**5. Applications**

Hybrid BCI is in the state of development and various BCI signals are combined to form a hybrid enhancing performance for numerous experimental-related applications which are summarized in **Table 2**. Most of the applications are based on wheelchair control. Other


**Table 2.** Hybrid BCI applications.

**4.4. Canonical correlation analysis (CCA)**

128 Evolving BCI Therapy - Engaging Brain State Dynamics

*Yf* =

The maximum correlation among X and *Yf*

**4.5. Common spatial patterns (CSPs)**

stationary spatial filters and the columns of *W*−1

was replaced by Laplacian filter in CSP algorithm.

**5. Applications**

improves SNR, classification accuracy, and ITR [81, 83].

channels data and *Yf*

harmonics, the reference signals *Yf*

CCA is a multivariate statistical method to analyze frequency components of SSVEP in EEG [82]. It extracts narrowband frequency components of SSVEP in EEG using maximum correlation between reference stimulus signals and EEG signals. Suppose X be the EEG all

are given as:

⎛

sin(2*ft*) cos(2*ft*) . . . sin(2*Nft*) cos(2*Nft*)

is obtained as:

⍴*max* = max[*correlation coefficient* (*X*, *Yf*)] (6)

CCA is more common method for analysis of SSVEP signals in frequency domain that

CSP is used to analyze spatial patterns of MI calculating spatial filters to find optimum variances for two different classes of EEG data. It uses simultaneous diagonalization of two covariance matrices, and the spatially filtered signal Z of a single-trial EEG data is obtained as:

*Z* = *WE* (7)

where E is *N* × *T* matrix of single-trial raw EEG data, N is the number of channels, T is the number of measurement samples per channel, and W is CSP projection matrix. The rows of W are

of motor action are dependent on the specific region of brain like left-hand movement on right cerebral hemisphere [84]. A higher classification accuracy for multitask learning with very few training samples among 19 healthy subjects was achieved by [85] in which spatial filter

Hybrid BCI is in the state of development and various BCI signals are combined to form a hybrid enhancing performance for numerous experimental-related applications which are summarized in **Table 2**. Most of the applications are based on wheelchair control. Other

are common spatial patterns. Spatial patterns

⎜

⎝

be the reference signals at f Hz stimulus frequency with N number of

⎞

⎟

(5)

⎠

applications include use of computer and communication, prosthetics using artificial limbs, advanced functional electrical therapy, monitoring ALS patients, entertainment and care in virtual smart home where MI is used mostly in prosthetics. Although BCI application is potentially safe, it needs regulatory approval before the experiment.
