10 Computational EEG Analysis for Hyperscanning …
225
[24]: different brains might encode stimuli in different ways, but the mental distances
among these encoded stimuli are expected to remain largely invariant. Therefore, it
is reasonable to hypothesize the existence of inter-brain hyperlinks in a higher-level
representational space. As such a representational space is more closely related to
our mental world than the signal space, representational hyperlinks should have more
important theoretical implications, as compared to our current findings.
Last but not least, the latest development in artificial intelligence and machine
learning methods, such as the deep learning neural networks, may help facilitate our
exploration of multi-brain data [20, 50]. These methods are expected to extract critical
information from high-dimensional multi-brain data without explicit modelling and
extensive labor, speeding up the development of the social neuroscience field.
References
1. F. Babiloni, F. Cincotti, D. Mattia et al., Hypermethods for EEG hyperscanning, in 28th Annual
International Conference of the IEEE Engineering in Medicine and Biology Society (New York,
NY, USA, 30 August–3 September 2006)
2. F. Babiloni, L. Astolfi, Social neuroscience and hyperscanning techniques: past, present and
future. Neurosci. Biobehav. Rev. 44, 76–93 (2014)
3. D.A. Bridwell, C. Roth, C.N. Gupta, V.D. Calhoun, Cortical response similarities predict which
audiovisual clips individuals viewed, but are unrelated to clip preference. PLoS ONE 10(6),
e0128833 (2015)
4. W.-T. Chang, I.P. Jääskeläinen, J.W. Belliveau et al., Combined MEG and EEG show reliable
patterns of electromagnetic brain activity during natural viewing. NeuroImage 114, 49–56
(2015)
5. A. de Cheveigné, L.C. Parra, Joint decorrelation, a versatile tool for multichannel data analysis.
NeuroImage 98, 487–505 (2014)
6. A. de Cheveigné, Sparse time artifact removal. J. Neurosci. Methods 262, 14–20 (2016)
7. H. De Jaegher, E. Di Paolo, R. Adolphs, What does the interactive brain hypothesis mean for
social neuroscience? A dialogue. Phil. Trans. R. Soc. B 371(1693), 20150379 (2016)
8. E.A. Di Paolo, H. De Jaegher, The interactive brain hypothesis. Front. Hum. Neurosci. 6, 163
(2012)
9. J.P. Dmochowski, P. Sajda, J. Dias, L.C. Parra, Correlated components of ongoing EEG point
to emotionally laden attention–a possible marker of engagement? Front. Hum. Neurosci. 6,
112 (2012)
10. J.P. Dmochowski, M.A. Bezdek, B.P. Abelson et al., Audience preferences are predicted by
temporal reliability of neural processing. Nat. Commun. 5, 4567 (2014)
11. J.P. Dmochowski, A.S. Greaves, A.M. Norcia, Maximally reliable spatial filtering of steady
state visual evoked potentials. NeuroImage 109, 63–72 (2015)
12. T.D. Duane, T. Behrendt, Extrasensory electroencephalographic induction between identical
twins. Science 150(3694), 367 (1965)
13. G. Dumas, J. Nadel, R. Soussignan et al., Inter-brain synchronization during social interaction.
PLoS ONE 5(8), e12166 (2010)
14. G. Dumas, F. Lachat, J. Martinerie et al., From social behaviour to brain synchronization:
review and perspectives in hyperscanning. Irbm 32(1), 48–53 (2011)
15. M.P. Eckstein, K. Das, B.T. Pham et al., Neural decoding of collective wisdom with multi-brain
computing. NeuroImage 59(1), 94–108 (2012)
16. S. Gao, Y. Wang, X. Gao, B. Hong, Visual and auditory brain–computer interfaces. IEEE Trans.
Biomed. Eng. 61(5), 1436–1447 (2014)
225
[24]: different brains might encode stimuli in different ways, but the mental distances
among these encoded stimuli are expected to remain largely invariant. Therefore, it
is reasonable to hypothesize the existence of inter-brain hyperlinks in a higher-level
representational space. As such a representational space is more closely related to
our mental world than the signal space, representational hyperlinks should have more
important theoretical implications, as compared to our current findings.
Last but not least, the latest development in artificial intelligence and machine
learning methods, such as the deep learning neural networks, may help facilitate our
exploration of multi-brain data [20, 50]. These methods are expected to extract critical
information from high-dimensional multi-brain data without explicit modelling and
extensive labor, speeding up the development of the social neuroscience field.
References
1. F. Babiloni, F. Cincotti, D. Mattia et al., Hypermethods for EEG hyperscanning, in 28th Annual
International Conference of the IEEE Engineering in Medicine and Biology Society (New York,
NY, USA, 30 August–3 September 2006)
2. F. Babiloni, L. Astolfi, Social neuroscience and hyperscanning techniques: past, present and
future. Neurosci. Biobehav. Rev. 44, 76–93 (2014)
3. D.A. Bridwell, C. Roth, C.N. Gupta, V.D. Calhoun, Cortical response similarities predict which
audiovisual clips individuals viewed, but are unrelated to clip preference. PLoS ONE 10(6),
e0128833 (2015)
4. W.-T. Chang, I.P. Jääskeläinen, J.W. Belliveau et al., Combined MEG and EEG show reliable
patterns of electromagnetic brain activity during natural viewing. NeuroImage 114, 49–56
(2015)
5. A. de Cheveigné, L.C. Parra, Joint decorrelation, a versatile tool for multichannel data analysis.
NeuroImage 98, 487–505 (2014)
6. A. de Cheveigné, Sparse time artifact removal. J. Neurosci. Methods 262, 14–20 (2016)
7. H. De Jaegher, E. Di Paolo, R. Adolphs, What does the interactive brain hypothesis mean for
social neuroscience? A dialogue. Phil. Trans. R. Soc. B 371(1693), 20150379 (2016)
8. E.A. Di Paolo, H. De Jaegher, The interactive brain hypothesis. Front. Hum. Neurosci. 6, 163
(2012)
9. J.P. Dmochowski, P. Sajda, J. Dias, L.C. Parra, Correlated components of ongoing EEG point
to emotionally laden attention–a possible marker of engagement? Front. Hum. Neurosci. 6,
112 (2012)
10. J.P. Dmochowski, M.A. Bezdek, B.P. Abelson et al., Audience preferences are predicted by
temporal reliability of neural processing. Nat. Commun. 5, 4567 (2014)
11. J.P. Dmochowski, A.S. Greaves, A.M. Norcia, Maximally reliable spatial filtering of steady
state visual evoked potentials. NeuroImage 109, 63–72 (2015)
12. T.D. Duane, T. Behrendt, Extrasensory electroencephalographic induction between identical
twins. Science 150(3694), 367 (1965)
13. G. Dumas, J. Nadel, R. Soussignan et al., Inter-brain synchronization during social interaction.
PLoS ONE 5(8), e12166 (2010)
14. G. Dumas, F. Lachat, J. Martinerie et al., From social behaviour to brain synchronization:
review and perspectives in hyperscanning. Irbm 32(1), 48–53 (2011)
15. M.P. Eckstein, K. Das, B.T. Pham et al., Neural decoding of collective wisdom with multi-brain
computing. NeuroImage 59(1), 94–108 (2012)
16. S. Gao, Y. Wang, X. Gao, B. Hong, Visual and auditory brain–computer interfaces. IEEE Trans.
Biomed. Eng. 61(5), 1436–1447 (2014)