Spatio-temporal transition of EEG activities during insightoccurrence

Ryo Kondo (PY)1, Tomohiro Shibata1, Naoya Oosugi2, and Kazushi Ikeda1

1 Graduate School of Information Science, Nara Institute of Science and Technology2 RIKEN-BSI / The University of TokyoE-mail: {r-kondo,tom,kazushi}@is.naist.jp

Abstract— Insight is a method for solving intel-lectual problems without any explicit algorithm. Theanalyses in the literature identified the areas in thebrain that are related to insight but they have notelucidated the temporal structure of insight due tolow time-precision. This study clarified the spatio-temporal transition of insight-related information inEEG activities, where the amount of information wasquantified by the performance of a decoder predictinginsight occurence and a dimension-reduction methodis applied for extracting the spatio-temporal structure.The results implies that the origin of insight is O2 ininternational 10-20 system.

Keywords—Insight, EEG, Decoding

1 IntroductionInsight is characterized as a method for human to

solve intellectual problems suddenly without any ex-plicit algorithm and it has been reported that activ-ities in the anterior cingulate cortex and prefrontalcortex increase when insight occurs [1, 2, 3]. Thisstudy aims at clarifying the spatio-temporal transitionof Electroencephalography (EEG) activities during in-sight occurrence using decoding methods.

The performance of a decoder can measure theamount of information contained in its input sig-nals [4]. For example, if a decoder of EEG signalsat a specific channel in a specific time-window can dis-criminate an occurrence of insight from an algorithmicmethod, the EEG signals can be said to have enoughinformation on insight. Hence, we consider the timecourse of channels that show high prediction perfor-mance in single channel decoding.

Applying dimension-reduction methods to the per-formance matrix, we can visualize the spatio-temporaltransition of EEG activities related to insight. Theresults implies that the origin of insight is O2 in inter-national 10-20 system.

2 Material and Methods2.1 Experimental Setup

Eleven healthy right-handed participants (age 19–35, three males) participated in the study. Allwere graduate/undergrad students and had normal orcorrected-to-normal vision. Subjects were paid fortheir participation. The experiment was conductedwith the approval of the Research Ethics Committeein NAIST, and written informed consent was given byall participants.

Figure 1: The flow of the anagram test in the experi-ment.

The participants were seated and faced to an LCD ina shielded room. Their task was anagram test of En-glish words with four letters on the display. The par-ticipants were instructed to solve anagram test, push abutton when they completed it, and orally report howthey solved. Figure 1 shows the flow of the task in onetrial. One block consists of twenty trials followed byan interval for rest and ten blocks were conducted.

2.2 Data Acquisition and PreprocessingEEG was acquired at 200 Hz sampling frequency

from fifteen scalp sites (Fp1, Fp2, F3, F4, C3, C4, P3,P4, T3, T4, Fz, Cz, Pz, O1 and O2 in international10-20 system) using electrodes. The EEG signalswere amplified and digitized using Polymate AP1132(TEAC, Japan) and bandpass-filtered between 7 and31 Hz (fourth-order Butterworth filter) using Matlab(The Mathworks, USA).

2.3 Decoding and AUC AnalysisWe constructed a decoder that classifies the EEG

signals to one of the two classes, insight or not. The in-put of a decoder is the set of single-channel single-time-window band powers (alpha waves, 7–13 Hz;, beta1waves, 14–20 Hz; beta2 waves, 21–30 Hz), where atime-window is 750–500 msec, 700–450 msec, . . ., or250-0 msec before the button-push in a trial.

The performance of a decoder was measured by eval-uating the area under the specificity-sensitivity curve(AUC), obtained by varying the detection thresholdwith the five-folded cross validation. Note that theAUC is a non-parametric statistics that estimates theprobability that a randomly chosen target has a higherfeature value than a randomly chosen non-target [5].

The 21st Annual Conference of the Japanese Neural Network Society (December, 2011)

[P3-12]

Hence, we can regard the score as a quantity of infor-mation on insight included in the channel and time-window.

2.4 Spatio-Temporal TransitionWe extracted the spatio-temporal transition of brain

activities that appear in common during insight pro-cess. To do so, we apply the singular-value decom-position (SVD) method to the AUC matrix, each rowof which consists of a participant’s class separation(CS) scores, defined as CS = AUC − 0.5. Note thatthe principal singular vector expresses the most typicalactivation in insight.

3 Results3.1 Behavioral Results

Two participants reported that they solved theproblem by insight at rate of only 1.2–1.4%, whosedata were removed since the rates were too small tomake classifiers. The others have 20–50% insight rate.

Note that we measured the participants’ responsetime when they were asked to push a button, whichwere between 430–510 msec.

3.2 AUC Values for Single ChannelsFigure 2 shows the AUC values of single-channel

decoders for each participant. The values are signifi-cantly higher than the chance level, 0.5.

Fp1 Fp2 F3 F4 C3 C4 P3 P4 T3 T4 Fz Cz Pz O1 O20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

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channels

AUC

Figure 2: The AUC values of single-channel decodersfor each participant.

3.3 Spatio-Temporal TransitionFigure 3 shows the principal spatio-temporal tran-

sition of activities, where topographical maps of CSscores in three disjoint temporal intervals are depicted.O2 was the are firstly activated as seen in the firsttime-window (c). Then, the activation was widelyspread to centro-parietal areas in the second time-window (b). Finally, the brain activities have littlerelationship to insight during the response time (a).

4 DiscussionsThe spatio-temporal transition during insight occur-

rence agrees in part with the previous studies that in-sight is related to the activity in the right hemisphereanterior superior temporal gyrus [1] or in the parieto-occipital and centro-temporal areas [2]. However, the

Figure 3: Spatio-temporal transition of activities.

relationship to the anterior cingulate cortex or the pre-frontal cortex [3] is not clear. Moreover, the effective-ness of each band discussed in [1, 2] is not clear eithersince we constructed the classifiers with band powersbeing input signals.

5 ConclusionsThis study clarified the spatio-temporal transition

of insight-related information in EEG activities by re-garding the CS scores of insight as the amount of infor-mation on insight and by applying the SVD method forextracting the common feature through participants.The results implies that the activities of insight areoriginated at O2 in international 10-20 system andspread to centro-parietal areas during insight occur-rence.

References[1] Jung-Beeman, M. et al. (2004). Neural activity

when people solve verbal problems with insight,PLoS Biology, 2, 0500–0510.

[2] Sheth, B. R., et al. (2009). Posterior beta and an-terior gamma oscillations predict cognitive insight,J. Cognitive Neuroscience, 21, 1269–1279.

[3] Dietrich, A., & Kanso, R. (2010). A review of EEG,ERP, and neuroimaging studies of creativity andinsight, Psychological Bulletin, 136, 822–848.

[4] Oosugi, N., et al. (2010). A BCI study on the detec-tion of insight occurrence from EEG, IEICE Tech-nical Report, NC2010-59.

[5] Fawcett, T. (2006). An introduction to ROC anal-ysis, Pattern Recognit. Lett., 27, 861–874.