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Estimating cognitive workload using wavelet entropy-based features during an arithmetic task Pega Zarjam a,b,n , Julien Epps a,b , Fang Chen b , Nigel H. Lovell c a School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia b ATP Research Laboratory, National ICT Australia, Eveleigh, NSW 2015, Australia c Graduate School of Biomedical Engineering, The University of New South Wales, Sydney, NSW 2052, Australia article info Article history: Received 6 April 2013 Accepted 23 August 2013 Keywords: EEG Memory workload Entropy features Frontal lobe Delta band abstract Electroencephalography (EEG) has shown promise as an indicator of cognitive workload; however, precise workload estimation is an ongoing research challenge. In this investigation, seven levels of workload were induced using an arithmetic task, and the entropy of wavelet coefcients extracted from EEG signals is shown to distinguish all seven levels. For a subject-independent multi-channel classica- tion scheme, the entropy features achieved high accuracy, up to 98% for channels from the frontal lobes, in the delta frequency band. This suggests that a smaller number of EEG channels in only one frequency band can be deployed for an effective EEG-based workload classication system. Together with analysis based on phase locking between channels, these results consistently suggest increased synchronization of neural responses for higher load levels. & 2013 Elsevier Ltd. All rights reserved. 1. Introduction Researchers in cognitive science, psychology, Brain-Computer Interfaces (BCI), and Human-Computer Interaction (HCI), recognize the importance of gaining information about the user's cognitive processing capacity and memory workload or task engagement or a combination of these [1,2]. This is due to the fact that the human cognitive system has a limited capacity for processing and holding information in the mind [3]. Therefore, if the increasing demand of cognitive activity exceeds the capacity limits, it may lead to cognitive overload, performance breakdown or even failure when accomplishing a cognitive task [3]. This should be prevented, particularly in highly demanding situa- tions and workplaces where focused thinking and sustained perfor- mance is a key determinant, such as air trafc control, medical and emergency applications, and military operations or when designing or developing adaptive interfaces. Thus, there is a signicant need to measure the amount of cognitive demand, precisely during a cognitive process to maintain efciency, productivity and also avoid cognitive overload. Conventional methods for measuring cognitive workload mainly include the measurement of reaction time, performance accuracy and self-assessment [4, 5]. However, these methods are based on the assumption that subjects are able and willing to respond accurately across tasks [6]. Besides, to date they have been measured in a post- hoc manner and are not available as on-line and continuous measure- ments, during the progress of the cognitive task. Physiological methods (i.e. brain activities, pupil dilatation [7], heartbeat rate, [7], hormone levels [6], galvanic skin response (GSR) [8]) have also been utilized in this eld, previously. Although, among the physiological measures, brain activity measurement has been known as the most sensitive and consistent reector of cognitive workload [2]. Since, it can interface more directly with the brain, which is the seat of cognitive activities using advanced brain-sensing technologies; such as Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) [9]. Behavioral measures (i.e. such as disuencies in speech [10] and mouse and pen-input movements [11]) can also reect cognitive workload, but they are the most distant level of measurement from the cognitive activity, and are also unable to measure the load, continuously. EEG is a noninvasive neuro-imaging technique widely used to monitor and measure various types of cognitive activities, workloads and physiological states of the brain [12]. It is reliable, economical, and easy to use to record the neural electrical uctuations of the brain, along the scalp. The EEG not only manifests the brain activity characteristics but can also reect the underlying neural dynamics (using suitable quantiers), due to its high temporal resolution (about 1 ms) [13, 14]. This is while, neural communications mostly occur in time-scales between 1 ms and 100 ms depending on brain proces- sing time for various stimuli, which make fMRI temporal resolution (about few seconds) unsuitable for assessing many cognitive tasks [14]. Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/cbm Computers in Biology and Medicine 0010-4825/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.compbiomed.2013.08.021 n Corresponding author at: School of Electrical Engineering and Telecommuni cations, The University of New South Wales, Sydney, NSW 2052, Australia. Tel.: þ61 293854803. E-mail address: [email protected] (P. Zarjam). Computers in Biology and Medicine 43 (2013) 21862195

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Page 1: Estimating cognitive workload using wavelet entropy-based features during an arithmetic task

Estimating cognitive workload using waveletentropy-based features during an arithmetic task

Pega Zarjam a,b,n, Julien Epps a,b, Fang Chen b, Nigel H. Lovell c

a School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australiab ATP Research Laboratory, National ICT Australia, Eveleigh, NSW 2015, Australiac Graduate School of Biomedical Engineering, The University of New South Wales, Sydney, NSW 2052, Australia

a r t i c l e i n f o

Article history:Received 6 April 2013Accepted 23 August 2013

Keywords:EEGMemory workloadEntropy featuresFrontal lobeDelta band

a b s t r a c t

Electroencephalography (EEG) has shown promise as an indicator of cognitive workload; however,precise workload estimation is an ongoing research challenge. In this investigation, seven levels ofworkload were induced using an arithmetic task, and the entropy of wavelet coefficients extracted fromEEG signals is shown to distinguish all seven levels. For a subject-independent multi-channel classifica-tion scheme, the entropy features achieved high accuracy, up to 98% for channels from the frontal lobes,in the delta frequency band. This suggests that a smaller number of EEG channels in only one frequencyband can be deployed for an effective EEG-based workload classification system. Together with analysisbased on phase locking between channels, these results consistently suggest increased synchronizationof neural responses for higher load levels.

