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Data Fusion and Analysis Techniques of Neuromarketing

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Data Fusion and Analysis Techniques of Neuromarketing Data fusion and analysis techniques of neuromarketing B. Li1,Y.Wang2 &K.Wang1,3 1Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, China 2School of Materials, University of Manchester, UK 3Knowledge Discovery Laboratory, Department of Production and Quality Engineering, Norwegian University of Science and Technology, Norway Abstract The electroencephalogram (EEG) is one of the main technical means of neuroscience research. It can read the level of physiologically activity in differ- ent areas of the brain by measuring the change in electric charge on the scalp. Eye tracking instrument is one of the main equipment of cognitive psychology re- search; it can explore people’scognitive process. Fusing EEG and eye tracking data together integrates the consumer’s affective (emotional) and cognitive responses, giving a comprehensive understanding of the consumer’s decision-making process. At present the international research achievements of this aspect is still less. In this paper, we combined with the international latest paper published in this aspect, described the data collection, data analysis and processing, and data integration framework, expected to provide some grounding for subsequent research. Keywords: neuromarketing, EEG, eye tracking, data fusion. 1 Introduction Neuromarketing applies the neuroimaging technology to study consumer’s be- havior, explore the mechanism of consumer decision-making on neural activity level, and find the real driving force behind the consumer’s behavior [1]. From the point of the research field, neuromarketing mainly includes the neuron research of consumer behavior, marketing strategies, advertising and customer relationship management, etc. [2]. Neuromarketing is an emerging discipline that developed based on the cognitive science and neuroscience [3]. WIT Transactions on Engineering Sciences, Vol 113, © 2016 WIT Press www.witpress.com, ISSN 1743-3533 (on-line) doi:10.2495/IWAMA150461 Advanced Manufacturing and Automation V 397 The development of neuroscience allow researchers to observe dynamically hu- man brain activity from different aspects and angles, uncover the direct reasons behind human behavior. Neuroscience studies of human brain structure and func- tion of each structure is the physiological basis of neuromarketing research [4]. Generally the electroencephalogram (EEG) devices have the characteristics of cheap, low requirement to the experimental environment and easy to popularize, so it has become one of the main technical means of neuroscience research [5]. Eye movement instrument, as an important equipment of vision research, plays an important role in cognitive science and psychology research [6]. The combination of EEG signals with eye tracking signals to study the consumer’s decision-making process has become one of the hot issues in present neural marketing research. 2 Data collection 2.1 EEG signal collection The EEG signal is the weak electrical potential difference that measured on the scalp [7] when a large number of neurons (Mainly the cone cells) discharge at the same time in the brain neural system. The patterns of cortical activity were obtained in the five principal frequency bands, Delta (0–4 Hz), Theta (3–7 Hz), Alpha (8–12 Hz), Beta (13–30 Hz), and Gamma (30–40 Hz) [8, 9]. The EEG sig- nals are very weak, usually determined by placing electrodes on the scalp to detect the changes of brain waves. According to the type of the electrode, and the signal acquisition method is divided into wet and dry electrode [10]. The wet electrode is one of the traditional acquisition methods, Participants must daub conductive paste when using. Dry electrode is a kind of emerging collection technology that avoid the hassles of conductive paste daub. It can reduce the subjects’ discomfort for a long time involved in the experiment and is more conducive to the practical application of EEG equipment in the future. Figure 1: EEG data collection processing. Khushaba et al. [11] chose the Emotiv EPOC to collect the EEG signals when re- searched people’s decision-making process of select preferred crackers according to their own preferences from the crackers described by different characteristics. As shown in Fig. 1, the Emotiv EPOC is a high-resolution, neuro-signal acquisition and processing wireless headset that monitors 14 channels of EEG data and has a gyroscope measure for two-dimensional control. Neuromarketing can adopt the WIT Transactions on Engineering Sciences, Vol 113, © 2016 WIT Press www.witpress.com, ISSN 1743-3533 (on-line) 398 Advanced Manufacturing and Automation V Emotiv EPOC to collect the EEG signals on the subjects’ scalp, and then Emotiv EPOC sends the data to a computer via Bluetooth. The Emotiv Software Develop- ment Kit (SDK) provides a packet count functionality to ensure no data is lost, a writable marker trace to ease single trial segmentation tasks, and real-time sensor contact display to ensure quality of measurements [11]. 