Evaluation of TV commercials using neurophysiological responses

Background

In recent years, neuroscientific knowledge has been applied to marketing as a novel and efficient means to comprehend the cognitive and behavioral aspects of consumers. A number of studies have attempted to evaluate media contents, especially TV commercials using various neuroimaging techniques such as electroencephalography (EEG). Yet neurophysiological examination of detailed cognitive and affective responses in viewers is still required to provide practical information to marketers. Here, this study develops a method to analyze temporal patterns of EEG data and extract affective and cognitive indices such as happiness, surprise, and attention for TV commercial evaluation.

Methods

Twenty participants participated in the study. We developed the neurophysiological indices for TV commercial evaluation using classification model. Specifically, these model-based indices were customized using individual EEG features. We used a video game for developing the index of attention and four video clips for developing indices of happiness and surprise. Statistical processes including one-way analyses of variance (ANOVA) and the cross validation scheme were used to select EEG features for each index. The EEG features were composed of the combinations of spectral power at selected channels from the cross validation for each individual. The Fisher’s linear discriminant classifier (FLDA) was used to estimate each neurophysiological index during viewing four different TV commercials. Post hoc behavioral responses of preference, short-term memory, and recall were measured.

Results

Behavioral results showed significant differences for all preference, short-term memory rates, and recall rates between commercials, leading to a ‘high-ranked’ commercial group and a ‘low-ranked’ group (P < 0.05). Neural estimation of happiness results revealed a significant difference between the high-ranked and the low-ranked commercials in happiness index (P < 0.01). The order of rankings based on happiness and attention matched well with the order of behavioral response rankings. In the elapsed-time analysis of the highest-ranked commercial, we could point to visual and auditory semantic structures of the commercial that induced increases in the happiness index.

Conclusions

Our results demonstrated that the neurophysiological indices developed in this study may provide a useful tool for evaluating TV commercials.     

Brain activity during the memorization of visual scenes from TV commercials: An application of high resolution EEG and steady state somatosensory evoked potentials technologies

The aim of this study was to elucidate if the TV commercials that were remembered by the subjects after their observation within a documentary elicited particular brain activity when compared to the activity generated during the observation of TV commercials that were forgotten. High resolution EEG recordings were performed in a group of 10 healthy subjects with the steady state somatosensory evoked potentials (SSSEPs) technique, in which a series of light electrical stimulation at the left wrist were delivered at the frequency of 20 Hz. The brain activity was indexed by the phase delay of the EEG spectral responses at 20 Hz with respect to the stimulus delivering and evaluated at the scalp level as well as at the cortical surface using several regions of interest coincident with the Brodmann areas (BAs). Results suggest that the cerebral processes involved during the observation of TV commercials that were remembered by the population examined (RMB dataset) are generated by the posterior parietal cortices and the prefrontal areas, rather bilaterally. These results are compatible with previously results obtained in literature by using MEG and fMRI devices during similar experimental tasks. High resolution EEG is able to summarize, with the use of SSSEPs methodologies, the behavior of the estimated cortical networks subserving the proposed memory tasks. It is likely that such tool could play a role in the next future for the investigation of the neural substrates of the human behavior in decision-making and recognition tasks.     

Recording brain waves at the supermarket: what can we learn from a shopper's brain?

