Journal of Biosciences and Medicines, 2014, 2, 6-11
Published Online June 2014 in SciRes.
How to cite this paper: Wang, P.-S., et al. (2014) Attention Drawing of Movie Trailers Revealed by Electroencephography
Using Sample Entropy. Journal of Biosciences and Medicines, 2, 6-11.
Attention Drawing of Movie Trailers
Revealed by Electroencephography Using
Sample Entropy
Po-Shan Wang1, Shang-Ran Huang2, Chao-Wen Tsai2, Chia-Feng Lu2, Shin Teng2,
C.-I. Hung2, Yu-Te Wu2,3
1The Neurological Institute, Taipei Municipal Gan-Dau Hospital, New Taipei, Chinese Taipei
2Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taip ei,
Chinese Taipei
3Institute of Biophotonics, National Yang-Ming University, Taipei, Chinese Taipei
Received February 2014
A movie trailer is a common advertising tool in the entertainment industry. Detection of a viewer’s
brain responses to a movie trailer can help film producers to tailor a more appealing trailer of a
movie. In this study, we acquired electroencephalographic (EEG) signals from subjects when they
watched movie trailers (labeled as Movie session), and compared with their resting state session
(labeled as Resting session) or when they watch nature scenes (labeled as Nature session). We
used Sample Entropy (SampEn) to analyze the EEG signals between different sessions. Results
showed that the complexity ratios at Fp1, Fp2 and Fz channels derived from Movie session were
significantly lower than that in Resting state or when subjects watched Nature session (p < 0.001).
Our results suggest that the brain status can affect the complexity of their EEG. Further, the attrac-
tion of attention of a movie trailer can be observed f rom the change of EEG.
Neuromarketing, Electroencephalography, Sample Entr op y, Movie Tr ailer
1. Introduction
Watching movies is a part of life in modern society. The scale of this entertainment industry has been over 10
billion US dollars since 2009 in North American market alone [1]. However, not every movie can generate posi-
tive income for the film producers and publishers. A successful promotional campaign plays a key role in
boosting the box office and resulting in positive net gross of a movie. With easy accessibility to movie database
through internet, such as YouTube, Rotten Tomatoes and Internet Movie Database (IMDb), movie trailers have
become a powerful advertising tool to promote a movie. In 2008, movie trailers clinched the third place on the
list of the most watched online videos [2]. Therefore, how to produce a good trailer for each movie become
P.-S. Wang et al.
more and more important for each movie producers.
In neuromarketing [3], modern techniques, such as electroencephalography (EEG), magnetic resonance imaging
(MRI), near infrared spectroscopy (NIRS) and so on, are used to investigate a customer’s cerebral feedback to an
advertisement. An American multinational neuromarketing company, NeuroFocus, stresses on using three primary
neurometrics (attention, memory and emotion) and three secondly derived neurometrics (purchase intent, novelty
and awareness) to assess the effectiveness of the advertisement [4]. Several reports have been proposed to inves-
tigate a customer’s cerebral response to different advertising static pictures or television commercials [5]-[7].
In contrast to the static pictures and television commercials films, few studies had focused on movie trailers
before. It could be because movie trailers are more complicated to study when compared to the television com-
mercial films. A movie trailer tends to have longer duration, and its advertising effectiveness is influenced by
variety of factors like movie genres, cast, director, etc. In this study, we focused on whether a movie trailer can
draw viewers’ attention so that the brain status change can be detected by using modern EEG techniques.
2. Materials and Methods
2.1. Data Acquisition
Twenty-seven right-handed health volunteers were recruited in this study, including 14 males and 13 females.
Their average age was 24.9 ± 3.2 (range 21 - 31). All of them were Mandarin Chinese native speakers. All the
subjects had normal or corrected-to-normal vision. None of them had neurological or psychiatric illness or al-
cohol abuse, and all of them were medication-free. Before the experiment, the purpose of this study was ex-
plained to the subjects. Informed consent was obtained from each subject. This study was performed based on
the declaration of Helsinki and approved by Institutional Review Board of National Yang-Ming University,
Chinese Taipei.
