EVALUATION OF EMOTIONAL COMPONENTS TO IMPROVE … · control the BCI in a ective modulation. In the present work, a passive-BCI able to co-work with a speci c reactive-BCI, is evaluated

EVALUATION OF EMOTIONAL COMPONENTS TO IMPROVE SSVEP-BCI

Anibal Cotrina∗, Alessandro B. Benevides∗, Andre Ferreira∗, Teodiano Bastos∗, MariaL. R. Menezes†, Carlos E. Pereira†, Javier Castillo‡

∗Post-Graduate Program of Electrical Engineering, Federal University of Espirito Santo (UFES),Av. Fernando Ferrari 514, Vitoria, Brazil

†Post-Graduate Program of Electrical Engineering, Federal University of Rio Grande do Sul (UFRGS),Av. Osvaldo Aranha 103, Porto Alegre-RS, Brazil

‡Post-Graduate Program of Electronics Engineering, University del Valle (UNIVALLE),Av. Paso Ancho 1300, Cali, Colombia

Emails: [email protected], [email protected], [email protected],[email protected], [email protected], [email protected],

[email protected]

Abstract— Brain-computer interface (BCI) provides a direct connection between the user’s brain signalsand a computer, generating an alternative channel of communication that does not involve the traditional wayas muscles and nerves. Recent decades have seen BCI applications as a novel and promising new channel ofcommunication, control and entertainment for disabled and healthy people. However, BCI technology can beprone to errors due to the basic emotional state of the user: the performance of reactive and active BCIsdecreases when user becomes stressed or bored, for example. Passive-BCI is a recent approach that fuses BCItechnology with cognitive monitoring, providing valuable information about the user’s intentions, the situationalinterpretations and mainly the emotional state. In order to improve the accuracy of BCIs, subjects can performsimultaneous or sequential tasks typically used in two BCI approaches in a hybrid condition that combinesboth BCIs. In this work, a system composed of a passive-BCI co-working with a reactive-BCI, with the aimof improving the performance of the reactive-BCI is proposed. Thus the possibility of adjusting recognitioncharacteristics of SSVEP-BCIs using a passive-BCI output is evaluated.

Keywords— Emotional components, passive-BCI, reactive-BCI, SSVEP-based BCI, asymmetry index

1 Introduction

A Brain-Computer Interface (BCI) provides a di-rect connection between the user’s brain signalsand a computer, generating an alternative chan-nel of communication that does not involve thetraditional way as muscles and nerves (Wolpawet al., 2002). A BCI defines a new input modal-ity for human-machine interaction (HMI), whichcould substitute or add up to other input modal-ities like manual input. Distinct mental statescan be associated with physical actions, such assending the command “turn right” to a wheelchairrobot just imagining the movement of the righthand (Ferreira et al., 2010). Although presentingmany advantages, most current BCIs are highlysusceptible to emotional states experienced by itsusers, since emotions indicate what is importantand what you care about (Picard, 2010). However,the BCIs have the advantage of direct access tobrain activity, being able to provide meaningful in-formation about the user’s emotional state. Suchinformation may be used in two forms (Molinaet al., 2009): 1) Knowledge of the influence of emo-tional state in the patterns of brain activity allowsthe BCI to adapt their recognition algorithms sothat the user’s intent is still interpreted correctlydespite signal changes induced by the emotionalstate of the user. 2) The ability to correctly rec-ognize emotions in BCIs that can be used to pro-vide the user a more natural and intuitive way to

control the BCI in affective modulation. In thepresent work, a passive-BCI able to co-work witha specific reactive-BCI, is evaluated in order toimprove the performance of the BCI by evaluat-ing emotional components. Experimental resultsare shown and the proposal seems effective.

1.1 BCI categorization

According to the categorization proposed in(Zander and Kothe, 2011), active-BCIs have out-puts derived from brain activity, which is directlyand consciously controlled by the user, thereforebeing independent of external events (Wolpawet al., 1991); and reactive-BCIs have outputs de-rived from brain activity arising in reaction toexternal stimulation, which is indirectly modu-lated by the user (Muller, Celeste, Bastos andSarcinelli, 2010). Passive-BCIs have outputs de-rived from implicit information on the actual usermental state, which arises arbitrarily without thepurpose of voluntary control. The first two cate-gories derive their outputs for controlling an ap-plication and the last one derive its output to im-prove human-environment interaction or human-machine interaction.

1.2 Reactive-BCI based on SSVEP

An event related potential (ERP) used in manyBCI systems is the visual evoked response (VEP).