Communication and marketing campaigns have traditionally been divided into two lines: above the line (ATL) and below the line (BTL). ATL campaigns refer to communications such as TV, print, and outdoor displays that are intended to reach large audiences. The effects of ATL are inherently difficult to measure; we do not see the direct consequences of viewing an advertisement (i.e., a talking baby giving financial advice) and actual purchase of the product. ATL is intended to indirectly improve the impression of a brand. BTL campaigns refer to promotions and in-store displays and are designed to affect the point-of-purchase behavior. The effects of BTL are easier to measure; we see direct consequences of viewing a display (i.e., “Today Only, Two for the Price of One”) and eventual purchase of the product. BTL is intended to directly improve the impression of a brand. Neuroscience plays an important role in measuring the effects of marketing campaigns. Traditional methods of measurement (such as surveys and interviews) depend on the verbal ability of the consumer to articulate their motivations for purchasing a product. It is well known that participants are poor at introspective reasoning, leading to an eventual purchase that omits emotional elements. Recently, methods normally employed in cognitive neuroscience have been adapted for use in the evaluation of campaign effectiveness. These methods have increased our understanding of factors leading to economic decisions. The application of neuroscience in ATL campaigns is relatively straightforward. Participants view TV commercials, for example, seated in a comfortable setting with minimal movement while electroencephalogram (EEG) measures are monitored. These brain waves reveal cognitive events related to the media. Participants are exposed to a functional magnetic resonance imaging (fMRI) scanner to monitor changes in blood flow in various regions of the brain. Both of these methods are sensitive to underlying cognitive and emotional activity and are complimentary. EEG is more sensitive to time-locked events (i.e., story lines), whereas fMRI is more sensitive to the brain regions involved. The application of neuroscience in BTL campaigns is significantly more difficult to achieve. Participants move unconstrained in a shopping environment while EEG and eye movements are monitored. In this scenario, fMRI is not possible. fMRI can be used with virtual store mock-ups, but it is expensive and seldom used. We have developed a technology that allows for the measurement of EEG in an unobtrusive manner. The intent is to record the brain waves of participants during their day-to-day shopping experience. A miniaturized video recorder, EEG amplifiers, and eye-tracking systems are used. Digital signal processing is employed to remove the substantial artifact generated by eye movements and motion. Eye fixations identify specific viewings of products and displays, and they are used for synchronizing the behavior with EEG response. The location of EEG sources is determined by the use of a source reconstruction software.     

Evaluation of quality in adult cardiac surgery: let us speak the same language

Over the last years, measurements of quality of care have become more and more a public product, used by providers, purchasers and consumers, and patients. This information serves as an important guide for improvement, as well as a decision support tool for everybody taking part in medical treatment. This evolution can be compared with advertising and as in commercials it is important to use the right information. In this report we focus on the quality of adult cardiac surgery. Honest information is of course essential, but in this article attention is asked for the variables used to evaluate the quality of cardiac surgery. (Neth Heart J 2010;18:365-9.)     

Patterns of cortical activity during the observation of Public Service Announcements and commercial advertisings

Background

In the present research we were interested to study the cerebral activity of a group of healthy subjects during the observation a documentary intermingled by a series of TV advertisements. In particular, we desired to examine whether Public Service Announcements (PSAs) are able to elicit a different pattern of activity, when compared with a different class of commercials, and correlate it with the memorization of the showed stimuli, as resulted from a following subject’s verbal interview.

Methods

We recorded the EEG signals from a group of 15 healthy subjects and applied the High Resolution EEG techniques in order to estimate and map their Power Spectral Density (PSD) on a realistic cortical model. The single subjects’ activities have been z-score transformed and then grouped to define four different datasets, related to subjects who remembered and forgotten the PSAs and to subjects who remembered and forgotten cars commercials (CAR) respectively, which we contrasted to investigate cortical areas involved in this encoding process.

Results

The results we here present show that the cortical activity elicited during the observation of the TV commercials that were remembered (RMB) is higher and localized in the left frontal brain areas when compared to the activity elicited during the vision of the TV commercials that were forgotten (FRG) in theta and gamma bands for both categories of advertisements (PSAs and CAR). Moreover, the cortical maps associated with the PSAs also show an increase of activity in the alpha and beta band.

Conclusions

In conclusion, the TV advertisements that will be remembered by the experimental population have increased their cerebral activity, mainly in the left hemisphere. These results seem to be congruent with and well inserted in the already existing literature, on this topic, related to the HERA model. The different pattern of activity in different frequency bands elicited by the observation of PSAs may be justified by the existence of additional cortical networks processing these kind of audiovisual stimuli. Further research with an extended set of subjects will be necessary to further validate the observations reported in this paper.