& 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Researchers in cognitive science, psychology, Brain-ComputerInterfaces (BCI), and Human-Computer Interaction (HCI), recognizethe importance of gaining information about the user's cognitiveprocessing capacity and memory workload or task engagement ora combination of these [1,2]. This is due to the fact that the humancognitive system has a limited capacity for processing and holdinginformation in the mind [3]. Therefore, if the increasing demandof cognitive activity exceeds the capacity limits, it may lead tocognitive overload, performance breakdown or even failure whenaccomplishing a cognitive task [3].

This should be prevented, particularly in highly demanding situa-tions and workplaces where focused thinking and sustained perfor-mance is a key determinant, such as air traffic control, medical andemergency applications, and military operations or when designing ordeveloping adaptive interfaces. Thus, there is a significant need tomeasure the amount of cognitive demand, precisely during a cognitiveprocess to maintain efficiency, productivity and also avoid cognitiveoverload.

Conventional methods for measuring cognitive workload mainlyinclude the measurement of reaction time, performance accuracy andself-assessment [4,5]. However, these methods are based on the

assumption that subjects are able and willing to respond accuratelyacross tasks [6]. Besides, to date they have been measured in a post-hoc manner and are not available as on-line and continuous measure-ments, during the progress of the cognitive task. Physiologicalmethods (i.e. brain activities, pupil dilatation [7], heartbeat rate, [7],hormone levels [6], galvanic skin response (GSR) [8]) have also beenutilized in this field, previously. Although, among the physiologicalmeasures, brain activity measurement has been known as the mostsensitive and consistent reflector of cognitive workload [2]. Since, itcan interface more directly with the brain, which is the seat ofcognitive activities using advanced brain-sensing technologies; suchas Electroencephalography (EEG) and functional magnetic resonanceimaging (fMRI) [9]. Behavioral measures (i.e. such as disfluencies inspeech [10] and mouse and pen-input movements [11]) can alsoreflect cognitive workload, but they are the most distant level ofmeasurement from the cognitive activity, and are also unable tomeasure the load, continuously.

EEG is a noninvasive neuro-imaging technique widely used tomonitor and measure various types of cognitive activities, workloadsand physiological states of the brain [12]. It is reliable, economical,and easy to use to record the neural electrical fluctuations of the brain,along the scalp. The EEG not only manifests the brain activitycharacteristics but can also reflect the underlying neural dynamics(using suitable quantifiers), due to its high temporal resolution (about1 ms) [13,14]. This is while, neural communications mostly occurin time-scales between 1ms and 100ms depending on brain proces-sing time for various stimuli, which make fMRI temporal resolution(about few seconds) unsuitable for assessingmany cognitive tasks [14].

Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/cbm

Computers in Biology and Medicine

0010-4825/$ - see front matter & 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.compbiomed.2013.08.021

n Corresponding author at: School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia.Tel.: þ61 293854803.

E-mail address: [email protected] (P. Zarjam).

Computers in Biology and Medicine 43 (2013) 2186–2195

Page 2: Estimating cognitive workload using wavelet entropy-based features during an arithmetic task

On the other hand, it is known that as a result of informationprocessing in the brain, dynamical variables of its electrical activityvary and this can change time, space and frequency characteristics ofthe EEG signal [13]. Thus, it would be beneficial to find thesedynamical variables or features of EEG signals that are indicative ofworkload variations to successfully measure and classify the cognitiveworkload.

In previous research, a range of spectral features have beendeployed for this purpose using EEG signals, including signal'smaximum or average powers extracted from its power spectrumdensity (PSD) [9,15–17]. Entropy-based features such as the waveletpacket entropy, entropy synchronization [18,19] and approximateentropy [20] have been also used, but mainly in different mentaltasks classification not in working memory load/cognitive workloadclassification.

Applying non-linear or dynamical features, in classifying differentmental tasks (or in comparison with the rest condition), have alsoattracted attention, recently. These features are not only discrimina-tive of some mental tasks but also can reflect the underlying neuraldynamics. Therefore, dynamical measures like Correlation Dimension(CD) [21,22], Hurst Exponent (HE), Approximate Entropy (ApEn) andLargest Lyapunov Exponent (LLE) [20,23] have been used previouslyto measure the complexity or irregularity of the underlying braindynamics in different mental tasks.

On the other hand, spectral coherence [24] and Phase LockingValue (PLV) [25] features have also been used previously to interpretthe underlying brain dynamics from a synchronization point of view,when performing different mental tasks. The synchronization is ofinterest, due to the fact that different regions of the brain mustcommunicate to make the integration of sensory information possible,which is necessary for conducting many cognitive functions. Thus,synchronization of oscillating neural ensembles is an integrativemechanism that may bring a widely distributed neural set togetherinto a coherent ensemble that underlies a cognitive task [25].

Notwithstanding the plethora of previous EEG-based systemsfor measuring various cognitive states and pathologies proposedto date, the measurement of cognitive workload using EEG signalsis still in its early stages, and there is still a need to validatethe applicability of new approaches across different tasks, toestablish the precision of cognitive load measurement, and inves-tigate the underlying brain dynamics or behavior when perform-ing a cognitive task with varying difficulty levels.

For this study, a cognitive task with seven levels of difficulty wasdesigned, to examine the performance of the entropy-based featuresfor fine load level measurement and discrimination. To our knowledge,five levels are the largest number of cognitive task loads previouslyinduced [26,27], and our work proceeds this to seven. We also aim toinvestigate the effect of load on neural regularity or order from theEEG signals when the imposed memory workload varies, which wasnot investigated prior to our study. In our previous work, features suchas spectral entropy, CD, HE, ApEn, wavelet-entropic measures provedto be a good discriminator of imposed memory load and an indicatorof higher predictability (less irregularity) in the brain activity, whendealing with higher memory load [28–30].