Anais do XX Congresso Brasileiro de Automática Belo Horizonte, MG, 20 a 24 de Setembro de 2014

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PASSIVE BCI(Emotion)

REACTIVE BCI(SSVEP) BCI command

FlickeringStimulus

BCI user Hybrid-BCI

Figure 1: Schematic overview of a passive-reactive Hybrid BCI.

This potential, occurring involuntarily in responseto a visual stimulus, can be measured over oc-cipital brain areas. Steady-State VEP (SSVEP)is a periodic response elicited by repetitive pre-sentation of a visual stimulus, with the samefundamental frequency as that of the flickeringstimulus as well as its harmonics (Middendorfet al., 2000), (Sutter, 1992), (Muller, Bastos andSarcinelli, 2010). In a typical SSVEP-based BCIsystem, multiple stimuli flickering at different fre-quencies are shown to the subject. The increasein the SSVEP amplitude can be detected in theelectroencephalographic (EEG) signal, which arefurther processed, classified and translated intocontrol commands (Wang et al., 2006), (Gaoet al., 2003), (Cheng et al., 2002), (Muller-Putzet al., 2005).

1.3 Passive-BCI based on Emotion Components

A Passive-BCI is a recent approach that fuses BCItechnology with cognitive monitoring, providingthe computer information about the user’s inten-tions, the situational interpretations and mainlythe emotional state. Emotions can be defined asa subjective, conscious experience characterizedprimarily by psycho-physiological expressions, bi-ological reactions, and mental state (Kleinginnaand Kleinginna, 1981). Affective computing stud-ies techniques that recognize, interpret, and pro-cess human emotions (Picard, 2003). Asymmetryof the frontal lobe, given by the variation of the al-pha band power of the EEG signals, is significantlyassociated with human emotional states; in which,high alpha band power in the right hemisphere isassociated to negative emotional states while highpower in the left hemisphere is associated withpositive emotional states (Davidson, 1992).

1.4 Hybrid BCI

In order to improve the accuracy of BCIs, subjectscan perform simultaneous or sequential tasks typ-ically used in two BCI approaches. A hybrid BCIis assembled by a collection of systems that worktogether to provide a communication pathway be-tween the human brain and a computer (machine).A hybrid BCI based on two different could com-bines active, reactive, and passive BCIs.

1.5 Assessment

Recently, a new perspective on BCI has emerged(Nijboer et al., 2009), which suggests that notonly voluntary self-regulated signals can be usedas input, but also involuntary signals might tellus something about the state of the BCI user (e.g.the emotional and cognitive state). It is assumedthat relevant features from these involuntary sig-nals (also referred to as passive signals) can beextracted and used to adapt the recognition al-gorithms of the BCI. In sum, the knowledge ofthe emotional state influence in brain activity pat-terns allows the BCI to adapt its recognition algo-rithms with the aim that the user intentions wouldbe interpreted efficiently.

In the present work, a passive-BCI monitorsemotional component of the BCI user with theaim improving a SSVEP-BCI performance is eval-uated. The increase of the SSVEP amplitude canbe detected in the EEG signals and translated intocontrol commands. However, stimuli flickeringcould cause a stress-related emotional state or lossof attention, as reported in (Muller, Bastos andSarcinelli, 2010). In order to accommodate thisissue, we propose a system in which passive-BCIco-works with a SSVEP-BCI, whose schematicoverview is shown in Figure 1. The SSVEP-BCIdetects the elicited evoked potential from EEGsignals registered at occipital electrodes. At thesame time, the passive-BCI identifies emotionalcomponents of user mental state from EEG signalson the frontal brain region. The system is thenswitched to a ”stress mode” when specific com-ponent of emotional state, like stress, is detectedand consequently the success rate of SSVEP de-creases. In this mode, the passive-BCI outputmodules the reactive-BCI characteristics aimingto maintain the success rate.

EEG signals of one subject were employed.Two flickering stimuli were used to evoke theSSVEP potential. Spectral density of signal iscomputed using Hilbert Transform. Two ways ofbecome the SSVEP more robust were evaluated:adjusting the amplitude response and adjustingthe frequency response. Asymmetry index com-puted of the alpha band from frontal electrodeswere used to evaluate the emotional state of thesubject.


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2 Methods

2.1 Subjects

Due to the preliminary aspects of this work, theevaluation was performed with only one volunteerwith no previous history of neurological or psy-chiatric disorder. The experiment was taken withthe understanding and written consent of the sub-ject, who gave informed consent. This study wasapproved by the research ethics committee of theFederal University of Espirito Santo (Brazil).

