**1.2. Visual evoked potential**

Visual evoked potentials (VEPs) are the brain activity modulations occurring in the visual cortex after encountering visual stimulus [3]. They are easy to detect as the movement of stimulus closer to the central visual field immensely enhances the amplitude of VEPs [4].

The feature extraction in SSVEP signals was often done with the study of amplitude in the target frequency [12–14], followed by independent component analysis (ICA) [16], the fast Fourier transform (FFT) [15], continuous wavelet transform (CWT) [17, 18], Hilbert-Huang

ICA follows a statistical procedure for separating a set of mixed signals into its sources without any presumptuous information regarding the nature of the signal. The only criteria that need to be followed are that the unspecified underlying sources must be statistically mutually

where, f is some unknown mixer function, s(t) is the source vector, n(t) is the additive arbitrary noisy vector and x(t) is the resultant EEG signal. ICA mainly follows two approaches: spatial ICA that extricates out independent spatial maps and temporal ICA that extricates out

EEG over the visual cortex was fragmented into SSVEP signals and background noise using

Fourier transform (FT) comprises fast Fourier transform (FFT) and fiscrete Fourier transform (DFT). Wang et al. used 256-point FFT for transforming EEG signals to corresponding frequency domain representation. This was represented in terms of five frequencies: 9, 11, 13, 15 and 17 Hz [21]. Mouli et al. considered the maximum amplitudes of the FFT as the prime parameter for

The most commonly endorsed EEG recording methods emanated monopolar signals or

<sup>L</sup> = yOx <sup>M</sup> − yO1 <sup>M</sup> + yO2 M \_\_\_\_\_\_ <sup>2</sup>

Laplacian signals are extracted considering both the sides (like Cz utilizes C3 and C4) [23].

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 basi-

<sup>M</sup> = yOx − yA<sup>2</sup>

x(t) = f(s(t)) + n(t) (1)

SSVEP-Based BCIs

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

transform (HHT) [19, 20] or the PSD [21] can be used.

independent. ICA can express an EEG signal as following:

differentiating various target stimuli of 7, 8, 9 and 10 Hz [22].

cally the convolution of signal with the wavelet function [25]:

Laplacian filtered signals. For example, channel Oz consisted as follows:

Monopolar: yOx

Laplacian: yOx

*1.4.1. Independent component analysis (ICA)*

independent time courses.

*1.4.3. Spatial filtering*

ICA in the study by Wang et al. [16].

*1.4.4. Continuous wavelet transform (CWT)*

*1.4.2. Fast Fourier transform (FFT)*

Based on following criteria, VEPs are classified into different categories [5]:

	- **a.** VEPs caused by flash stimulation
	- **b.** VEPs caused by graphic patterns like checkerboard lattice, gate
	- **a.** Transient VEPs (TVEPs): VEPs with visual stimulation frequency below 6 Hz
	- **b.** Steady-state VEPs (SSVEPs): VEPs with visual stimulation frequency above 6 Hz [3, 6]
	- **a.** Whole field VEPs
	- **b.** Half field VEPs
	- **c.** Part field VEPs

While the user needs to gaze at the screen and keep his eyes fixed on one particular point. These exogenous signals are not suitable while dealing with advanced level amyotrophic lateral sclerosis (ALS) patients or users with uncontrollable eye or neck movements [7].