The aim of the paper is threefold: first, it compares the perfor-mance of our proposed wavelet-entropic (wavelet entropy-based)feature set in precise estimation of working memory load in sevenlevels, which is the highest proposed, so far. Second, it investigates thefeatures' implications for synchronized and ordered neural activitiestowards a better understanding of the brain dynamics when dealingwith higher loads, as the relationship between cognitive workloadwith synchronized and ordered neural activity are yet to be fullyestablished. Finally, we compare the performance of this EEG-basedmethod with existing measures of cognitive workload; i.e. perfor-mance (accuracy of responses), self-assessment, and reaction time ofthe subjects.

2. Technical background

Entropy is a measure of regularity or order [31] and morespecifically in the case of EEG signals is a measure of the degreeof synchrony of the neural groups participating in differentneural responses [32]. When a stimulus is presented to thebrain, the neural generators are triggered accordingly, andrespond in a coherent way [13]. The EEG can reflect the activityof ensembles of these neural fluctuations in many frequencybands, which are active in a very complicated manner. Thisactivation is event-related and can be reflected by transitionfrom a disordered to an ordered state (or vice versa) in differentfrequency bands [13], thus can act as a potential feature. If thisentropy is calculated by wavelet transforms, it can offer a goodfrequency resolution at low frequencies and a good timeresolution at high frequencies (so-called multi-resolution prop-erty) which can capture the EEG signal variations with optimaltime-frequency resolutions [32].

When entropy is calculated at a particular wavelet scale,it provides a measure of the extent of uncertainty (disorganiza-tion) among the different wavelet coefficients at that scale. If thecoefficients all have roughly the same magnitude, this reflects arelatively random set of neural responses. In an extreme exam-ple, if all coefficients have the same magnitude, this can beconsidered analogous to a uniform distribution, which results inthe maximum entropy. Alternatively, in the opposite extremeexample, if one coefficient has a very large value, indicating astrongly coordinated neural response, this can be consideredanalogous to a density for which the probability of occurrence ofthat coefficient value is maximum and that of all others is zero,which results in the minimum entropy, i.e. the minimum uncer-tainty (the max organization). Therefore, as memory loadincreases, a more organized neural response and hence a lowerentropy value can be expected.

In the following section, we briefly review the technicalbackground of the features extracted from the EEG signals, inthis study.

2.1. Discrete wavelet transforms

The Discrete Wavelet Transforms (DWT) provides a time-scalerepresentation of a given signal, generated by dilation and transla-tion of a function known as the discrete mother wavelet.Its structure is obtained by mother wavelets and scaling sequencesdeducted from one octave to the next by a two-scale differenceequation [33]. At each octave level jAf1;2; :::; Jr log 2Ng, a signalxj�1½i� is passed through a low-pass filter and a high-pass filterwith impulse responses of g½i� and h½i�, respectively [33].This discrete decomposition at scale j can be mathematicallystated as

aj½i� ¼∑kxj�1½k�g½2i�k�; jZ1 ð1Þ

dj½i� ¼∑kxj�1½k�h½2i�k�; jZ1 ð2Þ

For each wavelet scale j, wavelets approximate coefficientsare given by aj½i�, and detail coefficients by dj½i�; i¼ 1;2; :::;Mj,where Mj ¼N � 2�j is the number of coefficients in scale j. Notethat x0½i� ¼ x½i� is the EEG segment under analysis, i¼ 1;2; :::;N,with N being the number of samples. The magnitudes of waveletapproximate (detail) coefficients in a particular scale j show theamount of the signal energy which resides in the correspondingfrequency band ½0�2�ðjþ1Þf s�Hz ð½2�ðjþ1Þf s�2�jf s�HzÞ. Here f srepresents the sampling frequency.

P. Zarjam et al. / Computers in Biology and Medicine 43 (2013) 2186–2195 2187

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2.2. Entropy

The parameters under study here are the DWT coefficients of thesegmented EEG signals, and hence these are used to constructempirical probability mass functions. For instance, in the case ofapproximate coefficients at the 3rd level (corresponding to the deltafrequency band-see Table 2), pi in Eqs. (4)–(7) can be estimated bya normalized histogram of a3½i�

pi ¼ja3½i�j

∑Mi ¼ 1ja3½i�j

ð3Þ

where M is the number of wavelet approximate coefficients (e.g. forthe window of length T¼5 s and sampling frequency of f s at the 3rdlevel; N¼ T � f s ¼ 320;M¼ ðN=23Þ ¼ 40Þ. Note that pi in (3) isnon-negative and the normalization ensures that ΣM

i ¼ 1pi ¼ 1; soP � fp1; :::; pMg is a valid probability mass function. The waveletcoefficients have shown their capability for EEG signals classificationin pathological diagnoses, previously [34,35].

We investigated four different entropies of the DWT coeffi-cients as discriminative features among seven load levels, as theseentropies showed promising results in other studies, involvingEEG signals [32].

