Table 2.
Summarizes some of the presented paper details such as subject signal type and electrode location.
| Type of Application | Representative Works | BCI Paradigm | Description | No. of Subjects | Signal Type | Electrode Number | Accuracy |
|---|---|---|---|---|---|---|---|
| BCI-Assistive robot for Rehabilitation |
Soekadar, S R et al. [99] | MI- EEG HOVs’ EOG |
Help paraplegic patients to control the exoskeleton hand for daily life activity | 6 | EEG-EOG | C3 | 84.96 ± 7.19% |
| Zhang Jinhua and et al. [100] | MI-EEG Left/right looking-EOG |
6 | EEG-EOG-EMG | 40 Ag/AgCl channels placed 10–20 System |
93.83% | ||
| N. Cheng et al. [101] | MI | Studied BCI-based Soft Robotic Glove applicability for stroke patient rehabilitation in daily life activities. | 11 | EEG | 24 Ag/AgCl channels placed 10–20 System |
- | |
| Mads Jochumsen and et al. [102] | MI | Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton |
11 | EEG | F1, F2, C3, Cz, C4, P1, and P2 | 86 ± 12%; | |
| Kathner et al. [103] | P300 | Check if VR devices can achieve the same precision and rapid data transmission compared to the regular display methods | 18 + 1 person (ALS). 80 years |
EEG-VR | Fz, Cz, P3, P4, PO7, POz, PO8, Oz | 96% | |
| BCI-virtual reality based for rehabilitation |
Ortner et al. [104] | MI | training stroke patients to imagine left and right hands movements in VR scenes | 3 | EEG-VR | 63 positions | mean 90.4% |
| Robert Lupu et al. [105] | MI | Flow instruction of virtual therapists, to control virtual characters in VR scenes using MI. Motor function was improved. | 7 | EEG-FES EOG |
16 sensorimotor areas of channels sensorimotor areas | mean85.44% |