A 40 channel QuickAmp system (Brain Products GmbH, Gilching, Germany) was used to record the EEG
signa ls, and the sampling rate was 500 Hz. Based on the International 10 - 20 system, six EEG electrodes were
used at the following recording sites, namely, Fp1, Fp2, Fz, O1, O2 and Oz, with the ground at AFz referred to
common reference in this study (Figure 1). In addition, two additional bipolar electrooculogram (EOG) was
used to detect eye movements; and the other two EEG electrodes were attached to the left and right ear lobe
separately from which average signals of both ear lobes were used as reference. The EEG electrode impedances
were kept below 5 kΩ in most cases. Experimental stimuli were disp la yed on the screen of a personal computer
(PC) with synchronization markers sent to the recording laptop. We used the software “Presentation” by Neuro-
behavioral Systems for displaying the stimuli. Subjects underwent the experiment in a light-controlled room and
sat in a comfortable office chair. Also, subjects were instructed to keep awake and relaxed for entire duration in
the experiment. The subjects sat about 100 cm away from the stimuli displaying PC. They were asked to sit
quietly and to look at the screen to minimize the muscle and eye movement artifacts.
Figure 1. EEG electrodes montage. Red circles indicate the 6 elec-
trodes, Fp1, Fp2, Fz, O1, O2, and Oz, used in the experiment.
P.-S. Wang et al.
2.2. Paradigm
In the experiment, each subject needed to watch 5 video clips, including 1 natural scenery video and 4 movie
trailers which were coming soon to theaters. The study totally consisted of 7 sessions. The procedure is illu-
strated in Figure 2. At the beginning, the subjects were asked to focus on watching a fixation white cross on a
black background without thinking anything; and the cross was presented in the center of the computer screen.
The session was called “Resting 1”. After the Resting 1 session, the natural scenery video was played accompa-
nied by soft piano music, and we called this session as “Nature”. Following the natural scenery video clip, four
different movie trailers were presented and the time duration of trailers varied for each one. We named those
sessions of movie trailers as “Movie 1”, “Movie 2”, “Movie 3” and “Movie 4” in order. The subjects might see
different movie trailers, but all the four trailers were not on theater before the date of the experiment. The release
dates for these trailers selected in this study were between Sept. 2011 and Aug. 2012. The collection of selected
trailers included different movie genres such as comedy, romance, animation, action, etc. Moreover, the movies
were from several different countries, but all the movie trailers had Traditional Chinese subtitles. Finally, a ses-
sion analogous to Resting 1 was added to the end of the experiment. The subjects had to look at a fixation white
cross again and not to think about anything as what they did in Resting 1. The second resting session was called
“Resting 2” and the duration was 3 minutes, too. There were 10-sec rests between sessions and a cue was dis-
played on the screen before the video clips p layed.
2.3. Data Analysis
We used sample entropy (SampEn) [8] to analyze the EEG signal. SampEn is based on the concept to measure
the randomness of a time series. We denote the SampEn of an N-length time series as SampEn (m, r, N), and it
depends on three parameters: m is embedded dimension; r is tolerance; and N is estimated data length. Given a
time series X of length n, Xi = {x1, x2, , xn}. The value of SampEn is computed by the following process:
1. Let l = m.
2. Form two set of vectors:
( )
, ,...,,1
mii im
uixxxiN l
=≤≤ −
( )
{ }
, ,...,,1
m jj
ujxxxj Nl
=≤≤ −
3. Define distance between two vectors from sets (1) and (2) as:
()( )() ()
( )
( )
[,] max11
mm km
d uiujabsoluteuikujk
=+− −+−
4. Define
)( rn
as the number of vectors
which distance was within r of
, where j i.
5. Define
( )( )()
Brnr Nm= −−
and calculate the average of
( )
for all i as:
( )( )
Br Br
6. Change the dimension m to m + 1, repeat step 2 - 5 and similarly calculate
( )( )
Ar Ar
( )( )( )
SampEn, ,ln
mrNAr Br
= −
Figure 2. Flowchart of experiment.
Resting 1Resting 2Movie 1Movie 4Nat u re
180 sec180 sec180 sec
T1 T4
P.-S. Wang et al.