The best-known entropy calculation method is the Shannonentropy ðHSHÞ; which for a given probability mass function P iscalculated as

HSH ½P� ¼ � ∑M

i ¼ 1pilnðpiÞ ð4Þ

where pi is the distribution of the DWT parameter of the ith EEGsegment. The relatively new Tsallis entropy ðHTSÞ; is based on ageneralized form of the Boltzmann–Gibbs entropy for dealing withnon-extensive settings [36]

HTS½P� ¼1

q�1∑M

i ¼ 1½pi�ðpiÞq� ð5Þ

Here qAð1;0Þ is the entropic index, also known as an index ofnon-extensively. Note that entropy is generally regarded to havean extensive property (i.e. its value depends on the amountof material provided). But there are systems in reality whosebehavior is strongly dependent on initial conditions, thus theirentropies are measured with non-extensive entropies [37]. Thegeneralized form of (5) includes the Escort–Tsallis ðHETSÞ, whichconsiders the Escort distribution of order q [36]

HETS½P� ¼1

q�11� ∑

M

i ¼ 1ðpiÞ1=q

" #�q !ð6Þ

Finally, the Renyi entropy ðHREÞ for a given discrete distributionP is calculated by [32]

HRE½P� ¼1

1�qln ∑

M

i ¼ 1ðpiÞq

" #ð7Þ

Note that if q-1, this entropy becomes the Shannon entropy asgiven by (4).

2.3. Multi-channel phase synchronization measure

The degree of synchrony of the neural groups participating inneural responses to different task load levels is of interest, andthis is assessed using the strength of phase synchrony betweenthe EEG signals. The phase synchrony here can be measured bythe PLV approach, which assumes that two dynamic systems mayhave their phase synchronized even if their amplitudes are notcorrelated [38]. To calculate the strength of phase synchrony

between the EEG signals acquired from CAf2;3; :::;32g channels,the following procedure is required:

1. For each EEG segment; x½i�, the instantaneous phase (IP); φ½i� iscomputed using the Hilbert transform [24].

2. For each electrode pair k and l, the PLV; i.e. Rkl is calculated

Rkl ¼1N

∑N

i ¼ 1eφkðiÞ�φlðiÞ

���������� ð8Þ

where N is the number of samples and ϕk [i] is the IP of the kth

channel. Note that RklA ½0;1�; where 1 implies perfect phasesynchrony between the two signals.

3. The multichannel phase synchrony measure among all C EEGsignals is defined as below

r¼ ΣCk ¼ 1Σ

Cl ¼ 1Rkl�C

C2�Cð9Þ

Note that the subtraction from the nominator and denominatorof C is because Rkk ¼ 1. The value of r approaches one when thePLVs between all pair-wise combinations of the signals are one, i.e.in perfect phase synchrony.

3. Materials

3.1. Experiment

We designed our cognitive experiment as an arithmetic task withseven levels of difficulty, starting from one digit addition (very easy)to multi-digit addition (extremely difficult). This is because there is arich literature on the concepts and procedure of mental arithmetic[39], and importantly this task allows the induction of many differentworkload levels. In [40], it is shown that the manipulation of thenumber of carry operations and the value of the carry is an importantvariable in varying the difficulty of arithmetic sums. Other relatedstudies have also demonstrated that mental calculation is a complexmental process that causes cortical activation depending upon taskspecificity and complexity [41,42].

The task was displayed and controlled on a laptop PC with aviewing distance of 70 cm to the subject. Each number was shownat the center of the screen in Arabic notation for 3 s. Subjects wereasked to sum the two presented numbers (shown sequentially),then were given 2 s (blank page) for retention followed bya multiple choice menu that displayed the possible answers. Thesubjects were required to click on the correct answer using themouse left button, with the minimum possible finger movement.

In total, 42 addition problems were displayed in the experi-ment, in seven difficulty levels, each level lasting for 2 minutes.Each task duration was chosen short (2 minutes) and was followedby a short rest period (30 seconds) to ensure that performingthe task does not impose fatigue on the subjects [43]. Thedifficulty level was manipulated by varying the n-digit numbersused and carries required to calculate the addition. The possibleanswers given in the multiple choices were selected carefully to bevery close to the correct answer (e.g. with forgotten carry ifneeded or inappropriate carry) to prevent the subjects fromguessing the answer and ensure they carried out the calculation(i.e. were engaged in the task). The given time for each additionproblem in all the levels was the same, to impose a greater mentaltask load on the subject when performing the more difficultaddition.

There was also a time limit for answering each addition taskand feedback on the difficulty level, in which the subjects rated thetask difficulty experienced (subjective ratings) for the given

P. Zarjam et al. / Computers in Biology and Medicine 43 (2013) 2186–21952188

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addition task. The experiment was run in order starting from veryeasy (very low level) to extremely difficult (extremely high level).The task detail is shown in Table 1.

To keep the chance of artifact recording to a minimum, theexperiment was performed under controlled conditions. For instance,the subjects were required to avoid blinking as much as possible toreduce the chance of excess ocular (Electrooculography/EOG) artefactduring recording. Besides, they were asked to refrain from anyunnecessary physical movements and their hand was placed in afixed position, where they could still make slight finger movementsin response to the correct answer on the mouse. This was tominimize any muscle movement artifact due to Electromyogram(EMG) activity during the recording. The subjects were given 30 srest between each two levels, when they could move or blink.

3.2. Subjects and data recording

EEG signals were acquired from twelve healthy male volunteersengaged in postgraduate study who completed the experiment.Their age ranged from 24 to 30 years, all were right-handedand had normal or corrected to normal eye-sight. They gavewritten informed consent, in accordance with human researchethics guidelines. The subjects were required to refrain fromtaking alcohol and caffeine 12 h prior to the experiment. Theexperiment was conducted in one session and lasted about 15 min.

An active two acquisition system [44] was used to record thesubjects' EEG signals. The recordings were conducted at the ATPLaboratory of National ICT Australia in Sydney. The experiment wasconducted under controlled conditions in an electrically isolated lab,with a minimum distance of 5 m from power sources to theexperiment desk and under natural illumination. Each recordingcontained 32 EEG channels mounted in an elastic cap, according tothe extended international 10–20 system. A linked earlobe referencewas used and impedance was kept under 5 kΩ. The EEG signals werepassed through a band-pass filter with cut-off frequencies of 0.1–100 Hz and were recorded at a 256 Hz sampling rate.

