80
M. Congedo
43. A.P. Holmes, R.C. Blair, J.D.G. Watson et al., Nonparametric analysis of statistic images from
functional mapping experiments. J. Cereb. Blood Flow Metab. 16, 7–22 (1996)
44. B.H. Jansen, G. Agarwal, A. Hedge et al., Phase synchronization of the ongoing EEG and
auditory EP generation. Clin. Neurophysiol. 114, 79–85 (2003)
45. E.R. John, D.S. Ruchkin, J. Vilegas, Experimental background: signal analysis and behavioral
correlates of evoked potential configurations in cats. Ann. N. Y. Acad. Sci. 112, 362–420
(1964)
46. W. Karniski, R.C. Blair, A.D. Snider, An exact statistical method for comparing topographic
maps, with any number of subjects and electrodes. Brain Topogr. 6, 203–210 (1994)
47. P.-J. Kindermans, D. Verstraeten, B. Schrauwen, A Bayesian Model for Exploiting Application
Constraints to Enable Unsupervised Training of a P300-based BCI. PLoS ONE 7, e33758
(2012)
48. P.-J. Kindermans, M. Schreuder, B. Schrauwen et al., True zero-training brain-computer interfacing—an online study. PLoS One 9, e102504 (2014)
49. L. Korczowski, F. Bouchard, C. Jutten et al., in Mining the Bilinear Structure of Data with
Approximate Joint Diagonalization. 24th European Signal Processing Conference, Budapest,
Hungary, August 2016. (2016) pp. 667–671
50. J.-P. Lachaux, E. Rodriguez, J. Martinerie et al., Measuring phase synchrony in brain signals.
Hum. Brain Mapp. 8, 194–208 (1999)
51. T.D. Lagerlund, F.W. Sharbrough, N.E. Busacker, Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. J. Clin. Neurophysiol. 14, 73–82 (1997)
52. D. Lehmann, W. Skrandies, Reference-free identification of components of checkerboardevoked multichannel potential fields. Electroencephalogr. Clin. Neurophysiol. 48, 609–621
(1980)
53. M. Le Van Quyen, J. Foucher, J.-P. Lachaux et al., Comparison of Hilbert transform and
wavelet methods for the analysis of neuronal synchrony. J. Neurosci. Methods 111, 83–98
(2001)
54. J.E. Lisman, O. Jensen, The theta-gamma neural code. Neuron 77, 1002–1016 (2013)
55. R.R. Llinas, The intrinsic electrophysiological properties of mammalian neurons: insights
into central nervous system function. Science 242, 1654–1664 (1988)
56. J.M. Le ˛ski, Robust weighted averaging. IEEE Trans. Biomed. Eng. 49, 796–804 (2002)
57. F.H. Lopes da Silva, Event-related neural activities: what about phase? Prog. Brain Res. 159,
3–17 (2006)
58. F.H. Lopes da Silva, J.P. Pijn, D. Velis et al., Alpha rhythms: noise, dynamics, and models.
Int. J. Psychophysiol. 26, 237–249 (1997)
59. F. Lotte, M. Congedo, A. Lécuyer et al., A review of classification algorithms for EEG-based
brain-computer interfaces. J. Neural Eng. 4, R1–R13 (2007)
60. F. Lotte, L. Bougrain, A. Cichocki et al., A review of classification algorithms for EEG-based
brain-computer interfaces: a 10-year update (Manuscript submitted for publication) (2018)
61. S. Luck, An Introduction to the Event-Related Potential Technique, 2nd edn. (The MIT Press,
Cambridge, MA, 2014)
62. S. Makeig, M. Westerfield, T.P. Jung et al., Dynamic brain sources of visual evoked responses.
Science 295, 690–694 (2002)
63. E. Maris, R. Oostenveld, Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci.
Methods 164, 177–190 (2007)
