*1.4.1. Independent component analysis (ICA)*

**1.2. Visual evoked potential**

92 Evolving BCI Therapy - Engaging Brain State Dynamics

**i.** Morphology of the optical stimuli

**ii.** Frequency of visual stimulation

**iii.** Field stimulation

**a.** Whole field VEPs

**1.3. Steady-state visual evoked potential**

observed around 10, 20, 40 and 80 Hz [11].

**1.4. Feature extraction**

**b.** Half field VEPs **c.** Part field VEPs

**a.** VEPs caused by flash stimulation

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].

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

**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]

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 lat-

Regan experimented with long trains of stimuli that comprised of sinusoidally modulated monochromatic light [8]. Small amplitude stable VEP were generated which were entitled "steady-state" visually evoked potentials (SSVEPs) of the human visionary system. There hence, steady-state visual evoked potentials (SSVEPs) are defined as the potential elicited by

When a user is presented with some periodic stimuli, SSVEP is generated strongly at the occipital areas of the brain [10]. SSVEP is usually acquired from various electrode sites like Oz, O1, O2, Pz, P3, P4, and some surrounding locations to occipital region. While the most commonly used SSVEP frequency range is 4–60 Hz, the resonance phenomenon is generally

Various features are procured from the properties of the brain signals that have discriminative information embedded in them. Various feature extraction techniques are used to extract such

eral sclerosis (ALS) patients or users with uncontrollable eye or neck movements [7].

the change in the visual field with the frequency higher than 6 Hz.

features when overlapped in time and space by several brain signals.

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 independent. ICA can express an EEG signal as following:

$$\mathbf{x}(\mathbf{t}) = \mathbf{f}(\mathbf{s}(\mathbf{t})) + \mathbf{n}(\mathbf{t}) \tag{1}$$

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 independent time courses.

EEG over the visual cortex was fragmented into SSVEP signals and background noise using ICA in the study by Wang et al. [16].

### *1.4.2. Fast Fourier transform (FFT)*

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