4. Methods

4.1. Preprocessing

The acquired EEG signals were first visually inspected andsegments contaminated with EMG and EOG artifacts were removedto obtain artifact-free segments.1 To extract features, the EEG signalswere segmented using a rectangular window of length T¼5 s. TheDC level of the segments was also removed and these segments wereband-pass filtered in the frequency band of 0.5–30 Hz (as the EEGsignals generally do not have many useful frequency components

above 30 Hz [45]). Finally, the EEG segments were re-sampled atfs¼64 Hz to reduce the computational load of the feature extractionalgorithms. Hereafter, the EEG segment under analysis is denoted byx½n�, which contains N¼ T � f s ¼ 320 samples. There was no overlapbetween the successive EEG segments.

4.2. EEG source localization

To narrow down the number of channels under study andselect optimal discriminatory channels, we applied a sourcelocalization technique for this experiment. Source localizationcan be utilized to estimate the localization and distribution ofelectrical events in brain disorders [46]. There are various algo-rithms for EEG source localization, among which cortical sourceimaging using a minimum norm estimate is one of the mostcommon [47]. We used the eConnectome software developedat Minnesota University [48] to apply this method to pick outthe channels which make the most contribution in discriminatingthe imposed task load, out of 32 channels recorded for eachsubject.

4.3. Feature extraction

We calculated four entropy features in Section 2; i.e. HSH ;HTS;

HETS and HRE using Eqs. (4)–(7) for each EEG segment. The EEGsegments were decomposed into three levels (scales) using aDaubechies-4 mother wavelet (due to its smoothing property whichmakes it more suitable to detect variations in the EEG signals [33]).13 artifact-free EEG segments of each channel were considered in theanalysis, for each task level and each subject, as the total artifact-freetask duration was at least 65 s for each load level and each subject.For each extracted feature, the dimension was C (i.e. the number ofchannels fromwhich a particular feature is extracted), correspondingto one entropy calculation from the 3rd level (the delta frequencyband) for each of C channels. Due to the sampling frequency (down-sampled to f s ¼ 64 Hz), the level (scale) parameter jwas chosen to be3 resulting in 3 wavelet scales, corresponding to the EEG frequencybands, as represented in Table 2.

4.4. Classification

To gauge the performance of the entropic features in classifyingdifferent load levels, we applied the extracted features from theselected channels into an Artificial Neural Network (ANN) classi-fier. We selected a multi-layer perceptron ANN, with a first hiddenlayer of 20 neurons, a second hidden layer of 14 neurons and anoutput layer of 7 neurons corresponding to 7 load levels, based onexperimental results. We applied leave-one-out cross-validationfor training and testing data (using one subject as the validationdata, and the remaining eleven subjects as the training data foreach fold), in a subject-independent manner. These selectedchannels for each feature were then input to an ANN classifier ina multi-channel arrangement. The benefit of the multi-channelapproach is to determine precisely the set of brain areasgenerating the EEG signal which cannot be directly observed froma single channel, because of electrical diffusion in the physiologicaltissues [49].

4.5. Synchronization

To measure the degree of synchronization among the neuralensembles participating in neural responses to different task loadlevels, we calculated the strength of phase synchrony between theinvolved EEG signals, in a pair-wise manner. The results are givenin the next section.

Table 1Details of the task design for all seven induced levels. In each task level, 6 additionswere presented.

Task level Number of digits Example

Very low (L1) 1 and 2 digit numbers 54þ5Low (L2) 1 and 2 digit numbers with 1 carry 73þ9Medium (L3) 2 digit numbers with 1 carry 47þ62Medium-high (L4) 2 digit numbers with 2 carries 69þ75High (L5) 2 and 3 digit numbers with 1 carry 477þ31Very high (L6) 2 and 3 digit numbers with 2 carries 347þ69Extremely high (L7) 3 digit numbers with 3 carries 893þ488

1 Within 1673 segments of recording across twelve subjects, 60 segments(3.6%) were removed, due to artefact contamination.

P. Zarjam et al. / Computers in Biology and Medicine 43 (2013) 2186–2195 2189

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5. Results

The source localization analysis demonstrated that the highestdistributions of electrical activities are mainly estimated in thefrontal and occipital regions of the brain in all the task difficultylevels, across all twelve subjects. For illustration purposes, thebrain map results for two levels of task difficulty for subject 1 areshown in Fig. 1(a) and (b). As seen, the frontal and occipital regionsexhibited notable cortical activation compared to other regions ofthe brain. However, these cortical activations deepened andwidened as the load level increased ((b) compared to (a)).We also carried out the source localization analysis for othersubjects. It was observed that there are individual differences inthe brain region activations but most of them exhibit frontal lobeactivation to some extent. We took this result, together withprevious findings (i.e. the frontal lobes have a close relationshipwith working memory), and hence we only considered EEGchannels located in this region (i.e. the 13 frontal channels; Fp1,AF3, F7, F3, FC1, FC5 FC6, FC2, F4, F8, AF4, Fp2, and Fz) to narrowdown the number of channels under study, for further analysis.

Spatial analysis of the selected region was also conducted by theextracted entropic-features and multichannel phase synchronizationto observe the effects of task difficulty variations. Spatial heat mapsof one entropy feature values; namely HSH for load levels L2 and L7for subject 1 are displayed in Fig. 1(c) and (d). As seen, as the loadlevel increased, the entropy variations rose and deepened in thesame activated regions (including the frontal region which is of ourinterest). Heat maps of multichannel phase synchrony measure; rvalues also showed the synchronization differences deepened withthe increased load level, as illustrated in Fig. 1(e) and (f).