64. S.L. Marple, Computing the discrete-time analytic signal via FFT. IEEE Trans. Signal Process.
47, 2600–2603 (1999)
65. A. Mazaheri, O. Jensen, Rhythmic pulsing: linking ongoing brain activity with evoked
responses. Front. Hum. Neurosci. 4, 177 (2010)
66. L. Mayaud, S. Cabanilles, A. Van Langhenhove et al., Brain-computer interface for the communication of acute patients: a feasibility study and a randomized controlled trial comparing
performance with healthy participants and a traditional assistive device. BCI 3, 197–215
(2016)
M. Congedo
43. A.P. Holmes, R.C. Blair, J.D.G. Watson et al., Nonparametric analysis of statistic images from
functional mapping experiments. J. Cereb. Blood Flow Metab. 16, 7–22 (1996)
44. B.H. Jansen, G. Agarwal, A. Hedge et al., Phase synchronization of the ongoing EEG and
auditory EP generation. Clin. Neurophysiol. 114, 79–85 (2003)
45. E.R. John, D.S. Ruchkin, J. Vilegas, Experimental background: signal analysis and behavioral
correlates of evoked potential configurations in cats. Ann. N. Y. Acad. Sci. 112, 362–420
(1964)
46. W. Karniski, R.C. Blair, A.D. Snider, An exact statistical method for comparing topographic
maps, with any number of subjects and electrodes. Brain Topogr. 6, 203–210 (1994)
47. P.-J. Kindermans, D. Verstraeten, B. Schrauwen, A Bayesian Model for Exploiting Application
Constraints to Enable Unsupervised Training of a P300-based BCI. PLoS ONE 7, e33758
(2012)
48. P.-J. Kindermans, M. Schreuder, B. Schrauwen et al., True zero-training brain-computer interfacing—an online study. PLoS One 9, e102504 (2014)
49. L. Korczowski, F. Bouchard, C. Jutten et al., in Mining the Bilinear Structure of Data with
Approximate Joint Diagonalization. 24th European Signal Processing Conference, Budapest,
Hungary, August 2016. (2016) pp. 667–671
50. J.-P. Lachaux, E. Rodriguez, J. Martinerie et al., Measuring phase synchrony in brain signals.
Hum. Brain Mapp. 8, 194–208 (1999)
51. T.D. Lagerlund, F.W. Sharbrough, N.E. Busacker, Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. J. Clin. Neurophysiol. 14, 73–82 (1997)
52. D. Lehmann, W. Skrandies, Reference-free identification of components of checkerboardevoked multichannel potential fields. Electroencephalogr. Clin. Neurophysiol. 48, 609–621
(1980)
53. M. Le Van Quyen, J. Foucher, J.-P. Lachaux et al., Comparison of Hilbert transform and
wavelet methods for the analysis of neuronal synchrony. J. Neurosci. Methods 111, 83–98
(2001)
54. J.E. Lisman, O. Jensen, The theta-gamma neural code. Neuron 77, 1002–1016 (2013)
55. R.R. Llinas, The intrinsic electrophysiological properties of mammalian neurons: insights
into central nervous system function. Science 242, 1654–1664 (1988)
56. J.M. Le ˛ski, Robust weighted averaging. IEEE Trans. Biomed. Eng. 49, 796–804 (2002)
57. F.H. Lopes da Silva, Event-related neural activities: what about phase? Prog. Brain Res. 159,
3–17 (2006)
58. F.H. Lopes da Silva, J.P. Pijn, D. Velis et al., Alpha rhythms: noise, dynamics, and models.
Int. J. Psychophysiol. 26, 237–249 (1997)
59. F. Lotte, M. Congedo, A. Lécuyer et al., A review of classification algorithms for EEG-based
brain-computer interfaces. J. Neural Eng. 4, R1–R13 (2007)
60. F. Lotte, L. Bougrain, A. Cichocki et al., A review of classification algorithms for EEG-based
brain-computer interfaces: a 10-year update (Manuscript submitted for publication) (2018)
61. S. Luck, An Introduction to the Event-Related Potential Technique, 2nd edn. (The MIT Press,
Cambridge, MA, 2014)
62. S. Makeig, M. Westerfield, T.P. Jung et al., Dynamic brain sources of visual evoked responses.
Science 295, 690–694 (2002)
63. E. Maris, R. Oostenveld, Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci.
Methods 164, 177–190 (2007)
64. S.L. Marple, Computing the discrete-time analytic signal via FFT. IEEE Trans. Signal Process.
47, 2600–2603 (1999)
65. A. Mazaheri, O. Jensen, Rhythmic pulsing: linking ongoing brain activity with evoked
responses. Front. Hum. Neurosci. 4, 177 (2010)
66. L. Mayaud, S. Cabanilles, A. Van Langhenhove et al., Brain-computer interface for the communication of acute patients: a feasibility study and a randomized controlled trial comparing
performance with healthy participants and a traditional assistive device. BCI 3, 197–215
(2016)