Abstract—In an increasingly knowledge based world, people are confronted with an explosion of information from the environment which must be viewed in restricted attention spans. Hence there is a need to investigate how best to model our Visual Attention (VA) with a view to allocate our attention efficiently. We use the color-word Stroop task combined with electroencephalogram (EEG) to model VA: subjects undertake the Stroop task and their EEG is recorded. This is in contrast to other studies that use techniques such as Event Related Potentials (ERP), Contextual Modeling Frameworks, eye movements and facial recognition. The paper presents a simple and useful model to recognize VA dynamically. We use the linear EEG features of different cortical fields as the main inference factors, and take the response time (RT) of the Stroop task as a metric to quantify subject performance. First, we obtain the most relevant EEG feature vectors from the recording, using a correlation analysis. Second, we use experimental data for training the VA model, using a regression method. Last, we then apply further experimental data to test the proposed model. The results from the tests conducted demonstrate that our model maps visual attention very closely.

Index Terms—Correlation Analysis, EEG, Linear Regression, Stroop task, Visual Attention (VA)

I. INTRODUCTION

Faced with an ever increasing amount of information presented to us in our daily lives, there is a need to filter and structure our perception of this knowledge, hence Attention Recognition (AR), and in particular Visual Attention (VA), the focus of our work, has become a popular topic of research. There are many applications where we can apply VR, such as monitoring Student VA with a view to dynamically change subject content and difficulty; monitoring Web User VA, so that servers can be directed to download content valuable to the user; monitoring Driver VA, so as to reduce accidents due to inattention.

This work was supported by the National Basic Research Program of

China(973 Program) (No.2011 CB711001),the National Natural Science Foundation of China (grantNo.60973138), the EU’s Seventh Framework Programme OPTIMI(grant No.248544), the Fundamental Research Funds for the Central Universities (grant No. lzujbky-2009-62), the Interdisciplinary Innovation Research Fund for Young Scholars of Lanzhou University (grant No.LZUJC200910).

Author Bin Hu is with School of Information Science & Engineering, Lanzhou University, Lanzhou, CO 730000China, School of CTN, TEE, Birmingham City University, Birmingham, UK (corresponding author phone: 86-0931-8912779; fax: 86-0931-8912779; e-mail: bh@lzu.edu.cn).

Authors Xiaowei Li, Qunxi Dong, Jianyuan Zhang are with School of Information Science & Engineering, Lanzhou University, Lanzhou, CO 730000China (e-mail: dongqunxi@gmail.com).

Author Martyn Ratcliffeis with Birmingham City University, Birmingham, UK (e-mail: Martyn.Ratcliffe@bcu.ac.uk).

A. Visual Attention (VA) VA is the cognitive process of selectively concentrating on

one aspect of the environment while ignoring others. Four processes are fundamental to attention: working memory, top-down sensitivity control, competitive selection, and automatic bottom-up filtering for salient stimuli [1]. VA is a selective process, which is usually conceptualized as being related to specific cognitive and brain resources. VA has also been referred to as the allocation of processing resources. Focalization and concentration of consciousness are its essence; it is a condition diametrically opposed to the confused, dazed, scatterbrained state. Marisa Carrasco identifies five topics of VA research in their review paper concerning psychophysical research on distinct attention systems: single-unit neuro-physiological research of monkey neuronal response with respect to attention states; neuro-imaging studies using attention tasks; research on eye movements related VA; computational modeling of VA [2].

B. Neurophysiological research on VA There are many neuro-physiological studies related to VA,

in particular, the following use EEG based techniques: [3] demonstrates that the theta band (4-7 Hz) is related to drowsiness, the alpha band (8-13Hz) to relaxation and creativity, and the beta band (13-25Hz)to activity and alertness.[4] Shows that induced gamma band activity in the human EEG is closely related to visual information processing and attention perceptual mechanisms.[5] reviews recent findings on the role of pre-stimulus alpha (~10Hz) oscillatory activity for visual perception and constructs a neuro-cognitive model that is able to account for various findings in temporal attention paradigms. [23] Concludes that theta activity of the frontal midline is correlated with attention-demanding tasks. Neuro-imaging investigations have revealed three networks related to different aspects of attention: alerting, orienting, and executive control. Alerting is defined as maintaining a state of high sensitivity to incoming stimuli, and is associated with the frontal and parietal regions of the right hemisphere. Orienting is the selection of information from sensory input, and is associated with posterior brain areas including the superior parietal lobe, the temporal parietal junction and the frontal eye fields. Executive control relates to the mechanisms for resolving conflict among possible responses. It has been shown to concern the anterior cingulated and the lateral prefrontal cortex [2].To observe the detail of attentional processing, most attentional studies investigate VA, while subjects perform specific tasks or view controlled environments:[8] uses various metrics of attention to identify the relationship between attentional states and physiological