2.2 Stimulus

Two stimuli, emitted by two 5 × 7 LED arrange-ments flickering at 5.6 Hz and 6.4 Hz were dis-played simultaneously. The subject seated in frontof the SSVEP box and was asked to gaze on thetarget LED for 17 s after a beep tone, then askedto close his eyes for 5 s, ending the trial after a sec-ond beep tone. The EEG signal was recorded be-tween seconds 5 and 17 of the trial. Two sessionsof 10 trials were performed during the experiment.

2.3 Signal acquisition

BrainNet36 (BNT) was the device used for EEGacquisition with a cap of integrated wet electrodes.EEG signals from 19 electrodes positioned accord-ing to the international 10-20 system were regis-tered (Figure 2). The grounding electrode waspositioned on the subject forehead and the bi-auricular reference was adopted. The EEG wasacquired at a sampling rate of 200 Hz. BNT is adevice for clinical purposes that does not exportdata in on-line mode. Therefore, a TCP-IP basedsniffer programmed in ANSI C was developed toexport these data, allowing the on-line processing,which was performed on MATLAB.

Figure 2: International 10-20 system for electrodeplacement.

2.4 Preprocessing

Signals were filtered employing an elliptic band-pass (4 Hz - 50 Hz). Signals from O1 and O2 elec-trodes were used to verify the SSVEP responses;other channels were employed to perform common

average reference (CAR) spatial filtering, in orderto reduce the correlation between channels origi-nated by external noise. CAR filter is given by:

µCARi = µERi −

1

n

n∑j=1

µERj , (1)

where µCARi is the filtered signal and µERi is the

potential between the i-th electrode and the ref-erence electrode.

2.5 Spectral density of an analytical signal

In rhythm modulation-based BCIs, the input of aBCI system is the modulated brain rhythms withembedded control intentions. Brain rhythm mod-ulation is realized by executing task-related activi-ties, e.g., attending to one of several visual stimuli.Demodulation of brain rhythms can extract theembedded information, which will be convertedinto a control signal. The brain rhythm modu-lations could be sorted into the following threeclasses: power modulation, frequency modulation,and phase modulation. For a signal s(t), its an-alytical signal g(t) is a complex function definedas:

g(t) = s(t) + jŝ(t), (2)

where ŝ(t) is the Hilbert transform of s(t), definedas:

ŝ(t) =1

π

∫ ∞−∞

s(t)

t− τdτ . (3)

Due to the ŝ(t) have the same energy as s(t), en-ergy spectral density is given by:

P (f) =1

4Ĝ(f)Ĝ(f)∗, (4)

where G(f) is the Fourier transform of g(t) andĜ(f)∗ denotes the complex conjugate of G(f).The analytic signal has no power at negative fre-quencies.

2.6 Adjusting of Amplitude of the Response

The amplitude of the SSVEP response of the EEGsignals depends on the quantity of samples em-ployed to perform the FFT transform. Normal-ized amplitude spectrum is calculated by:

Pnorm(f) =P (f)∑P (f)

, (5)

where P (f) is spectral energy density of the ana-lytical signal.

∑(Pf) denotes a summation over

the total frequency points of a spectrum. The re-sponse becomes more robust when more samplesare considered.


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2.7 Adjusting the Frequency of the Response

The frequency corresponding to amplitude ofpeaks in the frequency domain is compared withstimuli flickering frequencies to determine whichstimulus was chosen by the subject. However, itis common that the frequency of the peak (fun-damental or harmonic) is slightly different to thestimulus frequency, or other peaks appear at fre-quency domain. To solve this problem, PowerSpectral Density Analysis (PSDA), which involvesprocessing in the frequency domain, was usedto perform automatic recognition of SSVEP re-sponses of the target stimulus.

If there is a peak in the same frequency ofthe stimulus, the error will be zero. If the error isnot zero, the ratio will be small if the amplitude ishigh, and at different frequencies, the error will besmall. In this case, fk and fh could be adjusted.The power spectral density analysis around thestimulus frequency is given by:

Sk =mP (fk)

m/2∑i=−m/2

P (fk + ifr)

, (6)

usually expressed in dB; m is number of samplesaround the stimulus frequency, and fr is the fre-quency resolution which depends on the Fouriertransformation. P (fk + ifr) is the power den-sity around the stimulus frequency. In this studym = 60 was considered.

