This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably due to the instantaneous fluctuation of psychophysiological states. A wavelet coherence (WC) analysis for optimally selecting associative inter-subject channels is proposed here and is being used to boost performances of motor imagery (MI)-based inter-subject brain computer interface (BCI). The underlying hypothesis is that optimally associative inter-subject channels can reduce the effects of outliers and, thus, eliminate dissimilar cortical patterns. The proposed approach has been tested on the dataset IVa from BCI competition III, including EEG data acquired from five healthy subjects who were given visual cues to perform 280 trials of MI for the right hand and right foot. Experimental results have shown increased classification accuracy (81.79%) using the WC-based selected 16 channels compared to the one (56.79%) achieved using all the available 118 channels. The associative channels lie mostly around the sensorimotor regions of the brain, reinforced by the previous literature, describing spatial brain dynamics during sensorimotor oscillations. Apparently, the proposed approach paves the way for optimised EEG channel selection that could boost further the efficiency and real-time performance of BCI systems.
References
-
-
1)
-
25. Zich, C., Debener, S., Kranczioch, C., et al: ‘Real-time EEG feedback during simultaneous EEG–fMRI identifies the cortical signature of motor imagery’, NeuroImage, 2015, 114, pp. 438–447 (doi: 10.1016/j.neuroimage.2015.04.020).
-
2)
-
6. Hasson, U.: ‘Intersubject synchronization of cortical activity during natural vision’, Science, 2004, 303, (5664), pp. 1634–1640 (doi: 10.1126/science.1089506).
-
3)
-
13. Samek, W., Meinecke, F., Muller, K.: ‘Transferring subspaces between subjects in brain–computer interfacing’, IEEE Trans. Biomed. Eng., 2013, 60, (8), pp. 2289–2298 (doi: 10.1109/TBME.2013.2253608).
-
4)
-
27. GonÃğalves, S., de Munck, J., Pouwels, P., et al: ‘Correlating the alpha rhythm to BOLD using simultaneous EEG/fMRI: inter-subject variability’, NeuroImage, 2006, 30, (1), pp. 203–213 (doi: 10.1016/j.neuroimage.2005.09.062).
-
5)
-
5. Giedd, J., Rapoport, J.: ‘Structural MRI of pediatric brain development: what have we learned and where are we going?’, Neuron, 2010, 67, (5), pp. 728–734 (doi: 10.1016/j.neuron.2010.08.040).
-
6)
-
1. Wang, H., Chang, W., Zhang, C.: ‘Functional brain network and multichannel analysis for the P300-based brain computer interface system of lying detection’, Expert Syst. Appl., 2016, 53, pp. 117–128 (doi: 10.1016/j.eswa.2016.01.024).
-
7)
-
16. Devlaminck, D., Wyns, B., Grosse-Wentrup, M., et al: ‘Multisubject learning for common spatial patterns in motor-imagery BCI’, Comput. Intell. Neurosci., 2011, 2011, pp. 1–9 (doi: 10.1155/2011/217987).
-
8)
-
23. Addison, P.: ‘The illustrated wavelet transform handbook’ (Institute of Physics Publication, Bristol, UK, 2002).
-
9)
-
21. Ramoser, H., Muller-Gerking, J., Pfurtscheller, G.: ‘Optimal spatial filtering of single trial EEG during imagined hand movement’, IEEE Trans. Rehabil. Eng., 2000, 8, (4), pp. 441–446 (doi: 10.1109/86.895946).
-
10)
-
4. van de Laar, B., Gurkok, H., Plass-Oude Bos, D., et al: ‘Experiencing BCI control in a popular computer game’, IEEE Trans. Comput. Intell. AI Games, 2013, 5, (2), pp. 176–184 (doi: 10.1109/TCIAIG.2013.2253778).
-
11)
-
12. Lu, S., Guan, C., Zhang, H.: ‘Unsupervised brain computer interface based on intersubject information and online adaptation’, IEEE Trans. Neural Syst. Rehabil. Eng., 2009, 17, (2), pp. 135–145 (doi: 10.1109/TNSRE.2009.2015197).
-
12)
-
2. Farwell, L., Richardson, D., Richardson, G., et al: ‘Brain fingerprinting classification concealed information test detects US Navy military medical information with P300’, Front. Neurosci., 2014, 8, p. 410 (doi: 10.3389/fnins.2014.00410).
