Graph signal processing for electroencephalogram (EEG) with application to brain-computer interfaces

Introducing a graph structure to electrode location and the use of graph Fourier transform lead to effective feature extraction from EEG.

References

T. Tanaka, T. Uehara and Y. Tanaka, "Dimensionality reduction of sample covariance matrices by graph Fourier transform for motor imagery brain-machine interface," 2016 IEEE Statistical Signal Processing Workshop (SSP), Palma de Mallorca, 2016, pp. 1-5.

doi: 10.1109/SSP.2016.7551798

H. Higashi, T. M. Rutkowski, T. Tanaka, and Y. Tanaka, ``Multilinear discriminant analysis with subspace constraints for single-trial classification of event-related potentials,'' IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 7, pp. 1295-1305, 2016. 

[DOI] 10.1109/JSTSP.2016.2599297

H. Higashi, T. Tanaka, and Y. Tanaka ``Smoothing of spatial filter by graph Fourier transform for EEG signals,'' in Proceedings of The 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2014), Siem Reap, Cambodia, Dec. 2014. 

Active data selection of trials for motor-imagery brain computer interfaces

A novel sparse signal processing / machine learning methods for selecting useful trials from a dataset of multiple trials.

Reference

Tomida, N.; Tanaka, T.; Ono, S.; Yamagishi, M.; Higashi, H., "Active Data Selection for Motor Imagery EEG Classification," Biomedical Engineering, IEEE Transactions on , vol.62, no.2, pp.458,467, Feb. 2015

Digital communication through the brain with steady-state visually evoked potentials (SSVEP)

Reference

Kimura, Y.; Tanaka, T.; Higashi, H.; Morikawa, N., "SSVEP-Based Brain–Computer Interfaces Using FSK-Modulated Visual Stimuli," in Biomedical Engineering, IEEE Transactions on , vol.60, no.10, pp.2831-2838, Oct. 2013

doi: 10.1109/TBME.2013.2265260

Mining of active frequency bands during motor imagery

Reference

Higashi, H.; Tanaka, T., "Simultaneous Design of FIR Filter Banks and Spatial Patterns for EEG Signal Classification," in Biomedical Engineering, IEEE Transactions on , vol.60, no.4, pp.1100-1110, April 2013

doi: 10.1109/TBME.2012.2215960

Machine Learning and Signal Processing