**6. Limitations**

Phenomenon of acquiring and processing information by human brain is still unknown. A very few hybrid BCI are experimented with target users and most of the subjects are healthy with small sample size. Rehabilitation using BCI is still not used in clinical practice [97]. Various methods have been incorporated to improve accuracy and ITR, and some hybrid with different classifiers combination have shown some improved results, but mostly subject's specific. Type and design of electrodes have impact on subject's head which influence EEG signals and demands for high compatible systems. These systems are not free from physical and mental fatigue that challenges their adaptability. Moreover, there are obstacles in EEG acquisition due to electrodes placement, skull muscle movement, environment noises, limitations in hardware, and their calibrations. Two or more tasks need to be performed simultaneously in hybrid that might increase mental workload and cause discomfort to some users. Due to complexity, prior knowledge is required to use hybrid systems for target users. This demands for further research and hybrid BCI is still under development inside laboratory [57, 70].

to an artificially intelligent machine which adapts to learn from the input attributes without being explicitly programmed. So, a BCI illiterate at one point of time may adapt to learn with

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

http://dx.doi.org/10.5772/intechopen.75132

131

Human brain is a nonlinear dynamic system behaving as a chaotic and fractal system [104]. Therefore, EEG is a complex, nonlinear, and nonstationary signal. However, EEG signals have been analyzed based on linear/nonlinear and stationary/nonstationary techniques for feature extraction and classification. Fourier transforms, wavelet decomposition, power spectral density, autoregression, CCA, LDA, SVM, and CSP are some of the linear methods for EEG classification. However, only commonly used linear classifiers in hybrid BCI are discussed here. Due to the dynamical nature of the brain and the associated EEG signal, widely used linear approaches are not enough to obtain promising results. Therefore, the nonlinear dynamical behavior of EEG should be carefully considered during brain signal analysis. EEG signals need to be analyzed along with the dynamic states to reveal additional features that cannot be

EEG signals were analyzed dynamically in [105] to identify and code the attractors related to mental states using artificial neural network. It was shown that binary patterns of attractors resulting from neural firing of identical cognitive or sensory stimuli are similar, but they might appear as distinct features with different stimuli. The chaotic behavior of attractors highlights the fact that the neural signals are coherent. Indeed, brain dynamics has unveiled an emerging area of research to quantify information using attractors and fractals from EEG signals for useful operations applying hybrid BCI. It should be noted that attractors and fractals are the dynamic variables to measure complexity (correlation dimension and Hurst exponent) and stability (Lyapunov exponent and entropy) of EEG data [106–108]. The phenomenon of brain receiving the sensory inputs, storing the information, and processing output is still unknown. Hybrid BCI can be an efficient tool to transform the interesting brain dynamics into actions. In this chapter, hybrid BCI is reviewed, and advancements from single BCI system to hybrid BCI systems, associated signal processing methods, usages, shortcomings and future scopes are discussed. The common hybrid systems based on signal combinations as well as operation methods, their performances, and improvements are mentioned. Statistical analysis of BCI and hybrid BCI related to P300 and SSVEP are illustrated based on publications. Transitioning from laboratory to the possible commercial applications is well discussed along with the limitations onward. This review illustrates P300, SSVEP, and MI which are mostly used EEG signals for BCI. Simultaneous operation is very common in P300-SSVEP hybrid and sequential operations are incorporated mostly in MI-related hybrid experiments. Average accuracy and ITR among reviewed hybrid BCI papers are 90% and 28 bits/min, respectively, which demand

continuous trials due to dynamic brain states [103].

assessed with the linear methods.

the need for a more efficient hybrid BCI system.

Bijay Guragain, Ali Haider and Reza Fazel-Rezai\*

\*Address all correspondence to: reza.fazelrezai@engr.und.edu

Biomedical Signal and Image Processing Laboratory, University of North Dakota, USA

**Author details**
