The Relationship between Attention Assessment and EEG Control

Shih-Chung Chen1+ , Ching-Kai Huang2, Jin-Fu Chen2, Shih-Bin Su2 1Institute of Electrical Engineering, Southern Taiwan University, Tainan, Taiwan

2Institute of Biomedical Engineering, Southern Taiwan University, Tainan, Taiwan

Abstract. Attention concentration is the indispensable basis for learning. We found that the students’ attention is reducing according to many teachers’ professional researches and experience sharing. The similar cases of attention decreasing can be found not only within students but also adults. Therefore, our research aims at designing an interactive game controlled by EEG based on brain computer interface (BCI) technology which is the core of attention assessment system. We hope that the subject’s attention concentration can be enhanced by attention training and assessment through BCI games. The BCI game of attention training and assessment system was developed with the help of LabVIEW and NeuroScan software. There are 10 subjects divided into two groups attending the attention training and assessment experiments and preliminary experimental results can prove that the feasibility of attention assessment system. The real time power spectrum analysis of acquired EEG data became stronger obviously among alpha wave (8-13Hz) and beta wave (14-30Hz) frequency range when the subject was playing BCI game.

Keywords: attention, EEG, BCI, LabVIEW

1. Introduction Reviewing the related brain wave literatures, the papers（Robertson & Garavan, 2004[1]; Fan et al.,

2005[2]）mentioned that the subject must keep attention when he/she is in an alert situation and the frontal lobe and parietal lobe of the human brain always appear obvious signal features generally. According to Rueda、Rothbart、McCandliss、Saccomanno and Posner’s research（2005）[3], we can understand the flexibility of human attention. The result of experiments proved that the brain wave signal near the frontal lobe and parietal lobe of the child who is preschool age and trained is more similar to the brain wave signal of the adult than the child who is preschool age but not trained in advance. The research of attention assessment shows that the efficiency of attention training can be improved by the acquired education, even with worse inborn gene factor. Besides, Posner and Rothbart（2007）[4] thought that the attention training can not only enhance attention but also extend the influence to the other cognitive functions, for example, intelligence. Posner and Raichle (1996）[5] also thought that attention training is a special repetitive practice concept with respect to some kind of specific cognitive function and make the efficiency of the subject’s attention better.

In recent years, many developed countries are studying EEG research hard. Many scientists hope that EEG can be applied to control home appliance and instruments. Our research team also implemented the LEGO page turner controlled by BCI (Brain Computer Interface) successfully. In the future, we believe that the better the new technologies are developed the more extensive EEG application will appear. There are several toys, games and education software designed based on EEG research in the market recently. This paper describes that we studied EEG signal variation between attention pre-training and post-training by playing BCI interactive games. In the mean time, the efficiency of attention enhancement was analyzed and assessed by using EEG signal feedback mechanism.

+ Corresponding author. Tel.:+886-6-2533131 ext 3335, fax:886-6-2151353 Email address:chung@mail.stut.edu.tw;shihbin.su@msa.hinet.net

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2012 2nd International Conference on Biomedical Engineering and Technology IPCBEE vol. 34 (2012) © (2012) IACSIT Press, Singapore

2. Material and Methods

2.1. Material The major software of the attention assessment system included Scan 4.3 developed by Neuroscan

company and LabVIEW 2009 graphic program language developed by National Instruments company showed in Fig. 1. The major hardware of the attention assessment system includes NuAmps EEG Amplifier showed in Fig. 2, EEG head cap and personal computer etc. In general, EEG measurement followed international 10-20 standard electrodes positions showed in Fig. 3.

Fig. 1: logos of Scan4.3 & LabVIEW software

Fig. 2: NuAmps – EEG Amplifier Fig. 3: international 10－20 standard

2.2. Methods The architecture of the attention training and assessment system can be divided into two major parts: (1)

part 1: Brain Computer Interface interactive game (the arm wrestle game controlled by the subject’s EEG) (2) part 2: EEG measurement system. These two parts are showed in Fig. 4 and Fig. 5 respectively.

Fig. 4: arm wrestle BCI interactive game controlled by EEG Fig. 5: EEG measurement system

2.2.1 BCI (Brain Computer Interface) interactive game Part 1: The arm wrestle game designed in the attention training and assessment system is showed in Fig.

