Machine Learning for Healthcare Applications. Группа авторов
was given a stimulus of 5 s since the duration of each emotion is about 0.5 to 4 s.
To do this the data is derived from 4 frequency bands—alpha, beta, theta, delta. ECG (heart) artefacts which are about 1.2 Hz, “EOG” artefacts (Blinking) is below 4 Hz and EMG (Muscle) artefacts about 30 Hz and Non-Physiological artefacts power lines which is above 50 Hz which removed in preprocessing. In DWT all frequency bands are used and for each trial, the feature vector is 18 ∗ 3 ∗ 9 ∗ 4 = 1,944 (18 electrodes, 3 statistical features, 9 temporal windows & 4 frequency bands). In our instance, an “artificial neural network” has been used as a form of classifier of “backpropagation” algorithm for learning models implemented on the network. The architecture consists of 6 outputs and 10 hidden layers for all the different states of emotion. The accuracies “10-fold cross-validation technique” was used to avoid overfitting while estimating accuracies for the classifiers. As user’s emotion can be affected by many factors such as their emotional state during the experiment, the best achieved accuracy for the network was 55.58%.
They applied Support Vector Machine to explore the bond between neural signals elated in prefrontal cortex based on taste in music. They [5] explored the effects of music on mental illnesses like dementia. It was observed that music enabled listeners to regulate negative behaviors and thoughts occurring in the mind. A BCI-based music system is able to analyze the real time activities of neurons and possible to provide physiological information to the therapist for understanding their emotions. The methods used to evaluate the data depended on the subjects.
The BCI music system consisted of EEG capturing system, a Bluetooth module for transmitting data from the EEG signals to analyze real time issue and also accordingly control the music. 3 major categories of music were considered to trigger positive emotions in the brain, namely the subject’s favorite songs, K448 and high focus audio. The high focus audio comprised of non-vocal instrumentals produced by “The Brain Sync Company”. It consisted of classic white noise elements such as those found in nature like falling rain or a cricket’s cry, etc. The company claimed that these audio clippings helps a person reach their peak performance brain state with relative ease and without getting distracted. 28 participants attended the experiment having a mean age of 21.75 years.
‘Fast fourier transform with 0.5 overlap 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