So, given k-th stimulus frequency fk, thecloser peak response frequency fh, and the mag-nitude of the peak frequency P (fk), the followingratio of proportion was used:

Ratio =|fk − fh|P (fk)

. (7)

2.8 Asymmetry index

The index of asymmetry of alpha band can becomputed by comparing the power of contra-lateral frontal electrodes, in order to identify com-ponent of stress-related emotional states. Frontalcortex asymmetry has provided evidence thatgreater right frontal activity seems to be morehighly related to negative emotional states. Thisindex, that has a value between -1 and 1, canbe employed as a switch to shift the system tothe ”stress mode” (Davidson, 1992). The mostcommonly reported of the indexes is computed bysubtracting the left hemisphere alpha power (Plh)from the right hemisphere alpha power (Prh):

Assymetry =Plh − PrhPlh + Prh

,

where Plh and Prh were estimated by computingthe Power Spectral Density.

3 Results

As mentioned above, the subject was asked tochoose one specific target between two stimuliflickering at 5.6 Hz and 6.4 Hz. A particularmental state, such as stress, can affect the fre-quency or the amplitude of this potential. There-fore, a technique based on adjusting the numberof samples employed to perform the FFT trans-form and/or a technique based on the enlarge theratio of searching of the peak response to com-pensate the frequency and amplitude of evokedpotentials, respectively. Hence, elicited SSVEPpotential response and asymmetry index when thesubject was stimulated emotionally are presentedint his section.

3.1 Elicited SSVEP potential results

Hilbert transform was used to compute theSSVEP spectral responses shown in the Figures3 and 4.

(a)

(b)

Figure 3: Normalized amplitude spectra corre-sponding to different length. (a) For the stimu-lus flickering with 5.6 Hz. (b) For the stimulusflickering with 6.4 Hz.

Figures 3(a) and 3(b) show the normalizedamplitude spectra corresponding to four differentdata lengths. If the data length is n = 200 sam-ples, corresponding to 1 s of signal; then, the am-plitude of the response is weak. The response be-comes more robust when more samples are consid-ered. Thus, SSVEP response peak will be strongwhen n = 800 samples that corresponds to 4 s ofsignal. Hence, responses that were computed withfew data are affected with changes in the subjectmental states. In this sense, one way to maintain


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(a)

(b)

Figure 4: Normalized amplitude spectra SSVEPresponses of ten trials (gray) and their the average(black).(a) For the stimulus flickering with 5.6 Hz.(b) For the stimulus flickering with 6.4 Hz.

the SSVEP potential amplitude could be achievedthrough adjusting the data length of each trial.The number of samples increases the data and theprocessing time, so this assessment maintains thesuccess rate, but reduces the information transferrate (ITR). In practice, the length of the samplesare determined by a window function.

Figures 4(a) and 4(b) show the normalizedamplitude spectra of the SSVEP response of theelectrode O2 corresponding to ten trials (graycurves) and the average curve (black curve).The response to stimulus flickering with 5.6 Hzpresents weaker amplitude than its second har-monic frequency (11.2 Hz); concurrently the peakat 5.6 Hz is wider. In cases like this, thepeak detection using a threshold becomes ineffi-cient. Thus, adjusting of frequency of the responsemethod can be a good alternative. On the otherhand, for 6.4 Hz the SSVEP response presentspeaks at fundamental and second and third har-monic frequencies.

Topographic maps of all position of the sub-ject’s scalp is showed in the Figure: The two stim-ulation frequencies (5.6 Hz and 6.4 Hz) and thesecond (11.2 Hz and 12.8) and third (16.8 Hz and19.2 Hz) harmonics of each. Note that these mapspresent data derived with different frequencies. Itis clear from both stimulation frequencies the to-pographies show occipital activity characteristic ofSSVEP. Only the topography of second harmonicpresents occipital activity on the right side. Fi-nally, topography of the third harmonic presentsslight occipital activity.

(a) (b)

Figure 5: Topographic maps of SSVEP responseover six frequencies. (a) 5.6 Hz with their secondand third harmonics. (b) 6.4 Hz with their secondand third harmonics.