The effect of increasing the load level on the EEG waveletcoefficient distributions was also observed. The example normal-ized histograms shown in Fig. 2 demonstrate that as memory loadincreased (towards L7), the coefficient distributions exhibiteda stronger peak, suggested a more coordinated neural response,than for the lower load levels. Correspondingly, the entropiescalculated from these normalized histograms decreased withincreasing load level. Similar effects were observed for mostfrontal channels across all subjects.

In terms of the variations with the load level, the four entropicfeatures; HSH ;HTS;HETS and HRE demonstrated a decreasing trendas the task load increased in most of the channels of interest(i.e. in the frontal lobes). For instance, the extracted HSH values forchannel F7 of subject 1 for three load levels are HSHðL1Þ ¼ 0:38,HSHðL4Þ ¼ 0:30 and HSHðL7Þ ¼ 0:28: For illustration purposes, Fig. 3shows the median of extracted ðHSHÞ, from three frontal channelsof subject 1. As seen, the extracted median value declines as theload level increases. The fact that there is no overlap between thevalues of the features extracted from different load levels suggeststhat these features are capable to distinguish different load levelswell.

To examine the effectiveness of the extracted features in dis-criminating the load levels statistically, across all subjects, we usedKruskal–Wallis statistical test. This test examines three or moreindependent groups; it is a non-parametric method for one-wayanalysis of variance and is not affected by variations in small portionsof the data. Performing the test examines the hypothesis that allsamples were taken from identical populations and is especiallysensitive to differences in central tendency [50]. This statisticalanalysis of the four extracted features indicated that all the channelsin the frontal lobes returned small p-values (lower than 0.01 level).For instance, the p-values for the extracted HSH from the frontalchannels using Kruskal–Wallis test are as follows: Fp1; p¼1.144e-16,AF3; p¼7.253e-17, F7; p¼2.613e-14, F3; p¼7.253e-17, FC1; p¼1.167e-15, FC5; p¼7.253e-17, FC6; p¼7.253e-17, FC2; p¼8.329e-17,F4; p¼7.383e-17, F8; p¼7.253e-17, AF4; p¼7.253e-17, Fp2; p¼9.117e-17, Fz; p¼7.253e-17. This reveals a strong statistical signifi-cance in the extracted features from the frontal EEG channels interms of differentiating the seven load levels imposed, across twelvesubjects.

We also varied the index 0oqr1 in Eqs. (5)–(7) to find theoptimal value of q for the purpose of the load discrimination onour database (in terms of classification accuracy). Results showedthe extracted medians were able to distinguish the seven taskloads better with q closer to 1, as it consistently reveals adecreasing median with increasing task load. Therefore, we choseq¼ 0:9 in our further analysis, when applying HTS;HETS and HRE .

To investigate the degree of the synchronization among thefrontal EEG channels under study through different load levels, wecalculated their multichannel phase synchrony measure ðrÞ usingEq. (9). Fig. 4 shows the median of the calculated r for subject 1. Asseen, the median of r increases in many load levels, as the loadlevel increases, indicating growing PLVs between all pair-wisecombinations of the EEG channels. In another word, this demon-strates more synchronization among the channels as the loadincreases.

Fig. 1(e) and (f) also displayed the calculated synchrony forsubject 1, as a spatial heat map. As seen, as the load level increases,the r increases2 (i.e. close to “1”), clearly in the left region of thebrain and more importantly, on the frontal lobes (mainly the leftlobe), which are of interest in this study. The median of r for levelsL1 to L7 for this subject were: 0.7981, 0.815, 0.852, 0.870, 0.891,0.912, and 0.921, as illustrated in Fig. 4. Analysis of r across alltwelve subjects exhibited large variability within subjects, butthe increasing trend of synchronization with the load level wasalmost preserved among all the load levels except levels 5 and 6,which decreased slightly. The medians of r for levels L1 to L7across all subjects were: 0.915, 0.927, 0.940, 0.947, 0.942, 0.934,and 0.9485, respectively.

To further narrow down the number of channels under study,we considered the frontal lobe channels that revealed a consistentdeclining trend for all the extracted entropy features, across allsubjects (i.e. for a given feature the median of the values of thatfeature for seven load levels, across all subjects was found and thechannels that revealed a consistent declining median were cho-sen). The channels that showed the same trend across all subjectswere ultimately selected, that is channels; Fp1, F7, F3, FC5, FC6,FC2, and AF4.

The performance of each feature was evaluated separately asfollows. For a given feature, e.g. HSH , we calculated the value of thefeature for each of the 7 channels mentioned above, resulting ina 1� 7 vector FVSH (with feature dimension; C¼7). The vector wasthen input to the ANN-based classifier explained in Section 4.4.

Table 2EEG frequency bands approximately corresponding to each wavelet scale,f s ¼ 64 Hz.

Waveletscale j

Component Freq.range ðHzÞ

No. ofcoefficients ðMÞ

EEG freq.band

1 Detail d1½i� 16–32 160 BetaApproximatea1½i�

0–16 160

2 Detail d2½i� 8–16 80 AlphaApproximatea2½i�

0–8 80

3 Detail d3½i� 4–8 40 ThetaApproximatea3½i�

0–4 40 Delta

2 Note that the scale has been normalized, so that “0” reflects no difference and“1” reflects the maximum difference.

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Fig. 1. Source maps of load levels (a) L2 and (b) L7 for subject 1. Clearly, both load levels influence the similar regions more or less but the activation seems to increase as theload level increased. Note that the scale has been normalized; so that “0” reflects no difference to the background color and “1” reflects the maximum difference. Heat mapsof entropy feature values HSH for load levels (c) L2 and (d) L7 for subject 1, in each case with the entropy features for load level L1 subtracted to emphasize the variation withload level with the frontal channels shown at the top, scaled to the range [0, 1]. Heat maps of multichannel phase synchrony measure r values for load levels (e) L2 and (f) L7for subject 1, scaled to the range [0, 1], calculated for four regions. As seen, the contrast between L7 and L1 (f) is deeper and wider compared to L2 and L1 (e), indicating moresignificant difference between the two load levels as the task load increases.

