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This type of normalisation can be achieved by passing the EEG input signal through a filter that cancels the 1/ f γ spectral behaviour prior to any signal analysis.
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Most of these methods use single channel EEG as their input and are based on a methodology similar to the one previously defined and illustrated in Figure 4- a)[14- a].
The effect of the filter on the phase of the input EEG signal can be seen in Figure 8.
The process is outlined in Fig. 4 with an example of 1000 s of EEG as an input.
Our study results provide evidence about the feasibility of assessing the UX of older adults using a neural network that take as input the EEG signals; the classification accuracy of our neural network ranges from 60.87% to 82.61%.
In these methods we used FFT and autoregressive (AR) model by using maximum likelihood estimation (MLE) of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or nonepileptic seizure.
The decoding results are shown in Figure 13. Figure 13 shows the two different methods' processing capacities for high-frequency components with the same EEG signal as an input.
With this in mind, we explicitly modulated the subjects' attention on the stimulus input and recorded EEG both in a passive listening condition and in an attentive stimulus-oriented task (ignore and attend conditions, respectively).
The features characterizing the original EEG are used as the input of a classifier to differentiate different categories of EEGs.
Although EWT has been proposed by Gilles [26] for building adaptive wavelet to represent the signal to be processed, the author, however, has mentioned that the proposed method might fail to decompose properly when the input signal, like EEG signal (due to nature of multiple channels), compose of more than one chirp which overlaps in both time and frequency domain.
In humans, similar observations have been made [6] where highly synchronized EEG activity occurs in response to stimulus input.
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