A real-time EEG-based BCI system for attention recognition in ubiquitous environment

ABSTRACT

Several types of biological signal, such as Electroencephalogram (EEG), electrooculogram(EOG), electrocardiogram(ECG), electromyogram (EMG), skin temperature variation and electrodermal activity, may be used to measure a human subject's attention level. Generally electroencephalogram (EEG) is considered the most effective and objective indicator of attention level. However, few systems based on EEG have actually been developed to measure attention levels. In this paper we describe a pervasive system, based on an electroencephalogram (EEG) Brain-Computer Interface, which measures attention level. After demonstrating the effectiveness of our system we then go on to compare our approach with traditional approaches. In our study, three attention levels were classified by a KNN classifier based on the Self-Assessment Manikin (SAM) model. In our experiment, subjects were given several mental tasks to undertake and asked to report on their attention level during the tasks using a set of attention classifications. The average accuracy rate is shown to reach 57.03% after seven sessions' EEG training. Moreover, our system works in real-time while maintaining this accuracy. This is demonstrated by our time performance evaluation results which show that the time latency is short enough for our system to recognize attention in real-time.

References

1. Hamadicharef, B., Haihong Zhang, Cuntai Guan, Chuanchu Wang, Kok Soon Phua, Keng Peng Tee and Kai Keng Ang. Learning eeg-based spectral-spatial patterns for attention level measurement. IEEE International Symposium on Circuits and Systems, 2009, 1465--1468.
2. Liang, S. F., Lin, C. T., Wu, R. C., Chen, Y. C., Huang, T. Y. and Jung, T. P. Monitoring Driver's Alertness Based on the Driving Performance Estimation and the EEG Power Spectrum Analysis. Proceedings of the 2005 IEEE Engineering in Medicine and Biology Society, 2005, 5738--5741.
3. Nunez, P. L. Electric Fields of the Brain. second Edition. Oxford Univercity Press, New York, IN, USA, 2006.
4. Bassili, and J. N. Emotion recognition: The role of facial movement and the relative importance of upper and lower areas of the face. Journal of Personality and Social Psychology, 37(1979), 2049--2058.
5. Ira Cohen, Nicu Sebe, Ashutosh Garg, Lawrence S. Chen and Thomas S. Huang. Facial Expression Recognition from Video Sequences: Temporal and Static Modeling. Computer Vision and Image Understanding, 91(2003), 160--187.
6. L. C. De Silva, T. Miyasato, and R. Nakatsu. Use of multimodal information in facial emotion recognition. IEICE Transaction, E81-D(1)(1998), 105--114.
7. Carlos Busso, Zhigang Deng, Serdar Yildirim, Murtaza Bulut, Chul Min Lee, Abe Kazemzadeh, Sungbok Lee, Ulrich Neumann and Shrikanth Narayanan. Analysis of emotion recognition using facial expressions, speech and multimodal information. Proceedings of the 6th international conference on Multimodal interfaces, State College, USA, 2004.
8. K. H. Kim, S. W. Bang and S. R. Kim. Emotion recognition system using short-termmonitoring of physiological signalssamples. Medical and Biological Engineering and Computing, 42(2004), 419--427.
9. W. Ray and H. Cole. EEG alpha activity reflects attentional demands and beta activity reflects emotional and cognitive processes. Science, 228 (1985), 750--752.
10. W. Klimesch, W. Doppelmayr, H. Russegger, T. Pachinger, and J. Schwaiger. Induced alpha band power changes in the human EEG and attention. Neuroscience Letters, 244 (1998), 73--76.
