*1.4.4. Continuous wavelet transform (CWT)*

Wavelet transform (WT) is best suited to extract information from nonstationary signals as it extends a versatile method for representation of time-frequency of a signal [24]. CWT is basically the convolution of signal with the wavelet function [25]:

$$\mathbf{w}(\mathbf{s},\tau) = \underset{\omega}{\mathcal{J}} \mathbf{x}(t)\boldsymbol{\psi} \* (t)dt \tag{2}$$

between the classes. The decision hyper plane that divides the feature space into various classes is perpendicular to the projection direction [37]. The hyper plane is expressed as:

SVM classifies the feature vectors into various classes by the concept of construction of one or more hyper planes. This classifier differs from LDA, as in this, the decision boundary or hyper plane escalates the margins that implies, the distance between the decision boundary and the training sample nearest to it [38]. While the hyper plane separates the training data set with maximal margin, it also maps them to a higher dimensional space [39]. The decision boundary followed up in SVM may be linear as well as non-linear depending upon the choice of kernel function (linear, cubic, polynomial, Gaussian or radial basis

The classification principle of k-NNC is that the features belonging to different classes get flocked up in different clusters while keeping the adjacent neighbors in one cluster. It considers k metric distances between the testing dataset features and those of the nearest classes for classifying a test feature vector. Although classification with k-NNC reduces the error probability in the decision but still it is not so commonly used in BCI

A BCI is an artificial intelligence system that has the ability to identify particular set of patterns in the brain signals to provide an additional output channel for the control of artificial devices like restoring motor function, robot arm, communication program, etc. [43, 44].

**i.** Time modulated VEP (t-VEP) BCI: In this BCI, the follow up of flash sequences of various stimuli are orthogonal in time, that is, they are strictly non overlapping or stochastic in

**ii.** Frequency modulated VEP (f-VEP) BCI: In this BCI, stimuli are made to flash at some exclusive frequency and the potential evoked is generated with the fundamental frequency

SSVEPs based BCIs are classified into following categories:

same as that of the stimuli as it harmonics.

where, w, x and w<sup>o</sup>

*1.5.3. Support vector machine (SVM)*

**1.6. k-Nearest neighbor (k-NNC)**

respectively.

(RBF)) [40].

community [41].

nature.

**2. SSVEP in BCIs**

m(x) = w<sup>T</sup><sup>x</sup> + w<sup>o</sup> (4)

SSVEP-Based BCIs

95

http://dx.doi.org/10.5772/intechopen.75693

implies the weight vector, the input feature vector and the threshold,

where, ψ\*(t) is the complex conjugate wavelet function, x(t) is the particular function and w(s,τ) is the wavelet coefficient corresponding to frequency related with scale s and time τ of the involved wavelet function. CWT works like template matching just like matched filter where cross variance is calculated for the signal and some predefined waveform [26].

Zhang et al. established the use of CWT technique for extracting features and classifying them in SSVEP-based BCI [16]. Kumari et al. transformed the CWT coefficients into feature vectors for tracing out the site of high frequency SSVEP components [17].
