*1.4.5. Hilbert-Huang transform (HHT)*

HHT is a self-adaptive data analysis technique comprising empirical mode decomposition (EMD) and Hilbert spectral analysis (HAS) [27]. It can opt stationary and non-stationary signals analysis. An intrinsic mode function (IMF) is an oscillator function with time-varying frequencies capable of depicting the local properties of non-stationary signals [28].

Huang et al. identified high frequency SSVEP signals using HHT in SSVEP-based BCI [18]. HHT remodeled the original signals into 11-order IMF with the help of EMD.

## **1.5. Feature classification**

The classification algorithms designate boundaries between various targets in the feature space on the basis of feature vectors involved considering the as independent variables.

The classification methods for SSVEP signals are heterogeneous in nature, like Bayesian classifier [14], linear discriminant classifier (LDA) [29–31], support vector machine (SVM) [32–35], k-nearest neighbor (k-NNC) [31, 36].

## *1.5.1. Bayesian classifier*

Bayesian statistical classifier obtains the posterior probability P(y|x) as per prior probability of a feature vector for belonging to some particular class. The class that has got the maximum probability is the one to which the particular feature vector belongs.

$$\mathbf{P(y \mid x)} = \frac{\mathbf{P(y)P(x \mid y)}}{\mathbf{P(x)}} \tag{3}$$

#### *1.5.2. Linear discriminant classifier (LDA)*

LDA or Fisher's LDA (FLDA) classifies the data into various classes using hyper planes [36]. This classifier is successfully applied in BCI community despite of high computational time involvement. This classifier traces out an optimal projection that maximizes the distance 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:

$$\mathbf{m(x) = w^T x + w\_0} \tag{4}$$

where, w, x and w<sup>o</sup> implies the weight vector, the input feature vector and the threshold, respectively.
