to average power with a frequency band.’
Each power was normalized by the valve of the baseline in same frequency band across the scalp. (N = 3)
NEEGS, F = EEGS, F1/N * S = 1NBEEGS, F
To investigate the asymmetric response of alpha power of PFC, a relation ratio (RR) = RP − LPRP + LP * 100, RP = alpha power from right hemisphere of PFC (FP2) & LP is from (FP1).
SVM was used utilizing a “non-linear kernel function” to recognize the responses of EEG signals. In one-sample test setting the median to 128 with a range between 0 and 255, it was seen that values went from highest to lowest in favorite songs, “K448” and High Focus, in that order. This proved that SVM recognized emotions with high accuracy. This approach did vary vastly from other approaches such as using musical properties such as tempo and melody as a metric to judge emotional response.
It is used to pretreat EEG signals for recognizing emotions. Emotions and their states are divided broadly as being either optimistic or pessimistic. This study [6] is able to scientifically explain emotion driven events such as rash driving and creativity. “DEAP” datasets were used to divide the EEG signals into sub-band analysis using “Fisher’s Linear Discriminant” and then used “Naive Bayes Classifier” to classify emotions as being optimistic or pessimistic. 40 different locations of the brain were tracked for recording the EEG signals.
The result of X, are the size of filters. Defining hk as the kth convolution of any depth, then sampled feature is: hk = f (Wk * X + bk), where,
W = weight of filter, b = bias of filter, * = convolution,
f (.) = non linear activation function.
When CNN is trained, cross–entropy function is usually used as the cost function.
Cost = 1n x[y Ln y + (1 − y)Ln (1 − y)], where, n = no. of training samples, x = input samples, y = actual output, y = target output. It defines the smaller the cost function, the closer the classification results is to target output. The convolution layer input samples are {X, Y} = {{X1, Y1}, {X2, Y2},….,{Xi, Yi}}, i = {1, 2,….,n}.
X = feature of ith sample, Y = label of ith sample. X = {A * B * C}, a = channel of EEG signals. b = Down sampled EEG signals, f = sampling frequency. C = duration of EEG signals, t = time of video. C is the depth of 2 dimensional feature vector.
Labels are:Yi = {0, 0 < labels i < 4.5, 1, 4.5 < labels i < 9}Yi = {0, 0 < labels i < 3, 1, 3 < labels i < 6, 2, 6 < labels i < 9}
In 2 category recognition algorithm, 0 = optimism & 1 = pessimism. In 3 category recognition algorithm 0 = optimism, 1 = calm & 2 = pessimistic.tan (hk) = ehk − e − hkehk + e − hk
The full connection layers use following as an activation function: Softplus(y) = Log (1 + ey)
When trained, a stochastic gradient descent is used as an optimization algorithm to update the weights:
y() = output of CNN, J() is loss value which is mean of multiple cost function values. The program is written in python and implemented using keras library toolkit and theano.
Regarding Neuromarketing techniques, we read up n the recent research that linked EEG signals with predicting consumer behavior and emotions on self-reported ratings.
The correlation between neurons’ activities and the decision-making process is studied [7] during shopping have extensively been exploited to ascertain the bond between brain mapping and decision-making while visualizing a supermarket. The participants were asked to select one of every 3 brands after an interval of 90 stops. They discovered improvement in choice-predictions brand-wise. They also established significant correla-tions between right-parietal cortex activation with the participant’s previous experience with the brand.
The researchers [8] explored the Neuro-signals of 18 participants while evaluating products for like/dislike. It also incorporated eye-tracking methodology for recording participant’s choice from set of 3 images and capturing Neuro-signals at the same time. They implemented PCA and FFT for preprocessing the EEG data. After processing mutual data amongst preference and various EEG bands, they noticed major activity in “theta bands” in the frontal, occipital and parietal lobes.
The authors [9] tried to analyze and predict the 10 participant’s preference regarding consumer products in a visualization scenario. In the next procedure, the products were grouped into pair and presented to participants which recorded increase frequencies on mid frontal lobe and also studied the theta band EEG signals correlating to the products.
It has implemented an application-oriented solution [10] for footwear retailing industry which put forward the pre-market prediction system using EEG data to forecast demand. They recorded 40 consumers in store while viewing the products, evaluating them and asked to label it as bought/not with an additional rating-based questionnaire. They concluded that 80–60% of accuracy was achieved while classifying products into the 2 categories.
A suggestive system [11] was created based on EEG signal analysis platform which coupled pre and post buying ratings in a virtual 3D product display. Here, they factored the emotions of the subjects by analyzing beta and alpha EEG signals.
The authors created a preference prediction system [12] for automobile brands in a portable form while conducting trial on 12 participants as they watched the promotional ad. The Laplacian filter and Butterworth band pass was implemented for preprocessing and 3 tactical features—“Power-Spectral Density”, “Spectral Energy” and “Spectral Centroid” was procured from alpha band. The prediction was done by “K-Nearest Neighbor” and “Probabilistic Neural Network” classification with 96% accuracy.
They used the scenario of predicting the consumer’s choice based on EEG signal analysis [13] while viewing the trailers which resulted in finding significant gamma and beta high frequencies with high correlation to participants and average preferences.
Participants were assessed on self-arousal and valence features while watching particular scenes in a movie [14]. They analyzed the data while factoring in 5 peripheral physiological signals relating them to movie’s content-based features which inferred that they can be used to categorize and rank the videos.
Here 19 participants were shown 2 colors for an interval of 1 s and during the time EEG oscillations were analyzed [15] on Neural mechanisms for correlations of color preferences.
They had 18 participants who were subjected to a set of choices and analyzed their Neuro-activity and Eye-tracking activity to brain-map regions associated with decision making and inter-dependence of regions for the said task [16]. They concluded with high synchronization amongst frontal lobe and occipital lobe giving major frequencies in theta, alpha and beta waves.
They are trying to establish a bond between Neuro-signals and the learning capacity of a model software [17] while assuming that the model has the capability to train itself for dominant alpha wave participants.
“Independent Component Analysis (ICA)” to separate multivariate signals coming from 120 channels of electro-cortical activity [18]. This was done to convert those signals into additive subcomponents. Patterns of sensory impulses were recorded which matched movement of the body.
They have used filter is as a stabilizing and filtering
