Advances in Hybrid Brain-Computer Interfaces: Principles, Design, and Applications

Table 3

Representative applications of hBCI of multimodal signals.

Reference	Hybrid mode	Application	Classifiers	Commands	Accuracy (%)	Improvements
[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