Assume the data series X = {x1, x2, … , xn} was the EEG recoding of one channel in one of the experimental
session, and X was filtered out the 60 Hz component. We calculated the SampFn for every second, that is, 500
data points, because of the sampling frequency of the EEG system was 500 Hz. We set m = 2, r = 0.15 and N =
500 and calculated the value of SampEn for each second and obtain a set Y = {SampEn(x1x500),, Sam-
pEn(x(n-500)+1xn)}, in which the number of data was 1/500 times of the data length of each EEG recording X.
For example, if the data X in the resting state was 180 seconds, i.e., n = 500 × 180, the number of resultant
SampEn values was 180. We computed the distribution of the SampEn values in different 7 sessions of each
channel. There was a peak for the SampEn value between 2 and 3, especially in Fp1, Fp2 and Fz; and this pat-
tern was not observed in occipital channels. We computed the ratio of the number of the SampEn values in Y
which were between 2 and 3 to the total data number of Y, with only Fp1, Fp2 and Fz considered.
the number of the SampEn in Y which were between 2 to 3
Complexity ratiototal data number of Y
The complexity ratios in whole 7 sessions of all subjects in Fp1, Fp2 and Fz were calculated and analyzed by
the Statistical Package for the Social Science (SPSS) 18.0.
3. Results
Figure 3 showed that the mean values of EEG complexity ratios in the 2 Resting sessions, the Nature session
and the 4 Movie sessions were apparently different. Different to the Resting and Nature sessions, the mean
complexity ratio declined significantly when subjects watched the 4 Movie sessions. The mean complexity ra-
tios of the Nature session were higher than Movie sessions but lower than the Resting 1 and Resting 2 sessions.
The mean complexity ratio at both of the Resting 1 and Resting 2 sessions were highest in all sessions.
In order to examine whether the complexity ratios among different sessions were significantly different, we
compared the Resting 1 sessions with other sessions, Nature session with other sessions, and any session with its
adjacent session using the paired t-test. Results were presented in Table 1. Results demonstrated that the com-
Figure 3. The mean and standard deviation of complexity ratio in the 7 sessions. Horizontal axis:
7 sessions. Left to right: r1, nature, mv1, mv2, mv3, mv4, r2 (r1 and r2 stand for resting 1 and
resting 2; mv 1 - 4 stands for movie 1 - 4). Vertical axis: the value of complexity. Top to bottom:
Fp1 (blue), Fp2 (green) and Fz (pink).
P.-S. Wang et al.
Table 1. Statistical comparison of complexity ratios between different sessions. r1 and r2 stand for resting 1
and resting 2; mv 1 - 4 stands for movie 1 - 4. Top: compared with Resting 1. Middle: compared with Nature.
Bottom: compared with previous session. *: p < 0.05. **: p < 0.00 1.
Resting 1 session was compared with other sessions
R1\N R1\MV1 R1\MV2 R1\MV3 R1\MV4 R1\R2
p-value 0.000** 0.000** 0.000** 0.000** 0.000** 0.524
R1\N R1\MV1 R1\MV2 R1\MV3 R1\MV4 R1\R2
p-value 0.000** 0.000** 0.000** 0.000** 0.000** 0.410
R1\N R1\MV1 R1\MV2 R1\MV3 R1\MV4 R1\R2
p-value 0.049* 0.005* 0.000** 0.009* 0.024* 0.718
Nature session was compared with other sessions
N\R1 N\MV1 N\MV2 N\MV3 N\MV4 N\R2
p-value 0.000** 0.000** 0.000** 0.000** 0.005* 0.001**
N\R1 N\MV1 N\MV2 N\MV3 N\MV4 N\R2
p-value 0.000** 0.000** 0.000** 0.001** 0.003* 0.000**
N\R1 N\MV1 N\MV2 N\MV3 N\MV4 N\R2
p-value 0.049* 0.005* 0.001** 0.049* 0.124 0.072*
Comparison between two adjacent sessions
R1\N N\MV1 MV1\MV2 M V2\MV3 MV3 \MV4 MV4\R2
p-value 0.000** 0.000** 0.214 0.189 0.325 0.000**
R1\N N\MV1 MV1\MV2 M V2\MV3 MV3 \MV4 MV4\R2
p-value 0.000** 0.000** 0.477 0.439 0.249 0.000**
R1\N N\MV1 MV1\MV2 M V2\MV3 MV3 \MV4 MV4\R2
p-value 0.049* 0.005** 0.198 0.099 0.622 0.049*
plexity ratios in Resting 1 session were significantly different from that in Nature and any Movie session, and
that the complexity ratios in Nature session were significantly different from the Resting and Movie sessions.
