base from a consumer’s EEG data. Thus, major powerhouses in FMCG markets have started to adopt Neuromarketing techniques to exploit the consumer’s behaviour from a marketing and psychological angle.
3.1.6 About Machine Learning
“Machine Learning” is the process of enabling computing devices to try, learn, do and verify assigned tasks to be performed on their own without being hard-coded to do so. The learning process in the code needs to evolve on its own with the changing parameters and perform accordingly. Earlier on human used to analyze all the scenarios in a task and we would dictate the steps required to the computer but during wider and more complex situations we realized it’s better for the computers to develop its own algorithm.
This domain of Machine Learning implements various tracks to help computers automatedly accomplish tasks where no correct sequence of steps is known. It involves training the computer through a vast number of situations and label them as successful or not and accordingly perform the correct sequence of action for the respective situation. This is called training data which is used to improve the effectiveness and efficiency of an algorithm. We have extensively used this procedure to achieve significant results in regard to our efficiency in predicting the choice of a consumer and also establishing a brain map for like/dislike.
3.2 Literature Survey
In this section, we shall briefly mention most of the studies and research done currently in the field of EEG and Neuromarketing relevant to our study.
Primarily we got a solid foundation [1] to work on from Dr. Partha Pratim Roy’s and his associates’ paper on Analysis of EEG signals and application of Neuromarketing, in his paper he has used the deep learning method of Hidden Markov Model and recorded the dataset using user-independent test approach. He also proposed a predictive modelling framework to acquire the consumer’s knowledge about what all he like/dislikes amongst the sample products using an Emotiv EPOC+ sensor. We have borrowed his dataset for initial study as an ice-breaker and it has helped us in leaps.
After reading his paper, we inherently searched for the spectrum of mind which consciously makes the decision of a person liking/disliking a product in a natural environment. We had encountered lot of reasons such as presentation, composition of materials, past experiences, cost and brand value which a person uses to determine its likeability. But perhaps this wasn’t enough. So, we decided to delve a little into emotion recognition for identifying which all areas in brain elicit an emotion. Following will be our concise notes on emotion recognition and after which we shall provide the methodological research of models.
This paper [2] is about automatic emotional classification by EEG data using DEAP dataset led by Samarth Tripathi and his associates, applying Convolutional and Deep Neural Networks on DEAP datasets. Earlier emotion recognition involved text, speech, facial, etc. as analyzing parameter s. An emotion is a psychophysiological operation started by a voluntary or involuntary reception of a situation.
In this paper, peripheral physiological signals of 32 subjects were recorded while they watched videos and were evaluated on levels of arousals & valence. They used a 32 EEG-channel 512 Hz Biosemi Active2 device that utilizes active AgCl electrodes to compile the data.
Neural networks implement functions based on large datasets of unknown inputs by training & statistic models. Here, 2 neural models are used 1. Deep Neural Network (DNN) and 2. Convolutional Neural Network (CNN). The dataset is of 8064 signal data from 40 channels for each subject. A total of 322,560 readings were recorded for the models to process. The first model, i.e., DNN used 4 neural levels whose output became input for the subsequent levels. As the dataset was limited, they implemented dropout technique with superior Epoch which could keep the count of all training vectors for updating weights. The datasets were divided into groups for easier use and they all go through learning algorithms before Epoch update occurs. The data was trained in 310 groups with Epoch of 250. For the second model, i.e., CNN, the DEAP data is converted to 2D images for the 101 readings each totaling to a size of 4,040 units. CNN’s first layer used ‘Tan Hyperbolic’ as activation function in valence classification model & ‘Relu’ as activation for arousal model. The subsequent levels used 100 filters and 3 ∗ 3 sized kernel with the very same ‘Tan Hyperbolic’ function as activation function for both classifier models. The last dense layer used ‘Softplus’ as its activation function using CCE as loss function and SGD as an optimizer.
The learning rates were found to be 0.00001 for valence, 0.001 for arousal & a gradient momentum of 0.9. These models resulted in 4.51 & 4.96% improvement in classifying valence and arousal respectively among 2 classes (High/Low) in valence & 3 classes (High/Normal/Low) in arousal. The learning rate is marginally more useful, but dropout probability secures the best classification across levels. They also noted that wrong choice of activation functions especially 1st CNN layer will cause severe defects to models. The models were highly accurate with respect to previous researchers and prove the fact that neural networks are the key for EEG classification of emotions in a step to unlocking the brain.
Hence Deep Neural Networks are used to analyze human emotions and classify them by PSD and frontal asymmetry features. Training model for emotional dataset are created to identify its instances. Emotions are of 2 types—Discrete, classified as a synchronized response in neural anatomy, physiology & morphological expressions and Dimensional, i.e., they can be represented by a collection of small number of underlying effective dimensions, in other words, vectors in a multidimensional space.
The aim of this paper is to identify excitement, meditation, boredom and frustration from the DEAP emotion dataset by a classification algorithm. The Python language is used including libraries like SciKit Learn Toolbox, SciPy and Keras Library. The DEAP dataset contains physiological readings of 32 participants recorded at a sampling rate of 512 Hz with a “bandpass frequency filter” with a range of 4.0 to 45.0 Hz and eliminated EOG artifacts. Power Spectral Density (PSD), based on Fast Fourier Transform, decomposes the data into 4 distinct frequency ranges, i.e., theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–40 Hz) using the avgpower function available in Python’s Signal Processing toolbox. The left hemisphere of brain has more frequently activation with positive valence and the right hemisphere has negative valence.
Emotion estimation on EEG frontal asymmetry:
“Ramirez et al. classified emotional states by computing levels of arousal as prefrontal cortex and valence levels as below”:
“Whenever the arousal was computed as beta to alpha activity ratio in frontal cortex, valence was computed as relative frontal alpha activity in right lobe compared to left lobe as below”:
“A time-frequency transform was used to extract spectral features alpha (8–11 Hz) and beta (12–29 Hz). Lastly Mean absolute error (MAE), Mean squared error (MSE) and Pearson Correlation (Corr) is used.”
By scaling (1–9) into valence & arousal (High & Low) we see that feeling of frustration and excitement triggers as high arousal in a low valence area and high valence area respectively whereas meditation and boredom triggers as low arousal in high valence area and in low valence area respectively.
