A Review of Hybrid Brain-Computer Interface Systems

Table 1

A comparison of several different BCI hybrid systems.

Paper #	Hybrid type	System organization	Improvement	Number of subjects	Classification
[15]	ERD, SSVEP	Simultaneous	Accuracy significantly improved compared to ERD and slightly better than SSVEP	14	LDA
[16]	ERD, SSVEP	Sequential	False positive rate was reduced	6	FLDA
[27]	ERD, SSVEP	Sequential	Application of BCI for FES triggering was improved	3	Filters and thresholds
[28]	ERD, SSVEP	Simultaneous	Feedbacks were added to the work done in [15]	12	LDA
[29]	P300, SSVEP	Sequential	Improved ITR	10	FLDA and BLDA
[30]	P300, SSVEP	Sequential	New application (smart home)	3	LDA
[31]	P300, ERD	Sequential	Improvement in application (wheelchair control)	2	Frequency analysis
[32]	P300, ERD	Sequential	Expand control functions in virtual environment	4	SVM and FLDA
[33]	P300, ERD	Simultaneous	Increase reliability	4	Fisher’s discriminant analysis
[34]	ERD, NIRS	Simultaneous	Improvement in classification accuracy and performance	14	LDA
[35]	EEG, EMG	Simultaneous	Improvement in performance	12	Frequency analysis and Gaussian classifier
[36]	ERD, EOG	Simultaneous	Improvement in classification accuracy, reduction in number of electrodes and training time	3	Frequency analysis
[37]	ERD, EOG	Sequential	Improvement in performance	7	LDA