4. The subject can see the competitor (an old man) on opposite side and a virtual arm representing the subject’s arm. In order to acquire stable and correct EEG signal of the subject, computer will count down 5 seconds in the beginning of the game so that the subject can relax himself/herself and pay attention to the competition before the game starts. There is an energy meter with the default value 50% on the left side of the screen shown in Fig. 4. It represents the energy intensity of the subject’s attention. If the subject’s attention intensity is stronger than the default threshold value then the value of energy meter will increase and vice versa. The subject must pay all attention to the dynamic changes on the screen when he/she plays the game with the help of his/her own EEG signal otherwise the subject might fail in the arm wrestle game.

2.2 .2 EEG measurement system Part 2: EEG measurement of the attention training and assessment System is showed in Fig. 5 including

the functions of data acquisition, signal processing, data analysis and data presentation. 28

2.2.2.1 Data acquisition (EEG amplifier, A/D conversion, data transmission) We integrated NuAmps, Scan 4.3 software, Graphic User Interface (GUI), LabVIEW and computer

successfully to finish data acquisition and transmission with the help of TCP/IP protocol programmed in LabVIEW language. When the arm wrestle game starts the arm of the old man on the screen will swing to attract the subject’s attention. Once the subject starts playing the game, NuAmps and Scan 4.3 software start to acquire the subject’s EEG signal and transmit data to computer through TCP/IP protocol for further data analysis and data presentation programmed in LabVIEW.

2.2.2.2 Signal processing and data analysis There are some common digital signal processing algorithms often used for EEG study by the

researchers, for example, filter theorems, Fast Fourier Transform (FFT), Wavelet Transform (WT), Bispectral Power Spectrum and Power Spectral Density (PSD) etc. We adopted some filter theorems, FFT (described as eq. (1) and eq. (2)) and power spectrum (PS, described as eq. (3)) algorithm to evaluate and analyze the subject’s EEG signal. Meanwhile the threshold value of power spectrum of alpha wave and beta wave included in the subject’s EEG signal was defined to justify whether the subject’s attention intensity is stronger than the old man cartoon on the screen. After repetitive comparison between the threshold value representing the default attention intensity and the amplitude of power spectrum of the subject’s EEG signal, the result of comparison can decide the intensity of the energy meter to increase or decrease in Fig. 4. Finally we can see the competition result between the subject and the old man cartoon.

dtetxfX tfj∫∞

∞−

⋅⋅−⋅= π2)()( (1)

t: time; f: frequency; x(t): source signal; x(f):the Fourier Transform of x(t); if x(f) includes real part and imaginary part, then x(f) can be expressed as equation (2)

( )[ ] [ ])(ImRe)( fXgifXalfX ×+= (2)

Power Spectrum (PS)

2)()(*

nsignalFFTsignalFFT ×=

(3)

*: conjugate; n: the number of all samples in the signal。

2.2.2.3 Data presentation The NeuroScan 4.3 and data analysis software followed the flow chart of EEG signal acquisition and

analysis showed in Fig. 6 and recorded the subject’s EEG signal under the condition of 1KHz sampling rate lasting for one period of data acquisition. After noise filtering, signal processing and data computation, the LabVIEW program will show the processed data graph in time domain as Fig. 7.

Fig. 6: The flow chart of EEG signal processing Fig. 7: EEG data graph in time domain

3. Results and Discussion

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There were 10 subjects including 1 female and 9 males attending the test of attention training and assessment. All these subjects divided into two groups are healthy and in good mental condition, their ages are between 18 and 26 years old. These subjects are all right-hand and never have the experience of playing the arm wrestle game we designed. The experimental procedure showed in Fig. 8 is described as follows:

Fig. 8: the test flow chart of attention training and assessment by playing BCI interactive game

The subjects are divided into two groups in the experiment: one is called control group the other is called experimental group. The subjects are demanded to pay attention to gazing the screen and count down 5 seconds in mind when playing the arm wrestle game in the test of attention training and assessment. The whole experiment procedure includes 3 stages and every stage spent 1 minute. Both groups must undergo three stages.

The first stage: to record each subject’s original EEG of the two different groups without any visional stimulation or interference. Both test contents of two groups are the same at stage 1.