     

Application of a dissimilarity index of EEG and its sub-bands on prediction of induced epileptic seizures from rat's EEG signals

ObjectiveEpileptic seizures are defined as manifest of excessive and hyper-synchronous activity of neurons in the cerebral cortex that cause frequent malfunction of the human central nervous system. Therefore, finding precursors and predictors of epileptic seizure is of utmost clinical relevance to reduce the epileptic seizure induced nervous system malfunction consequences. Researchers for this purpose may even guide us to a deep understanding of the seizure generating mechanisms. The goal of this paper is to predict epileptic seizures in epileptic rats.MethodsSeizures were induced in rats using pentylenetetrazole (PTZ) model. EEG signals in interictal, preictal, ictal and postictal periods were then recorded and analyzed to predict epileptic seizures. Epileptic seizures were predicted by calculating an index in consecutive windows of EEG signal and comparing the index with a threshold. In this work, a newly proposed dissimilarity index called Bhattacharyya Based Dissimilarity Index (BBDI), dynamical similarity index and fuzzy similarity index were investigated.ResultsBBDI, dynamical similarity index and fuzzy similarity index were examined on case and control groups and compared to each other. The results show that BBDI outperforms dynamical and fuzzy similarity indices. In order to improve the results, EEG sub-bands were also analyzed. The best result achieved when the proposed dissimilarity index was applied on Delta sub-band that predicts epileptic seizures in all rats with a mean of 299.5 s.ConclusionThe dissimilarity of neural network activity between reference window and present window of EEG signal has a significant increase prior to an epileptic seizure and the proposed dissimilarity index (BBDI) can reveal this variation to predict epileptic seizures. In addition, analyzing EEG sub-bands results in more accurate information about constituent neuronal activities underlying the EEG since certain changes in EEG signal may be amplified when each sub-band is analyzed separately.SignificanceThis paper presents application of a dissimilarity index (BBDI) on EEG signals and its sub-bands to predict PTZ-induced epileptic seizures in rats. Based on the results of this work, BBDI will predict epileptic seizures more accurately and more reliably compared to current indices that increases epileptic patient comfort and improves patient outcomes.     

视频脑电图鉴别诊断癫痫患者睡眠障碍、认知障碍的临床价值研究

目的:探讨视频脑电图诊断癫痫患者睡眠障碍、认知障碍的临床价值。方法:选取2014年1月~2016年12月在我院神经内科进行诊治的癫痫患者236例作为癫痫组,另选取同期的健康患者家属或者其他健康体检者236例作为正常对照组,对两组进行视频脑电图联合睡眠参数分析;并对癫痫组视频脑电图联合认知参数进行分析。结果:癫痫组睡眠Ⅰ~Ⅱ期时间显著长于正常对照组且具有统计学差异(P=0.000),睡眠Ⅲ~Ⅳ期时间显著短于正常对照组且具有统计学差异(P=0.000),睡眠时相转换频率、觉醒指数均显著高于正常对照组且均具有统计学差异(P=0.000);清醒期、睡眠期不同痫样放电指数(IED)的WAIS-RC IQ和WMS-RC MQ均具有统计学差异(P0.05),10%IED≤50%者的WAIS-RC IQ和WMS-RC MQ均显著低于1%IED≤10%者且均具有统计学差异(P0.05),IED 10%可能是痫样放电影响患者认知功能的最低阈值。结论:视频脑电图在癫痫患者睡眠障碍、认知障碍识别中具有重要的临床价值。     