11. Srinivasan, R., Thorpe, S., Deng, S., Lappas, T., and D'Zmura, M. Decoding attentional orientation from eeg spectra. Human-Computer Interaction: New Trends, Lecture Notes in Computer Science, 5610 (2009), 176--183.
12. Santesso, D. L., Schmidt, L. A., and Trainor, L. J. Frontal brain electrical activity (EEG) and heart rate in response to affective infant-directed (ID) speech in 9-month-old infants. Brain and Cognition, 65(2007), 14--21.
13. J. Duann, P. Chen, L. Ko, R. Huang, T. Jung. and C. Lin, Detecting Frontal EEG Activities with Forehead Electrodes. Proc. HCI (16), 2009, 373--379.
14. Savran, A., K. Ciftci, G. Chanel, J. Mota, L. Viet, B. Sankur, L. Akarun, A. Caplier, M. Rombaut. Emotion detection in the loop from brain signals and facial images, http://www.enterface.net/results/, 2006.
15. Jausovec, N. Differences in cognitive processes between gifted, intelligent, creative and average individuals while solving complex problems. Intelligence, 28( 2000), 213--237.
16. S. F. Liang, C. T. Lin, R. C. Wu, Y. C. Chen, T. Y. Huang and T. P. Jung. Monitoring Driver's Alertness Based on the Driving Performance Estimation and the EEG Power Spectrum Analysis. Proceedings of the 2005 IEEE Engineering in Medicine and Biology Society (2005), 5738--5741.
17. Jose C. Principe, Member, IEEE, and Jack R. Smith. Design and Implementation of Linear Phase FIR Filters for Biological Signal Processing. IEEE Transactions on Biomedical Engineering, BME-33 (1986), 550--559.
18. Berger, H. Uber das elektroenzephalogramm des enschen. Arch Psychiatr, 87 (1929), 527--570.
19. Kilmesch, W., Schimke, H., and Pfurtscheller, G. Alpha frequency, cognitive load and memory performance. Brain Topography, 5 (1993), 241--251.
20. Mecklinger, A., Kramer, A. F., and Strayer, D. L. Event related potentials and EEG components in a semantic memory search task. Psychophysiology, 29(1992), 104--119.
21. Howard, M. W., Rizzuto, D. S., Caplan, J. B., Madsen, J. R., Lisman, J., Aschenbrenner-Scheibe, R., Schulze-Bonhage, A., and Kahana, M. J. Gamma oscillations correlate with working memory load in humans. Cerebral Cortex, 13(2003), 1369--1374.
22. Pleydell-Pearce, C. W., Whitecross, S. E., and Dickson, B. T. Multivariate analysis of EEG: Predicting cognition on the basis of frequency decomposition, inter-electrode correlation, coherence, cross phase and cross power. Hawaii International Conference on System Sciences. 2003, 11--20.
23. David Grimes, Desney S. Tan, Scott E. Hudson, Pradeep Henoy, and Rajesh P. N. Rao. Feasibility and pragmatics of lassifying working memory load with an electroencephalograph. Proceeding of the twenty sixth annual SIGCHI conference on Human factors in omputing systems. Apr. 2008, 835--844.
24. Prinzel, L. J., Pope, A. T., Freeman, F. G., Scerbo, M. W., and Mikulka, P. J. Empirical analysis of EEG and ERPs for psychophysiological adaptive task allocation. NASA Technical Report TM-2001-211016, 2001.
25. Bradley, M. M. - Lang, P. J, Measuring Emotion: The Self-Assessment Manikin (SAM) and the Semantic Differential. Journal of Experimental Psychiatry Behavior Therapy, 25(1994), 4--59.
26. Li, X., Zhao, Q., Hu, B., Liu, L., Peng, H., Qi, Y., Mao, C., Fang, Z., and Liu, Q. Improve Affective Learning with EEG Approach. Proceedings of Computing and Informatics, 2010, 557--570.
27. F. Lotte, M. Congedo, A. Lécuyer, F. Lamarché and B. Arnaldi. A review of classification algorithms for EEG-based brain-computer interfaces. Journal of Neural Engineering, 2007, 1--13.
28. Asada, H., Fukuda, Y., Tsunoda, S., Yamaguchi, M., and Tonoike, M. Frontal midline theta rhythms reflect alternative activation of prefrontal cortex and anterior cingulated cortex in humans. Neuroscience Letters, 274(1999), 29--32.

Index Terms

• Human-centered computing