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Reference | Hybrid mode | Application | Classifiers | Commands | Accuracy (%) | Improvements |
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[37] | EMG, EEG | A motor imagery hybrid BCI speller | GMM | 2 | End-users: 91 Able-bodied users: 94 | Better performance over command accuracy |
[38] | EEG, EMG | Home environmental control system | CCA | 4 | 96.3 | Higher control accuracy, security, and interactivity |
[39] | EEG, EOG | AIDS recovery | AR | 4 | 62.28 | Substantially better control over assistive devices |
[40] | EEG, EOG | Mobile robot control | LDA | 9 | 87.3 | Reduce the best completion time |
[41] | EEG, EOG | Hybrid speller system | LDA | 1 | 97.6 | Better performance and usability |
[42] | fNIRS, EEG, eye movement | Control a quadcopter online | LDA | 8 | fNIRS: 75.6 EEG: 86 | Higher accuracy on decoding |
[43] | EEG, fNIRS | Hand movement and recognition | LDA | 2 | 94.2 | Reduce fNIRS delay time in detection |
[44] | EEG, fNIRS | Left- and right-hand motion imagination | DL | 2 | — | Reduce response time |
[45] | EEG, NIRS | Decoding of four movements | LDA | 5 | >80 | Higher classification accuracy |
[46] | EEG, NIRS | Mental state recognition | Meta | 6 | 65.6 | Better performance on mental states classification |
[47] | EEG, MEG | Left- and right-hand motor imagery | CSP, LR | 2 | MEG: 70.6 EEG: 67.7 | Better performance over good within-subject accuracy |
[48] | EEG, NIRS | Classification of mental arithmetic, MI, and idle state | sLDA | 3 | 82.2 ± 10.2 | Higher classification accuracy |
[49] | EEG, MEG | Intersubject decoding of left- vs. right-hand motor imagery | LR, L2, 1-norm regularization | 4 | MEG: 70 EEG: 67.7 | Higher within-subject accuracy |
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