A Study on Visual Attention Modeling--A Linear Regression method based on EEG

Qunxi Dong, Bin Hu*, Jianyuan Zhang, Xiaowei Li, Martyn Ratcliffe

Proceedings of International Joint Conference on Neural Networks, Dallas, Texas, USA, August 4-9, 2013

978-1-4673-6129-3/13/$31.00 ©2013 IEEE 1183

signals. The color-word Stroop task is widely study of VA [14, 20].

Though many studies have shown that multfactors are better than relying purely on EEG/associated multi-modeling introduce subjectivhence increase the complexity and variance oWe therefore apply objective measurements omore cortical fields to mitigate the use of EEGresearch, we consider features of three commobands (alpha, theta, beta) in the aforementionewhile subjects are conducting Stroop tasks [18

C. Attention modeling method Attention is easily affected by mood and en

surrounding, so an effective method to construrepresentative VA models is difficult. Figure1model commonly used by VA researchers.

Fig. 1. The process of Attention resear

Features used to model human cognitive sta

classified into the following categories: casuafeatures, physiological features, performance combinations of these categories [6]. [7] Usesrecognize the attention states of a subject duriby the application of Bayes classification algoKNN. In methods to investigate drivers fatiguBayesian Network (DBN)is used with paramemood, sleeping quality, circadian rhythm, expmovements and so on. Firstly the subjects arenight’s sleep, they are then asked to do some atasks. During the tasks different factors are mcomposed into a composite fatigue score to deof fatigue [3, 8]. [6] presents a method for muinferring of human cognitive states by integranetwork and information fusion techniques. [1other methods of computational neurosciencemodeling, such as neural networks modeling ainterpretation. Psychological modeling is not cerebral plausibility, hence there is a general dthe BN or DBN methods when determining imparameters such as the prior probability and cprobability [6, 9]. [22] Shows that result fromestimation using the full EEG spectrum compresults using a linear regression model. [24] Cmultiple linear regression approaches can be eimplemented for measuring evoked potentialsmodalities. Our approach is to mine as much afrom the EEG as possible, to obtain an objectiactivity.

D. Our work Our research attempts to find an effective V

can give a macroscopic description of VA. Th

employed in the

ti-inference /EMG, the ve elements and f the analysis.

of EEG, but use G alone. In our on frequency ed cortical fields, 8, 22].

nvironmental uct 1 shows a process

rch

ates can be al/contextual features, and

s EEG features to ing their learning orithms and ue, a Dynamic eters relating to pression, eye e deprived of a attention based

measured and escribe the level ultimodality ating neuro-fuzzy 10] Describes and cognitive and image concerned with difficulty with mportant conditional

m neural network pare favorably to Concludes that easily s in sensory as information ive view of brain

VA model which he Stroop task is

considered to be the “gold standard”[14], so we use it as the experimentaResponse Time (RT) as a metric to qperformance [8].

In our research, we mainly use EEphysiological state and Response Timthe VA model [7, 8, 11]. The reasonbelieved to represent not only brain the whole body [7]. If the EEG featudescriptive and the cognitive modelimodel based on EEG should reflect we consider EEG features of the corthree bands (alpha, theta, beta) with linear alpha band features per channneuro-physiological theory, we attemdynamical VA model, and we use Rassess the proposed VA model.

Our results reveal that the model rvery well. In the following sections construct the VA model based on EEbriefly describe the special EEG featSection 3 presents the process of buiSection 4 shows the experiments to vour VA model. Section 5 deals with and discusses the advantages and dramethodological choices. Finally we further reflection on the results.