EEG Mu Rhythm in Typical and Atypical Development

Electroencephalography (EEG) is an effective, efficient, and noninvasive method of assessing and recording brain activity. Given the excellent temporal resolution, EEG can be used to examine the neural response related to specific behaviors, states, or external stimuli. An example of this utility is the assessment of the mirror neuron system (MNS) in humans through the examination of the EEG mu rhythm. The EEG mu rhythm, oscillatory activity in the 8-12 Hz frequency range recorded from centrally located electrodes, is suppressed when an individual executes, or simply observes, goal directed actions. As such, it has been proposed to reflect activity of the MNS. It has been theorized that dysfunction in the mirror neuron system (MNS) plays a contributing role in the social deficits of autism spectrum disorder (ASD). The MNS can then be noninvasively examined in clinical populations by using EEG mu rhythm attenuation as an index for its activity. The described protocol provides an avenue to examine social cognitive functions theoretically linked to the MNS in individuals with typical and atypical development, such as ASD.      

A novel index of functional connectivity: phase lag based on Wilcoxon signed rank test

Phase synchronization has been an effective measurement of functional connectivity, detecting similar dynamics over time among distinct brain regions. However, traditional phase synchronization-based functional connectivity indices have been proved to have some drawbacks. For example, the phase locking value (PLV) index is sensitive to volume conduction, while the phase lag index (PLI) and the weighted phase lag index (wPLI) are easily affected by noise perturbations. In addition, thresholds need to be applied to these indices to obtain the binary adjacency matrix that determines the connections. However, the selection of the thresholds is generally arbitrary. To address these issues, in this paper we propose a novel index of functional connectivity, named the phase lag based on the Wilcoxon signed-rank test (PLWT). Specifically, it characterizes the functional connectivity based on the phase lag with a weighting procedure to reduce the influence of volume conduction and noise. Besides, it automatically identifies the important connections without relying on thresholds, by taking advantage of the framework of the Wilcoxon signed-rank test. The performance of the proposed PLWT index is evaluated on simulated electroencephalograph (EEG) datasets, as well as on two resting-state EEG datasets. The experimental results on the simulated EEG data show that the PLWT index is robust to volume conduction and noise. Furthermore, the brain functional networks derived by PLWT on the real EEG data exhibit a reasonable scale-free characteristic and high test–retest (TRT) reliability of graph measures. We believe that the proposed PLWT index provides a useful and reliable tool to identify the underlying neural interactions, while effectively diminishing the influence of volume conduction and noise.     

In-stream behaviour of threatened fishes and their food organisms based on remote video monitoring

Can remote underwater video be used to investigate the in-stream behaviour of small fishes and decapods? Diel activity of two threatened freshwater fishes (Macquaria australasica and Gadopsis bispinosus), a palaemonid prawn and an atyid shrimp, was established from remote underwater video in a pool of an upland stream in the current study. Decapods and large fishes (>5 cm TL) were nocturnal, whereas, small fishes (<5 cm TL) were diurnal. The suitability of using underwater video to quantify short-term (seconds) behavioural interaction among individuals was also demonstrated, with early-juvenile-phase Gadopsis bispinosus exhibiting interference competition on 35% of occasions when two or more individuals were observed. This study demonstrates that remote underwater video is useful for observing the in-stream behaviour of threatened freshwater fauna where other techniques are not viable, and presents sub-sampling of video as a means of reducing video processing time in assessing fish diel activity patterns.     

Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables

Monitoring depth of anesthesia (DOA) via vital signs is a major ongoing challenge for anesthetists. A number of electroencephalogram (EEG)-based monitors such as the Bispectral (BIS) index have been proposed. However, anesthesia is related to central and autonomic nervous system functions whereas the EEG signal originates only from the central nervous system. This paper proposes an automated DOA detection system which consists of three steps. Initially, we introduce multiscale modified permutation entropy index which is robust in the characterization of the burst suppression pattern and combine multiscale information. This index quantifies the amount of complexity in EEG data and is computationally efficient, conceptually simple and artifact resistant. Then, autonomic nervous system activity is quantified with heart rate and mean arterial pressure which are easily acquired using routine monitoring machine. Finally, the extracted features are used as input to a linear discriminate analyzer (LDA). The method is validated with data obtained from 25 patients during the cardiac surgery requiring cardiopulmonary bypass. The experimental results indicate that an overall accuracy of 89.4 % can be obtained using combination of EEG measure and hemodynamic variables, together with LDA to classify the vital sign into awake, light, surgical and deep anesthetised states. The results demonstrate that the proposed method can estimate DOA more effectively than the commercial BIS index with a stronger artifact-resistance.     