3.2 Asymmetry Index results

The alpha power of the contralateral electrodes F3and F4 was estimated by computing the PowerSpectral Density based on the modified peri-odogram. The absolute value of Fast FourierTransformation provides the amount of informa-tion contained at a given frequency, and the squareof the absolute value is considered the power of thesignal. In order to compute the asymmetry of al-pha band power at frontal lobe from contralateralelectrodes F3 and F4, One-minute signal was con-sidered to the analysis. One-second segments thetrial signal were taken into account to perform theanalysis; thus, for each trial, the CAR filtering,the wavelet frequency band decomposition, andthe frequency band power estimation were per-formed. Computing the asymmetry was realizedwhen subject was listening to unpleasant soundswhile he was asked to gaze at an SSVEP stimu-lus. Four types of sound stimuli were selected toelicit an emotional state: 1) Nothing, 2) ruler ona bottle, 3) dental drill, and 4) baby laughing. In(Kumar et al., 2012), stimuli (2) and 4) were ratedas one of the most unpleasant sounds and the ofthe least unpleasant sounds, respectively.

The box plot was used to show the distribu-tion of results (See Figure 6). It can be seen that,in all cases the value of the median of the indexwas negative. However, a clear difference betweenthe sounds 2) and 4) is showed. It is evident thatthe results shows that the sound 3) has the leastindex for the subject. It indicates that a stress re-


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lated emotional state was elicited on the subject,because high alpha band power in the right hemi-sphere is associated to negative emotional stateswhile high power in the left hemisphere is asso-ciated to positive emotional states. Finally, theeffect of the stimulus 4) was the same that the“stimulus nothing”.

Figure 6: boxplot with 90th percentile (10% and90%) of the results of asymmetry index comput-ing.

4 Conclusion

The method of recognizing the fundamental fre-quency of an SSVEP elicited response described inSections 2.6 and 2.7 can maintain the error rate byadjusting two parameters fk and fh, that deter-mine the window width around the stimulus fre-quency. Thus, it can be concluded that the search-ing limits of evoked potential peaks and the num-ber of samples used to compute the FFT transfor-mation can be adjusted to improve the search ofthe SSVEP potential’s frequency. Those resultsare promising because they show that passive-BCIs could improve or maintain the accuracy ratedespite of BCI user’s emotional states, such asstress. In the Section 2.8, although the assessmentreduces the information transfer rate, it maintainsthe error rate of the reactive-BCI. Since the asym-metry or energy in alpha band can be used toidentify emotional components of the BCI user,the next step in this work will be to integrate thepassive-BCI and the reactive-BCI showed in theFigure 1 in order to develop a more robust BCI.

The index is used to modulate the reactive-BCI characteristics by using two adjustable pa-rameters using a specific emotional componentsuch as asymmetry index: 1) the number of sam-ples to compute the FFT Transform and 2) thesearch range around the stimulus frequency at thespectral domain. Although the ITR decreases be-cause the first adjustment increases the samplesand the time between two trials, and the secondadjustment increases the SSVEP peak searching

time, this assessment could improve the interac-tion between the user and the reactive-BCI be-cause it maintains the success rate.

Alpha power has been found to be more re-liably related to task performance compared toother frequency bands, when the tasks comparedcarefully match on psychometric properties. Al-pha power asymmetry may be considered a gra-dient of power that exists between the two ho-mologous electrodes in the pair, with the slope ofthe gradient being towards the electrode with thegreatest amount of power in this frequency band.

The next step in this research will be to com-pute the asymmetry index and to propose a linearequation that ties in this index with SSVEP-basedBCI parameters. It is well known that BCIs, likeSSVEP-based BCIs, are not suitable for all users(Guger et al., 2012). The causes for this ineffi-ciency have not yet been satisfactorily described.Few studies exist that explicitly investigated thepredictive value of internal (user related) and ex-ternal (BCI related) factors on the BCI perfor-mance. The accuracy of SSVEP can be monitoredby a Reclassifier, which evaluate a number of con-secutive results. The Re-classifier is able to acti-vate a switch if the accuracy is not being recov-ered. In this case, an autonomous interface can beimplemented in order to take control of the ma-chine. Commands like “Stop the machine”, “Re-turn to previous stage” or “Return to the startingpoint” can be sent to a control system, as shownin the Figure 7.

Re-Classifier

AUTONOMOUSINTERFACE

Switch

HYBRID-BCI

Return

Wait

Stop

Call

Figure 7: Schematic overview human-machine in-terface composed by a passive-reactive hybrid BCIand an autonomous interface.

5 Acknowledgment

The authors would like to acknowledge the fi-nancial funding from FAPES/CNPq (Process53666038/2011), and thank CAPES agency forscholarship support.


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Documents

EVALUATION OF EMOTIONAL COMPONENTS TO IMPROVE … · control the BCI in a ective modulation. In the present work, a passive-BCI able to co-work with a speci c reactive-BCI, is evaluated