Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

Abstract

Brain-computer interface (BCI) is an emerging field, and an increasing number of BCI research projects are being carried globally to interface computer with human using EEG for useful operations in both healthy and locked persons. Although several methods have been used to enhance the BCI performance in terms of signal processing, noise reduction, accuracy, information transfer rate, and user acceptability, the effective BCI system is still in the verge of development. So far, various modifications on single BCI systems as well as hybrid are done and the hybrid BCIs have shown increased but insufficient per - formance. Therefore, more efficient hybrid BCI models are still under the investigation by different research groups. In this review chapter, single BCI systems are briefly dis - cussed and more detail discussions on hybrid BCIs, their modifications, operations, and performances with comparisons in terms of signal processing approaches, applications, limitations, and future scopes are presented. ] merg-ing ERD to control a device such that additional features of one could be used to another. are The common hybrid systems based on signal combinations as well as operation methods, their performances, and improvements are Statistical analysis of BCI and hybrid BCI to P300 and SSVEP are on publications. Transitioning from laboratory to the possible commercial applications with the limi tations This P300, SSVEP, and MI which used EEG sig nals for BCI. Simultaneous operation is very common in P300-SSVEP hybrid and sequential are incorporated in MI-related hybrid experiments. Average accuracy ITR among