Besides, the complexity ratios between ant two movies were insignificant.
P.-S. Wang et al.
4. Discussion and Conclusions
We used the SampEn algorithm and introduced a novel complexity ratio to analyze the EEG signals acquired
from subjects being resting, watching video clips of Nature scene and Movie trailers. Results showed that the
mean complexity ratio in Movie sessions was lower than that in the Resting and Nature sessions. The decreases
of mean complexity ratios can be attributed to atte ntion engagement. A previous study also found that the EEG
values of SampEn were lower in attention state than in inattention state, and the differences were mainly in Fp1,
Fp2, F3, F4 [9]. Therefore we suggest that the complexity ratios decrease when subjects watch Movie sessions
reflect that the subjects are in attention to watch movie trailers and the changes of brain status can be detected on
their EEG.
It has been reported that fatigue would make the values of SampEn reduce as well [10]. In other words, the
EEG signals are less complex during fatigue. To ensure that fa t igue was not a major influence in our study, we
have observed in Figure 2 that the mean complexity ratio of Movi e sessions did not decrease with respect to
time. In addition, the mean value of complexity ratios of Resting 2 session was noticeable higher than the pre-
vious session (Movie 4), and was almost the same as that of Resting 1 session. This suggests the reductions of
complexity ratios in Movie sessions should not be caused by mental fatigue, but by attention.
In short, we compute the mean complexity ratios of the EEG signals acquired from the Resting, Nature, and
Movie sessions. The mean complexity ratios of the Movie sessions were significantly lower than that of Nature
and Resting sessions, suggesting the mean complexity ratio can be an effective index to differentiate inattention
state and attention state when subjects watched movie trailers. Accordingly, we may use the complexity ratio of
EEG as an effective index to identify whether a movie trailer can draw people’s attention or not.
This study is supported by National Science Council, Chinese Taipei (NSC 101 -2221-E-010-004-MY2 & NSC
101-2314-B-733-001-MY2) and by the project of Center for Dynamical Biomarkers and Translational Medicine,
National Central University, Chinese Taipei (NSC 102-2911-I-008-001).
[2] e -trailers/
[3] Fi sher, C.E., Chin, L. and Klitzman, R. (2010) Defining Neu romarketin g: Practices and Professional Challenges. Har-
vard Review of Psychiatr y, 18, 230-237.
[4] Pradeep, A. (2010) The Buying Brain: Secrets for Selling to the Subconscious Mind. John Wiley & Sons Ltd., Chiche-
[5] Yıl maza, B., Korkmazb, S., Arslanb, D.B., Güngörb, E. and Asyalıc, M.H. Like/Dislike Analysis Using EEG: Deter-
mination of Most Discriminative Channels and Frequ encies. Computer Methods and Programs in Biomedicine, 113,
[6] Morris, J.D., Klahr , N.J. and Shen, F., et al. (2009 ) Mapping a Multidimensional Emotion in Response to Television
Commercial s. Human Brain Mapping, 30, 789-796.
[7] Vecchiato, G., Toppi, J. and Astolfi, L., et al. (2011) Spectral EEG Frontal Asymmetries Correlate with the Expe-
rienced Pleasantness of TV Commercial Advertisements. Medical & Biological Engineering & Computing, 49, 579-
583. 47-x
[8] Rich man, J.S. and Moorman, J.R. (2000) Physiological Time -Series Analysis Using Approximate Entropy and Sample
Entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278, H 2039 -H20 49.
[9] Yan, N., Wan g , J., Wei, N. and Zong, L. (2007) Feature Extraction and Classification of Attention Related Electroen-
cephalographic Signals Based on Sample Entropy. Journal-Xian Jiaotong University, 41, 1237.
[10] Tran , Y., Thuraisingham, R.A., Wijesuriya, N., Nguyen, H.T. and Craig, A. (2007) Detecting Neural Change during
Stress and Fatigue Effectively: A Comparison of Spectral Analysis and Sample Entropy. Proceedings of the 3rd Inter-
national IEEE EMBS Conference on Neural Engineering, Hawaii, 2-5 May, 2-5.