High-density EEG Recordings of the Freely Moving Mice using Polyimide-based Microelectrode

Electroencephalogram (EEG) indicates the averaged electrical activity of the neuronal populations on a large-scale level. It is widely utilized as a noninvasive brain monitoring tool in cognitive neuroscience as well as a diagnostic tool for epilepsy and sleep disorders in neurology. However, the underlying mechanism of EEG rhythm generation is still under the veil. Recently introduced polyimide-based microelectrode (PBM-array) for high resolution mouse EEG¹ is one of the trials to answer the neurophysiological questions on EEG signals based on a rich genetic resource that the mouse model contains for the analysis of complex EEG generation process. This application of nanofabricated PBM-array to mouse skull is an efficient tool for collecting large-scale brain activity of transgenic mice and accommodates to identify the neural correlates to certain EEG rhythms in conjunction with behavior. However its ultra-thin thickness and bifurcated structure cause a trouble in handling and implantation of PBM-array. In the presented video, the preparation and surgery steps for the implantation of PBM-array on a mouse skull are described step by step. Handling and surgery tips to help researchers succeed in implantation are also provided.     

Alpha-Theta Effects Associated with Ageing during the Stroop Test

The Stroop effect is considered as a standard attentional measure to study conflict resolution in humans. The response of the brain to conflict is supposed to change over time and it is impaired in certain pathological conditions. Neuropsychological Stroop test measures have been complemented with electroencephalography (EEG) techniques to evaluate the mechanisms in the brain that underlie conflict resolution from the age of 20 to 70. To study the changes in EEG activity during life, we recruited a large sample of healthy subjects of different ages that included 90 healthy individuals, divided by age into decade intervals, which performed the Stroop test while recording a 14 channel EEG. The results highlighted an interaction between age and stimulus that was focused on the prefrontal (Alpha and Theta band) and Occipital (Alpha band) areas. We concluded that behavioural Stroop interference is directly influenced by opposing Alpha and Theta activity and evolves across the decades of life.     

Video Sequence-Based Iris Recognition Inspired by Human Cognition Manner

In video sequence-based iris recognition system, the problem of making full use of relationship and correlation among frames still remains to be solved. A brand new template level multimodal fusion algorithm inspired by human cognition manner is proposed. In that a non-isolated geometrical manifold, named Hyper Sausage Chain due to its sausage shape, is trained using the frames from a pattern class for representing an iris class in feature space. We can classify any input iris by observing which manifold it locates in. This process is closer to the function of human being, which takes ＇matter cognition＇ instead of ＇matter classification＇ as its basic principle. The experiments on self-developed JLUBR-IRIS dataset with several video sequences per person demonstrate the effectiveness and usability of the proposed algorithm for video sequence-based iris recognition. Fur- thermore, the comparative experiments on public CASIA-I and CASIA-V4-Interval datasets show that our method can also achieve improved performance of image-based iris recognition system, provided enough samples are involved in training stage.     