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The classification results are summarized in Table 3. Since, thedelta band was the main band to exhibit more selected channelsand higher accuracy rates for all the extracted features over allsubjects, we only reported the classification accuracy results inthis frequency band.

To compare the accuracy of our EEG-based method withcommonly employed existing methods for measuring workload;such as performance scores, reaction time, and self-assessment, wecollected the reaction times, accuracy of responses (performance)and self-assessment data to each addition task for each subject.

The results showed that as the task difficulty increased, thereaction times rose, mostly from load level 2 onwards for eachsubject. However, eleven out of twelve participants experienced alonger reaction time at the beginning of the experiment, i.e.for level 1 compared with level 2. This might be due to the timethese subjects needed to adapt to the experimental procedure.It was also observed that the reaction time dramatically increasesin most of the subjects in load level 5, the first high load level,in which two and three-digit numbers with one carry are firstintroduced. Fig. 5 displays the averaged reaction times for eachtask difficulty level over twelve subjects. As seen, the averagereaction time increases consistently as the task difficulty increasesand also the reaction time notably increases in load level 7,the highest load level imposed. Analysis of the collected accuracyof responses across twelve subjects showed that from load level2 onwards, accuracy percentage decreased consistently, as task

difficulty increased, as shown in Fig. 6. In order to validate theaccuracy of responses statistically, we executed Friedman's test.The designed task loads were considered as columns and theresponses as rows for twelve subjects. This ranked the givenresponses according to load levels for all subjects, as follows:the mean-ratings of 4.91, 5.54, 4.95, 4.58, 3.87, 2.70, and 1.41, withp¼ 2:44e�7.

We also asked the subjects to rate the difficulty experienced foreach addition task on a 7-point scale, on which 1 denoted theeasiest task and 7 the extremely difficult task (subjective ratingsor self-assessment), after each addition task (load level) con-ducted. Analysis of the results showed our subjects were notcapable of accurately rating their experienced mental load (self-ratings) and tended to under-rate the task difficulty experienced,in most cases. The self-assessment results for levels L1 to L7, res-pectively, are 48.61%, 51.39% 51.39%, 11.11%, 18.06%, 6.94%, and 25%.

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.080

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

Wavelet coefficient magnitude

Den

sity

L1L4L7

H(L1)=5.32H(L4)=4.12H(L7)=3.33

Fig. 2. Example normalized histograms (empirical probability mass functions) ofa3½i� for three load levels; L1, L4 and L7 from channel F7 of subject 2. As load levelincreases, higher densities are increasingly concentrated among a narrower rangeof wavelet coefficient magnitudes, producing lower uncertainty (entropy). Theextracted HSH values for the same channel are also displayed.

Fig. 3. The extracted Shannon entropy ðHSH Þ for channels Fp1, F7 and F3 of subject 1, with the load levels in scale 3. On each box, the red mark is the median; the edges of thebox are the 25th and the 75th percentiles.

1 2 3 4 5 6 70

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Load levels

r

Fig. 4. The median of multichannel phase synchrony measure r versus task difficultyin the frontal channels (Fp1, AF3, F7, F3, FC1, FC5, FC6, FC2, F4, F8, AF4, Fp2)for subject 1. The synchronization value increases consistently as the load levelgrows. Note that r is averaged across the frontal channels for each load level.

Table 3Classification accuracy of the four entropic measures using leave-one-out technique(q¼ 0:9, for Renyi, Tsallis, and Tsallis–Escort entropies) extracted from the deltaband, across twelve subjects.

Channels Feature Accuracy%

Frontal Fp1, F7, F3, FC5, FC6, FC2, and AF4 HSH 98.35HTS 98.44HETS 92.21HRE 89.74

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6. Discussion

Our objective was to investigate the usefulness of a new featureset namely; the wavelet-entropic features, in classifying workingmemory load (cognitive workload) on a fine scale. The four entropicmeasures; that is Shannon, Tsallis, Escort–Tsallis and Renyi entropieswere calculated from the wavelet coefficients of the EEG signals todistinguish the memory load imposed during an arithmetic task.Wavelet coefficients have shown their capability for EEG signalclassification in pathological diagnoses previously [34,35], but hadnot been used in the area of working memory load or mental taskclassification, prior to this study. On the other hand, wavelet analysisitself has proven to be one of the most suitable tools for processing ofthe EEG signals, as it does not assume the signals are stationary andpresents an optimal time-frequency resolution for analysis of the EEGsignals which are generally of low frequency [51], and have a hightemporal resolution [14].

We deployed a source localization technique primarily to findthe subset of EEG channels which make the most contribution inthe memory load discrimination. The source localization resultsassisted us in focusing on the frontal lobes of the brain which werethe most influenced by the task load. This supports the previousresults that the frontal lobes have a close relationship with

attention and working memory [52,53]. The extracted wavelet-entropic features from the selected channels in the frontal chan-nels not only showed high classification accuracy, but also wereable to represent the behavior of the brain when dealing withvarying memory load, due to their entropic nature. Statisticalanalysis of the extracted features using a Kruskal–Wallis test alsoconfirmed their strong statistical significance in differentiating theseven task load levels imposed, through the selected EEG chan-nels, across twelve subjects. Five levels of load are the highestnumber of discrete task load levels imposed to date [26,27],and the results herein advance this to seven levels.