II. EEG FEATUREVA involves not only most fields

lobe, frontal lobe, occipitallobe, paribetween different cortical fields, butinformation transfer between these rnecessary to use many electrodes abIn this study, we adopt 19 electrodesFp2, C3, C4, Cz, F3, F4,F7,F8, Fz, OT5, T6, using A1-A2 as the referenc

As section1.2 discussed, three banalpha band (8-13Hz), beta band (13-brain activities with respect to VA. 9compute the three bands separately. following [12, 13]:

1. PPmean: Peak to peak am2. MeanSquare: the mean sq

wave. 3. Variance: the Variance am4. Activity: The signal powe5. Mobility: The mean frequ6. Complexity: The change i7. F0: Center frequency of a 8. Max Power: Maximum p

band wave. 9. Sum Power: Sum power a

These features are extracted from during VA tasks. In the following sefollowing number nomenclature to rAlpha_F3_1 means the PPmean featalpha band.

” of attentional measures al task and use the quantify subject

EG features of me (RT) to parameterize

n for this is that EEG is function but the status of

ures are sufficiently ing is accurate, the VA visual attention well. So

rtical fields related VA, in 19 channels, giving 9

nel. Based on standard mpt to construct a

RT of attentional tasks to

reflects visual attention we demonstrate how we EG. In the section 2 we tures used in this study. ilding the VA model. verify the performance of the experimental results awbacks of our conclude with some

ES AND VA of the cortex (temporal ietal lobe) and junctions t also the complex regions [2]. It is therefore bove these cortical fields. s (10/20 system): Fp1, O1, O2, P3, P4, Pz,T3, T4, ce electrodes [22]. nds: theta band (4-7 Hz), -25Hz) are used to reflect 9 EEG features are used to The nine features are as

mplitude of a band wave. quare amplitude of a band

mplitude of a band wave. er of a band wave. uency of a band wave. in frequency a band wave. band wave.

power spectral density of a

amplitude of a band wave. the three wave bands

ection, we will use the represent the nine features: tures of channel F3 in the

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III. EXPERIMENTS AND ANALYSIS M

A. Experiments Experimentation was performed on 6 heal

males and 3 females, aged 21-26.The subjindividually. They were seated in front of a Cused three keys of the keyboard: ’R’,’G’, ‘B’ Stroop task. The distance between subjects ancm or so. The experimental tasks consistewords of different colors: Green, Red, Blue. 20 trials (words), with each participant sessions, the interval between sessions was 5sof the trials is random. In each trial, the partito give the color of the display words, as quwhile trying to make no mistakes. A trial presentation of the colored word stimuluduration. Following stimulus presentation, blank for 0.5s [14].

B. Analysis Methods We use RT as the metric of attention and E

described in [8]. The VA model is then constrthe Stroop task. Figure2 shows the stages of a

Fig. 2 The process of VA modeling

1) EEG Features and RT of Each Block

Steinn et al. has shown that high reliability average montage and reliability increases withup to 40s, but longer epochs gave only margin[15]. Before the Stroop task, EEG is recordedwhen the subjects are in an ambient state. ThiEEG data is computed as the standard baselinsubject’s EEG features [6]. During the performparticipant, EEG and the reaction time of eachrecorded. After EEG raw data is collected fromdevice at a sampling rate of 512Hz, it is filtereFourier transformed (FFT) and a Hanning winuse FastICA and ApEn algorithms for band sefeature extraction [13, 16]. The EEG features are computed once every two seconds (the lenFeature computation and correlation analysis to each subject by using values from each ind[17, 18]. Each session feature gets 20 relative correspond to 20 trials. The session data is the

METHODS

lthy volunteers, 3 jects were tested CRT monitor and to respond to the

nd screen was 50 ed of identifying Each session had

performing 20 s or so. The order icipants are asked

uickly as possible, started with the

us, each of 1.5s the screen was

EEG processing as ructed to model analysis:

g

is obtained with h epoch length nal improvement

d for about 40s, is segment of nes of each mance of each h trial are m the Nexus32 ed by Fast ndow. We then eparation and of each session

ngth of a trial). are normalized

dividual baseline value points, en constructed as

a 20*171 matrix. The first column isthe 2-172 columns are the features ochannels*3 bands*9 features).