2.2 Eye tracking data collection The data collection process using eye tracking equipment mainly includes first determining the fixation point, then distribution of the fixation point, fixation time, and fixation order [12]. The pupil diameter and blink rate can be used as indicators of cognitive processing. The data can be used to assess human psychology and behavior, which could be further utilized to get more objective results [13]. At present large manufacturers of eye tracking instrument in the world include Tobii, SMI (Sensomotic Instrument), SR Research, ASL (Applied Science Labor- atory), etc. Most product types include head-mounted (head movements freely), fixed head, and remote sensing. Sampling rate is up to 1,250 Hz (Type: iView X; Hi-Speed). System accuracy degree can reach 0.5◦ and resolution can reach 0.2◦ in a certain scope angle (Type: Tobii TX300). The scale of tens of milliseconds system error exists for all kinds of products [14]. Eye tracking instrument has a very wide range of applications, mainly used for the psychological mechanism of visual information processing, such as reading, visual search, advertising, psycho- logy, etc. In addition, the eye tracking instrument can also be used in developmental psychology, ergonomics, traffic psychology, and sports psychology research [15]. Wei-Long Zheng et al. adopted SMI eye tracking glasses to record pupil size changes under different emotional stimuli in the aspects of research human emotional cognitive [16]. 3 Data analysis 3.1 The analysis of EEG signals 3.1.1 Cleaning and denoising EEG signals EEG signals are measured by electrodes on the scalp surface, because the device circuit interference or other physiological signals change will affect the voltage field of the electrode nearby. It is inevitably mixed with other signals in the process of measurement. As a result, the original EEG signals have low signal-to-noise ratio (SNR) and contain a lot of noise and artifact. Detecting and removing arti- facts in the EEG data due to muscle activity, eye blinks, electrical noise, etc. is an important problem in EEG signal processing research [17]. The existing pretreatment method of denoising and removing artifact from EEG signals mainly includes the following four: (1) Directly delete the EEG signals fragments that contain artifact. First observe the presence of artifact in the EEG signals when specific processing, then use manual way to delete it [18]. Or set a threshold for WIT Transactions on Engineering Sciences, Vol 113, © 2016 WIT Press www.witpress.com, ISSN 1743-3533 (on-line) Advanced Manufacturing and Automation V 399 the power or amplitude of EEG signals. Signal more than threshold would be considered as artifact signal and be deleted automatically by software program [19]. (2) Filter the artifact in EEG signals by using the linear filter. For example, use of a high-pass filter can filter out some eye elec- tric artifact and the low-pass filter can be used to filter out some muscle artifacts [20]. (3) Adopt the method of linear regression to filter out artifact from the EEG signals. For example, under the two kinds of waveform that electrical interference of eye presence and electrical interference of eye no presence, based on the same kind of event-related potential. Combination of eye signals recorded at the time and adopted the least squares method to calculate how much eye electrical signals being contained in the event-related potential of objects under interference. Record eye electrical signals at the same time in online processing, then minus the corresponding proportion eye electrical signals from EEG signals for correction of EEG signals [21]. (4) By using the method of signal decomposition. Using the method of signal decomposition such as singular value de- composition or independent component analysis (ICA), the original EEG signals are decomposed into multiple components, the artifact composition are identified [22], the non-artifact components to reconstruct EEG signals are reused. 3.1.2 The spectrum analysis method of EEG signals The power spectrum analysis methods is the earliest and the most commonly used in EEG spectrum analysis methods, originating from Fourier transform. In the traditional EEG power spectrum analysis method, first a correlation function is obtained, then according to Wiener–Khinchin theorem spectrum estimation [23] is obtained. But the premise of the power spectrum analysis methods is for sta- tionary random signals. There is a big difference in the spectral analysis results of different time for
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