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

Table 2

Representative applications of multisensory hBCIs.

Reference	Hybrid mode	Application	Classifiers	Commands	Accuracy (%)	Improvements
[30]	P300, visual, audio	P300 audiovisual speller	Regularized linear LR	—	>80	Improvement in performance
[31]	Visual, audio	Consciousness detection in patients with DOC	SVM	2	>64	Better performance and feasible to patients with DOC
[32]	Visual, audio	Visual-auditory speller	LDA	30	87.7 (chance level <3%)	Better BCI performance
[33]	Visual, audio	Awareness detection	SVM	2	95.67	Better performance over auditory-only and visual-only systems
[34]	Auditory, tactile, visual, P300	Visual saccade-independent BCI	BLDA	4	88.67	Better online performance
[35]	Auditory, tactile, P300	Tactile and bone-conduction BCI	SW-LDA	6	70	Higher classification accuracy
[36]	Audio, tactile	Robot gesture	FGMMs, SVM	10	92.75	Better performance over framework