 70% | CSP | 12 | 10 | 1 | 120 | Robotic | Patients | 41 |
Mousavi [85] | LR | 62% | CSP | 64 | 12 | 1 | 180 | Screen | Healthy | 20 |
Benzy [86] | NB | 68% | PLV | 64 | 16 | 2 | 50 | Screen | Patients | 50 |
Achanccaray [21] | SVM | 93% | CSP | 16 | 20 | - | - | VR+FES | Healthy | 26 |
Gaur [22] | LDA | 80% | CSP | 12 | 10 | 3 | 40 | Robotic | Patients | 41 |
Vasilyev [87] | NB | 80% | CSP | 30 | 11 | 6 | - | Screen | Healthy | 26 |
Zhang [16] | LDA | 75% | WT+AR | 16 | 7 | 3 | 200 | Screen | Patients | 60 |
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Vavoulis, A.; Figueiredo, P.; Vourvopoulos, A. A Review of Online Classification Performance in Motor Imagery-Based Brain–Computer Interfaces for Stroke Neurorehabilitation. Signals 2023, 4, 73-86. https://doi.org/10.3390/signals4010004
Vavoulis A, Figueiredo P, Vourvopoulos A. A Review of Online Classification Performance in Motor Imagery-Based Brain–Computer Interfaces for Stroke Neurorehabilitation. Signals. 2023; 4(1):73-86. https://doi.org/10.3390/signals4010004
Chicago/Turabian StyleVavoulis, Athanasios, Patricia Figueiredo, and Athanasios Vourvopoulos. 2023. "A Review of Online Classification Performance in Motor Imagery-Based Brain–Computer Interfaces for Stroke Neurorehabilitation" Signals 4, no. 1: 73-86. https://doi.org/10.3390/signals4010004