CONCATENATION METHOD FOR HIGH-TEMPORAL RESOLUTION SSVEP-BCI

Yohei Tomita, Antoine Gaume, Hovagim Bakardjian, Monique Maurice, Andrzej Cichocki, Yoko Yamaguchi, Gérard Dreyfus, François-Benoît Maurice

2011

Abstract

Electroencephalographic (EEG) signals are generally non-stationary, however, nearly stationary brain responses, such as steady-state visually evoked potentials (SSVEP), can be recorded in response to repetitive stimuli. Although Fourier transform has precise resolution with long time windows (5 or 10 s for instance) to extract SSVEP response (1-100 Hz ranges), its resolution with shorter windows decreases due to the Heisenberg-Gabor uncertainty principle. Therefore, it is not easy to extract evoked responses such as SSVEP within short EEG epochs. This limits the information transfer rate of SSVEP-based brain-computer interfaces. In order to circumvent this limitation, we concatenate EEG signals recorded simultaneously from different channels, and we Fourier analyze the resulting sequence. From this constructed signal, high frequency resolution can be obtained with time epochs as small as only 1 s, which improves SSVEPs classification. This method may be effective for high-speed brain computer interfaces (BCI).

References

  1. Bashashati, A., Fatourechi, M., and Ward, R. K. (2007). A survey of signal processing algorithms in braincomputer interfaces based on electrical brain signals. J. Neural Engineering, 4:R32-R57.
  2. Bin, G., Gao, X., Yan, Z., Hong, B., and Gao, S. (2009). An online multi-channel ssvep-based brain-computer interface using a canonical correlation analysis method. J. Neural Engineering, 6(4):046002.
  3. Birbaumer, N. (2006). Breaking the silence: braincomputer interfaces (bci) for communication and motor control. Psychophysiology, 43:517-532.
  4. Dauwels, J., Vialatte, F.-B., Musha, T., and Cichocki, A. (2010). A comparative study of synchrony measures for the early diagnosis of alzheimer's disease based on eeg. NeuroImage, 49:668-693.
  5. Dreyfus, G. (2005). Neural networks: methodology and applications. Springer-Verlag.
  6. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., and Liu, H. H. (1998). The empirical mode decomposition and hilbert spectrum from nonlinear and non-stationary time series analysis. In R. Soc. Lond. A, volume 454, pages 903-995.
  7. Kawabata, N. (1973). A non-stationary analysis of the electroencephalogram. IEEE Trans Biomed Eng, 20:444- 452.
  8. Lotte, F., Cogedo, M., Lecuyer, A., Lamarche, F., and Arnaldi, B. (2007). A review of classification algorithms for eeg-based brain-computer interfaces. J. Neural Engineering, 4:R1-R13.
  9. Nunez, P. L., Srinivasan, R., Westdorp, A. F., Wijesinghe, R. S., Tucker, D. M., Silberstein, R. B., and Cadusch, P. J. (1997). Eeg coherency: l: statistics, reference electrode, volume conduction, laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalography and Clinical Neurophysiology, 103:499-515.
  10. Quiroga, R., Sakowitz, O., Bas¸ar, E., and Schürmann, M. (2001). Wavelet transform in the analysis of the frequency composition of evoked potentials. Brain Research Protocols, 8:16-24.
  11. Sanei, S. and Chambers, J. A. (2007). EEG signal processing. Wiley, Lodon.
  12. Vialatte, F., Solé-Casals, J., and Cichocki, A. (2008). Eeg windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts. Physiological Measurements, 29(12):1435-1452.
  13. Vialatte, F.-B., Maurice, M., Dauwels, J., and Cichocki, A. (2010). Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. Progress in Neurobiology, 90:418-438.
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Paper Citation


in Harvard Style

Tomita Y., Gaume A., Bakardjian H., Maurice M., Cichocki A., Yamaguchi Y., Dreyfus G. and Maurice F. (2011). CONCATENATION METHOD FOR HIGH-TEMPORAL RESOLUTION SSVEP-BCI . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: Special Session on Challenges in Neuroengineering, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 444-452. DOI: 10.5220/0003724404440452


in Bibtex Style

@conference{special session on challenges in neuroengineering11,
author={Yohei Tomita and Antoine Gaume and Hovagim Bakardjian and Monique Maurice and Andrzej Cichocki and Yoko Yamaguchi and Gérard Dreyfus and François-Benoît Maurice},
title={CONCATENATION METHOD FOR HIGH-TEMPORAL RESOLUTION SSVEP-BCI },
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: Special Session on Challenges in Neuroengineering, (IJCCI 2011)},
year={2011},
pages={444-452},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003724404440452},
isbn={978-989-8425-84-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: Special Session on Challenges in Neuroengineering, (IJCCI 2011)
TI - CONCATENATION METHOD FOR HIGH-TEMPORAL RESOLUTION SSVEP-BCI
SN - 978-989-8425-84-3
AU - Tomita Y.
AU - Gaume A.
AU - Bakardjian H.
AU - Maurice M.
AU - Cichocki A.
AU - Yamaguchi Y.
AU - Dreyfus G.
AU - Maurice F.
PY - 2011
SP - 444
EP - 452
DO - 10.5220/0003724404440452