Fig. 3 The Matrix of ea

The recording of each subject has 20the most relevant EEG feature vectocorrelation analysis, and derive the mSecond, each subject has 20 sessionsof the data, we define a block to avethe overlap is then 4 sessions, with 1as shown in Figure4. The vector is cwhere n represents the number of blnumber of session:

Fig. 4 Blocks in one

4n

i nnBlock

+=∑=

2) Construction and verification meUsing coefficient analysis and reg

we construct a VA model based on Ethe correlation analysis, we simplifythree features per block. They are sethat the features are most suitable inrelated to attention (Prefrontal cortexTemporal parietal) [1, 2], and that adsame feature collection. We use priotraining data, and at least one PosterFigure5 shows the process of inferenEEG features are normalized, so as tbetween the attentional metric RT anprior block, we make the vector EEGand apply multiple linear regression linear regression algorithm shown inalgorithm returns the vector ß of reglinear model shown in equation (3).

s the RT of each trial, and of each channel (19

ach session

0 sessions. First, we obtain or per session, using matrix of each session. s of data, for the stability

erage 5 sessions at a time, 16 blocks for each subject, codified in equation (1) ocks and i represents the

e session

5iSession

(1)

ethods

gression analysis methods, EEG features. Based on y the VA model by using elected under the criteria n the main cortical fields x, Posterior parietal cortex, djacent blocks have the or block data as the rior block as testing data, nce. In this study, RT and to construct a linear model nd EEG outputs [9]. In the G Index (EI) equal to RT, analysis, by the Multiple

n equation (2). This gression coefficients in the

Features_prior is a 20*3

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matrix, it presents the collection of features seprior block. EI_Prior is a 20*1 vector, it inclureaction time of each block. ß is a 3*1 vector,weights of each feature selected from the prioFeatures_Posterior is a (20*n)*3 matrix, it prefeatures set from the posterior n blocks. EI_Povector, it is computed by the regression mode

(REGRESS EI_Prior, Features_Pβ =

EI _ Posterior Features _ Posβ= ×

The model is then used to compute the featthe posterior block, and EI vector is obtained.correlation analysis (CA) between RT and EI block is used to verify the VA model. The problocks is shown in Figure6:

Fig. 5 The process of inferring

Fig. 6 The process of VA model construction and

IV. EXPERIMENTAL RESULT

In the experimental study, each subject haddataset had 16 blocks, where each block is a mAs Figure6 shows, the correlation analysis is asame feature collection between adjacent blocthen selected from the prior block as training regression analysis. The weights of each featuthe analysis are used to construct the VA modposterior block is calculated from equation 3 tfeasible of the model. The resulting blocks frosubject are presented in Figure 7, where the Pcorrelation is 0.719 between EI and RT. It canEI and RT traces show similar trends.

Subsequently, the whole block of each subjand verified. Figure8 shows the performance It can be seen that the VA model has a strong between EEG features and RT. The reliabilitycan be quantified by the correlation coefficienshowing that the coefficients of correlation ar0.6.The exception is the correlation, between may be due to experimental error.

During the process of VA modeling, the chfeatures may be changed based on the subjectstates. Take block 6 to 10 for an example, the stable, the weights of these three features are

elected from the udes the mean , it gives the or block. esents the same osterior is 20*1

el.

)Prior (2)

sterior (3)

ture collection of Finally of the posterior

ocessing between

d verification.

S d 6 datasets. Each matrix of 20*172. applied to the cks. Features are data for the

ure derived from del. The EI of the to verify the om a typical

Pearson n be seen that the

ject are modeled of the VA model. correlation

y of this model nt, with results re mainly above 12-13, which

hosen EEG t attentional feature set is computed using

equation2. The VA model is construfrom block 6, the weights of each chTable I. Equation4 is used to compu10.

Fig. 7 EI(gray line) derived from VA modetask RT(black line) in block 4. The two curvcorrelation is 0.719, p<0.001).

Fig. 8 Features selected from Su

EI Alpha _ Fz Alpha _ Fz _ 4 Alpha Alpha _ Pz Alpha _ Pz _1

= × ++ ×

TABLE I

THE WEIGHTS OF EACH FEATURE TR

The RT of block 7-10is then used

to verify EI. Figure9 gives the modeperformance of blocks 7-10. From ththe EI vector, obtained from the VASince the attentional state of subjectfeature correlation analysis must be EI computation. Relaxing this constrspeed of inference, but lower the accfurther work.