Preterm EEG: A Multimodal Neurophysiological Protocol

Since its introduction in early 1950s, electroencephalography (EEG) has been widely used in the neonatal intensive care units (NICU) for assessment and monitoring of brain function in preterm and term babies. Most common indications are the diagnosis of epileptic seizures, assessment of brain maturity, and recovery from hypoxic-ischemic events. EEG recording techniques and the understanding of neonatal EEG signals have dramatically improved, but these advances have been slow to penetrate through the clinical traditions. The aim of this presentation is to bring theory and practice of advanced EEG recording available for neonatal units. In the theoretical part, we will present animations to illustrate how a preterm brain gives rise to spontaneous and evoked EEG activities, both of which are unique to this developmental phase, as well as crucial for a proper brain maturation. Recent animal work has shown that the structural brain development is clearly reflected in early EEG activity. Most important structures in this regard are the growing long range connections and the transient cortical structure, subplate. Sensory stimuli in a preterm baby will generate responses that are seen at a single trial level, and they have underpinnings in the subplate-cortex interaction. This brings neonatal EEG readily into a multimodal study, where EEG is not only recording cortical function, but it also tests subplate function via different sensory modalities. Finally, introduction of clinically suitable dense array EEG caps, as well as amplifiers capable of recording low frequencies, have disclosed multitude of brain activities that have as yet been overlooked.In the practical part of this video, we show how a multimodal, dense array EEG study is performed in neonatal intensive care unit from a preterm baby in the incubator. The video demonstrates preparation of the baby and incubator, application of the EEG cap, and performance of the sensory stimulations.     

食物奖赏和渴求行为相关的大鼠左侧眶额叶皮质Delta频段脑电活动(英文）

眶额叶皮质与中脑边缘多巴胺奖赏系统有着复杂的相互纤维联系。先前的研究探讨了药物成瘾过程中眶额叶皮质的脑电活动。在本实验中,将探讨食物奖赏和渴求过程中该皮质的脑电活动。实验采用了两个环境:对照环境和食物刺激相关的环境。首先,训练大鼠在食物刺激相关的环境中吃巧克力花生豆,而后在该环境中设置两种不同的刺激方式:能看到和闻到但不能吃到(渴求实验),或者仍旧可以吃到巧克力花生豆(奖赏实验);同时进行左侧眶额叶皮质的脑电记录。结果发现,在食物刺激相关的环境中大鼠 Delta 频段(2-4 Hz)的脑电活动与食物刺激显著相关,此外,与在对照环境中相比,其相对功率在食物渴求时下降而在食物奖赏时升高。本实验表明,食物相关的奖励可以改变大鼠眶额叶皮质的脑电活动,而且,Delta 频段的脑电活动能够作为监测该奖励的一个指标。     

Functional variability of the autocorrelation structure of the EEG

On an example of records EEG of 39 healthy subjects, the quantitative analysis of variability of the autocorrelation structure of one-second EEG segments was carried out on the basis of comparison of structural functions constructed for these segments. It was shown that more than 30% of cases, statistically significant sifferences were observed between the structural functions of successive one-second EEG segments shifted by 1-3 s, as compared to surrogate EEGs formed with the tangled random sequence of count points. On the basis of the obtained data, the index of nonstationarity of the EEG autocorrelation structure was proposed. This index can be used for the objective quantitative evaluation of the functional states of the human brain.     

Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer’s disease

The complexity change of brain activity in Alzheimer’s disease (AD) is an interesting topic for clinical purpose. To investigate the dynamical complexity of brain activity in AD, a multivariate multi-scale weighted permutation entropy (MMSWPE) method is proposed to measure the complexity of electroencephalograph (EEG) obtained in AD patients. MMSWPE combines the weighted permutation entropy and the multivariate multi-scale method. It is able to quantify not only the characteristics of different brain regions and multiple time scales but also the amplitude information contained in the multichannel EEG signals simultaneously. The effectiveness of the proposed method is verified by both the simulated chaotic signals and EEG recordings of AD patients. The simulation results from the Lorenz system indicate that MMSWPE has the ability to distinguish the multivariate signals with different complexity. In addition, the EEG analysis results show that in contrast with the normal group, the significantly decreased complexity of AD patients is distributed in the temporal and occipitoparietal regions for the theta and the alpha bands, and also distributed from the right frontal to the left occipitoparietal region for the theta, the alpha and the beta bands at each time scale, which may be attributed to the brain dysfunction. Therefore, it suggests that the MMSWPE method may be a promising method to reveal dynamic changes in AD.