The second stage: the test content for control group is different from the test content for experimental group in the second stage. Each subject’s EEG signal of control group was recorded for 1 minute without any visional stimulation or interference and then rested for 20 minutes in 1 cycle. The second stage for control group needs to run 5 cycles totally. However, each subject’s EEG signal of experimental group had to be recorded simultaneously for 1 minute when they were playing the arm wrestle game and then rested for 20 minutes in 1 cycle. The second stage for experimental group also needs to run 5 cycles totally.

The third stage: each subject’s EEG of two groups had to be recorded for 1 minute simultaneously during the period of playing the arm wrestle game after finishing the test of the second stage 30 minutes later. On the whole, each subject of control group had not played arm wrestle game until the third stage, however each subject of experimental group had played arm wrestle game for 6 times during the whole attention training and assessment test. The major difference of test content between control group and experimental group is at the second stage. Each subject in experimental group had to play the arm wrestle game 5 times for attention training at stage 2 and the subjects in control group didn’t.

After finishing the experimental tests of 10 subjects divided into two groups, we found that the experimental results showed that most trends of attention assessment of the subjects in either control group or experimental group are similar. The power spectrum amplitudes of alpha wave and beta wave frequency range for acquired EEG raw data are increasing. It means that most of the subjects always pay more attention to the game when playing it if the game is interesting or attractive to the subject. We chose one of the subjects’ EEG data as an example from experimental group and control group respectively, such as E1 (the subject representative of experimental group) and C1 (the subject representative of control group). Fig. 9(a) shows PS (Power Spectrum) analysis of E1’s EEG data at stage 1 and Fig. 9(b) shows PS analysis of E1’s EEG data at stage 3. We can find that the average of PS amplitudes for E1’s EEG at stage 3 is stronger than the average of PS amplitudes for E1’s EEG data at stage 1 obviously. The similar experimental results for C1 were showed in Fig. 10. Although, C1 accepted different test content (without playing the arm wrestle game 5 cycles) at stage 2 we also found that the average of PS amplitude of C1’s EEG becomes stronger at stage 3 showed in Fig. 10(b) than the average of PS amplitudes of C1’s EEG at stage 1 as shown in Fig. 10(a).

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(a) Power Spectrum of the attention test at stage 1 (b) Power Spectrum of the attention test at stage 3

Fig. 9: EEG data of the subject E1 in Exprimental group

(a) Power Spectrum of the attention test at stage 1 (b) Power Spectrum of the attention test at stage 3

Fig. 10: EEG data of the subject C1 in control group

4. Conclusion Due to the experiments mentioned above, either the non-trained subjects in control group or the

well-trained subjects in advance in experimental group, both have stronger average of PS amplitude of EEG among the frequency range of alpha wave (8-13Hz) and beta wave (14-30Hz) when they were playing the game. On the whole, we found the possible trend that the subjects will increase their attention when they are playing the game with interesting visional stimulation whether the subjects accept the attention training in advance or not.

In spite that we got the possible trend of the relationship between attention concentration and visional stimulation with the help of EEG control experiments, it still needs further analysis in frequency domain or time domain for example, p300 latency analysis, and gets more experimental data to prove the possible trend correct in the future. However, this preliminary experimental result can make us more confident to proceed with the next step of attention and EEG study.

5. Acknowledgements We want to appreciate everyone joining the research team and NSC (National Science Council)

supporting the funds by offering the NSC project numbered NSC 99-2221-E-218-016.

6. References

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[1] Robertson, I. H., & Garavan, H. (2004). Vigilant attention. In M. S. Gazzaniga (Ed.), The Cognitive Neurosciences III (pp. 631-640). New York: MIT

[2] Fan, J., McCandliss, B. D., Fossella, J., Flombaum, J. I., & Posner, M. I. (2005). “The activation of attentional networks,” Neuroimage, 26, 471-479.

[3] Rueda, M. R., Rothbart, M. K., McCandliss, B. D., Saccomanno, L., & Posner, M. I. (2005). “Training, maturation, and genetic influences on the development of executive attention,” Proceedings of National Academy of Sciences of the United States of America, 102(41), 14931-14936.

[4] Posner, M. I., & Rothbart, M. K. (2007). “Educating the Human Brain. Washington,” DC: American Psychological Association.

[5] Posner, M. I., & Raichle M. E. (1996). “Images of Mind,” NY: Scientific American Library.

[6] Murata, A., Uetake, A., and Takasawa, Y. “Evaluation of mental fatigue using feature parameter extracted from event-related potential,” Int. J. Ind. Ergon., 35: 761-770, 2005.