Table II presents the main featuremodeling. It shows that there are 3 mby the EEG feature set. The features

ucted based on the features hosen feature are shown in ute the EI of block 7, 8, 9,

el versus the standardized Stroop ves track each other well (Pearson

ubject blocks(p<0.05)

a _ C4 Alpha _ C4 _1× (4)

RAINED FROM BLOCK 6

d as the attentional metric eling and verification he figure9, we can see that

A model, tracks RT well. s may change randomly, made before every current raint will improve the curacy, this is a topic for

es set used in the VA main segments separated s selected in block1 to 5

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are relatively stable, they mostly come from the cortical fields of Prefrontal and Temporal parietal, with the most effective

Fig. 9 EI(gray line) versus the standardized Stroop task RT(black line) in block 7-10. The two curves track each other well(Pearson correlation is 0.637, p<0.001).

TABLE II FEATURES SETS OF EACH INFERENCE

EEG bands are beta and theta bands: the blocks from 5 to 10, select EEG features from the cortical fields of Posterior parietal cortex and Temporal parietal, and the effective EEG band is mainly alpha band; the blocks from 13 to 16, select EEG features from the cortical fields of the cortical fields of Prefrontal cortex, Posterior parietal cortex and Temporal parietal, and the effective EEG bands are theta, beta and alpha bands. From the results, we can conclude that as the subject attention state is relatively stable, so the adjacent blocks have the similar EEG feature collection, as in block 1 to 4 and 5 to 10. To find the common relation between EEG and RT, we tend to choose the most common EEG features among blocks. However there are also some blocks that have very distinct features (e.g. the features in block 10-13).The reasons for these differing patterns are unclear and we propose to pursue this in future research. Here we focus on the common features.

V. DISCUSSION AND CONCLUSION In this study, after correlation analysis between EEG

features and RT, three EEG features are selected to construct a VA model with respect to a Stroop task. This research focuses on the macroscopic description of VA, using EEG rather than ERPs, fMRI and so on. Hence the analysis and processing methods may be conducted in real time. This research is different from most of the traditional cognitive and behavior research related to neuroscience. Traditional research focuses on responses after several milliseconds before or after a stimuli to derive conclusions about the microcosmic and physiological changes of the brain. Some studies also focus on the dynamic process of VA, many such methods, like DBN, and Neuro-Fuzzy Networks, combine different contextual factors that may affect attentional state. It is true that many such environmental factors have been demonstrated to alter the quantitative EEG, such as pharmacological agents caffeine, nicotine and alcohol, the individual differences in age, gender and mental load at the time of data capture [19]. To reduce these factors, in our research, we average the adjacent trials to reduce the randomness of EEG features, and compute EEG features relative to a baseline.

From the experimental results, we can see that similar cortical fields are significantly related to RT in most of the adjacent blocks. One VA model based on a block can infer the next several blocks: the EI tracks the RT well (see figure9). The reliability of this model can be quantified using an intra-class correlation coefficient: from figure8, we can see that the reliability of this model mainly remained around the 0.700 level (p<0.05). But there are few blocks that do not track the VA model very well, this may be due to sampling time or an indication that other features are required to fully express the VA. The results from this study, using feature correlation analysis, indicate that there are three significant cortical fields: Prefrontal cortex, Posterior parietal cortex and Temporal parietal. The results also indicate that the linear features F0 and Maximum, contain most common features used to construct the VA model, whereas features in the alpha bands are mostly used to derive the relation between RT and EEG. Hence the research clearly demonstrates that a VA model can be constructed from EEG features dynamically.

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VI. FUTURE WORK Since there are few studies concerned with EEG based VA

modeling, the stable EEG features, used to express the change of VA, require further investigation and corroboration. In this research, we focused on 9 linear features. Among these features we found that F0 (Central frequency of a waveband) and Max Power(Maximum power spectral density of a waveband) are particularly effective parameters in VA modeling. However several other features do not perform well in some blocks indicating that a more comprehensive experimental model is required that includes additional attentional metrics, such as the accuracy and eye movements during the task. There is also a requirement to test more EEG features in order to find more stable EEG features to express the continuous change of VA.

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