-
13)
-
11. Rana, M., Gupta, N., Dalboni Da Rocha, J., et al: ‘A toolbox for real-time subject-independent and subject-dependent classification of brain states from fMRI signals’, Front. Neurosci., 2013, 7, p. 170 (doi: 10.3389/fnins.2013.00170).
-
14)
-
17. Fazli, S., Popescu, F., Danóczy, M., et al: ‘Subject-independent mental state classification in single trials’, Neural Netw., 2009, 22, (9), pp. 1305–1312 (doi: 10.1016/j.neunet.2009.06.003).
-
15)
-
24. Grinsted, A., Moore, J., Jevrejeva, S.: ‘Application of the cross wavelet transform and wavelet coherence to geophysical time series’, Nonlin. Processes Geophys., 2004, 11, (56), pp. 561–566 (doi: 10.5194/npg-11-561-2004).
-
16)
-
19. Klein, A., Sauer, T., Jedynak, A., et al: ‘Conventional and wavelet coherence applied to sensory–evoked electrical brain activity’, IEEE Trans. Biomed. Eng., 2006, 53, (2), pp. 266–272 (doi: 10.1109/TBME.2005.862535).
-
17)
-
7. Abrams, D., Ryali, S., Chen, T., et al: ‘Inter-subject synchronization of brain responses during natural music listening’, Eur. J. Neurosci., 2013, 37, (9), pp. 1458–1469 (doi: 10.1111/ejn.12173).
-
18)
-
10. Ray, A., Sitaram, R., Rana, M., et al: ‘A subject-independent pattern-based brain-computer interface’, Front. Behav. Neurosci., 2015, 9, p. 269 (doi: 10.3389/fnbeh.2015.00269).
-
19)
-
14. Samek, W., Kawanabe, M., Muller, K.: ‘Divergence-based framework for common spatial patterns algorithms’, IEEE Rev. Biomed. Eng., 2014, 7, pp. 50–72 (doi: 10.1109/RBME.2013.2290621).
-
20)
-
22. Svozil, D., Kvasnicka, V., Pospichal, J.: ‘Introduction to multi-layer feed-forward neural networks’, Chemometr. Intell. Lab. Syst., 1997, 39, (1), pp. 43–62 (doi: 10.1016/S0169-7439(97)00061-0).
-
21)
-
15. Lotte, F., Guan, C.: ‘Learning from other subjects helps reducing Brain-Computer Interface calibration time’. 2010 IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Dallas, TX, 2010, pp. 614–617.
-
22)
-
9. Niazi, I., Jiang, N., Jochumsen, M., et al: ‘Detection of movement-related cortical potentials based on subject-independent training’, Med. Biol. Eng. Comput., 2013, 51, (5), pp. 507–512 (doi: 10.1007/s11517-012-1018-1).
-
23)
-
8. Jeannerod, M.: ‘Mental imagery in the motor context’, Neuropsychologia, 1995, 33, (11), pp. 1419–1432 (doi: 10.1016/0028-3932(95)00073-C).
-
24)
-
18. Arvaneh, M., Guan, C., Ang, K.K., et al: ‘Optimizing the channel selection and classification accuracy in EEG-based BCI’, IEEE Trans. Biomed. Eng., 2011, 58, (6), pp. 1865–1873 (doi: 10.1109/TBME.2011.2131142).
-
25)
-
3. Ortiz Carreon, F., Gonzalez Serna, J., Montes Rendon, A., et al: ‘Induction of emotional states in people with disabilities through film clips using brain computer interfaces’, IEEE Latin Am. Trans., 2016, 14, (2), pp. 563–568 (doi: 10.1109/TLA.2016.7437193).
-
26)
-
28. Ahn, M., Jun, S.: ‘Performance variation in motor imagery brain–computer interface: a brief review’, J. Neurosci. Methods, 2015, 243, pp. 103–110 (doi: 10.1016/j.jneumeth.2015.01.033).
-
27)
-
26. Sannelli, C., Vidaurre, C., Müller, K., et al: ‘Ensembles of adaptive spatial filters increase BCI performance: an online evaluation’, J. Neural Eng., 2016, 13, (4), p. 046003 (doi: 10.1088/1741-2560/13/4/046003).
-
28)
-
20. Cui, X., Bryant, D., Reiss, A.: ‘NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation’, NeuroImage, 2012, 59, (3), pp. 2430–2437 (doi: 10.1016/j.neuroimage.2011.09.003).
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