A consistent decline in the medians of the entropic featureswas seen as the task load increased, revealing that the degree ofthe signal's disorder decreases as the task difficulty and workingmemory load imposed increases. Stated differently, decreasingdisorder of the signal represents that the degree of synchrony ofthe neural groups participated in the neural responses, grows.In general, more order indicates a higher degree of synchrony ofthe cell groups contributed in neural responses [32]. Having foundthis trend in the medians of the entropic features encouraged us tofollow our intuition with the trend of the synchrony of the neuralresponses. Therefore, we examined the strength of phase syn-chrony between the EEG channels using multi-channel phasesynchrony. Here, we demonstrated that as the load level increases,the synchronization between the EEG channels increases.This seems to confirm the changing order or dynamics of thebrain when performing a task with different load or difficultylevels. This is also in line with previous studies which have shownthat the increasing workload is reflected by more activity andmostly in the frontal lobes of the brain [26,54].

The primarily source localization results not only selected theaffected regions of the brain by the task load, but also demonstratedthat when applying different load levels on the brain,the influence of the higher load was deeper and wider in the frontallobes of the brain, indicating higher and deeper activation of theunderlying neurons. On the other hand, this may reveal that the brainbehaves in a more regular or organized manner when dealing withmore difficult tasks or higher loads. The source localization resultswere also supported by the synchronization results, selecting the leftfrontal lobe as the most effective region of the brain by higher loadlevels, reflecting more order and synchronization at the same time.The left frontal region has been recognized as an important area to beactivated during mental calculation, previously [47].

The observed decline in the medians of the entropic featuresacross all subject assisted us to further narrow down the frontalEEG channels, under study (i.e. from 13 to 7 channels). This showsa smaller number of EEG channels may be needed for futuresimilar work. This has implications for real-time applications andthe kind of EEG device used and setup cost. The extended benefitof applying these features includes their computational efficiency(relative to using non-linear dynamical features) and their relativefreedom from parameter tuning, which can be highly application-dependent (for non-linear dynamical features) [32].

To compare our classification results with previous EEG-basedstudies, we replicated the classification methodology used in [9],with the reported 99% classification accuracy for two load levels(for eight subjects) and 88% for four load levels (for eight subjects).Using the same feature set (power spectral density estimationof different EEG frequency bands), channels, and window-length,we achieved 83% classification accuracy for seven load levelsin the delta frequency, on our database. This is while; ourextracted features achieve higher discrimination accuracy for theselected frontal channels across the seven load levels. The highestclassification rate was achieved by Shannon and Tsallis entropies(98% across twelve subjects), in a multi-channel and subject-independent manner.

3

3.5

4

4.5

5

1 2 3 4 5 6 7

Task difficulty

Rea

ctio

n tim

e (m

s)

Fig. 5. Averaged reaction time versus task difficulty across twelve subjects.

Fig. 6. Response accuracy versus task difficulty averaged over twelve subjects.

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The frequency band investigation revealed that the delta bandcarries sufficient information for our task load discrimination (i.e. taskdemand information), including more selected channels for the fourmeasures in our study. This can be compared with the alpha and thetabands, in which just a couple of channels exhibited a consistent trendacross load levels for all subjects, and they showed poor classificationaccuracy among all seven load levels. This is in line with relatively fewprevious studies showing that the delta activity could be an indicatorof attention during some mental tasks, so that by increasing taskdemands, the participant's attention to the task and also the deltaband activity increases [41,55]. The delta band has often beendisregarded due to the chance of artifact contamination in manycognitive studies. We emphasize that in this study, the task wasconducted under controlled conditions, DC level and artifact-contaminated segments were removed and only artifact-free seg-ments were studied.

Analysis of recorded reaction time indicated that as the taskdifficulty increased, the average reaction time of subjects increased.The performance score results also showed that the increasing taskdifficulty was accompanied by degraded performance accuracy,as expected. Although, these measures could act as indicators ofmemory load, they can only be measured in a post-hoc manner andare not available as an on-line, continuous measurement during theprogress of the cognitive task. Besides, analysis of the subjectiveratings results revealed that our subjects were not capable of ratingtheir experienced mental load, well. On the other hand, EEG-derivedindicators show good potential for tracking cognitive workload innear-real time, in an on-line and continuous manner, with highaccuracy.

Limitations of this experiment included the order of the taskpresentation for the different task difficulty levels, which was notrandomized to avoid any possible order effect, and the highlycontrolled conditions subjects were placed under while perform-ing the task (e.g. to sit still and refrain from body movement andexcessive blink). However, it has been suggested that randomizingthe order of task presentation may reduce response order effectbut does not eliminate it, completely [56]. The setup was also longand may impose some slight physical fatigue in subjects prior tothe start of the experiment. We acknowledge this method shouldbe further validated on a larger database with more subjects,in order to generalize the method as a subject-independenttechnique. We also recognize the effect of existing volume con-duction between EEG channels, in underlying source configurationwhich has not been investigated here.

Our future work includes collection of a bigger database, andinvestigating the applicability the proposed features for other cogni-tive tasks. We would also like to investigate the multi-channelsynchronization method deeper and consider the volume conductioneffect when using the source localization method. There is also scopefor introducing further signal pre-processing, towards improving thecontrolled conditions of the tasks for more realistic environments.

Conflict of interest statement

None.

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