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

Table 1

Representative hBCI applications of multiple brain patterns.

Reference	Hybrid mode	Application	Classifiers	Commands	Accuracy (%)	Improvements
[19]	SSVEP, P300, MI	Humanoid machine navigation	CCA	6	P300: 84.6,; SSVEP: 84.1	Better commands performance in navigation and exploration
[20]	SSVEP, P300	Wheelchair control with stop command	SVM	2	>80	Higher detection accuracy and low response time
[21]	SSVEP, P300	Target selection speller	SW-LDA	9	93.3	More effective in target discrimination
[22]	SSVEP, P300	Cursor control	SVM	9	>90	Higher accuracy and better commands performance
[11]	SSVEP, P300	Multiple option selection	CCA, LDA	4	P300: 99.9; SSVEP: 67.2	Better performance and user-friendly
[23]	P300, SSVEP	Speller	SW-LDA	36	93.85	Higher accuracy
[24]	MI, SSVEP	Play Tetris games in MI-SSVEP paradigm	LDA, CSP, CCA	4	MI: 87.01; SSVEP: 90.26	Higher accuracy
[25]	MI, SSVEP	Hybrid BCI system of MI and SSVEP	LDC	2	85.6 ± 7.7	Better classification performance
[9]	MI, SSVEP, visual, and auditory	Wheelchair control	SVM	6	—	Multidegree control commands
[26]	MI, SSVEP	Hybrid BCI system with feedback	LDA	2	≥83	Better MI training performance
[27]	SSVEP, MI	Control commands	CCA	5	MI: 93.3; SSVEP: 89	Better performance and easiness for users
[16]	MI, P300	2-D cursor control	SVM	2	>80	Multiple-degree control
[17]	P300, MI	BCI mouse-based web browser	SVM	3	93.21	Multidegree control with a feasible BCI mouse
[28]	P300, MI	BCI wheelchair with direction and speed control	LDA	4	83.10 ± 2.12	Direction and speed control