Bimodal BCI Using Simultaneously NIRS and EEG
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Andrzej Cichocki | Yasue Mitsukura | François-Benoît Vialatte | Gérard Dreyfus | Yohei Tomita | Hovagim Bakardjian | A. Cichocki | G. Dreyfus | H. Bakardjian | Y. Mitsukura | F. Vialatte | Y. Tomita
[1] M. Bianciardi,et al. Single-epoch analysis of interleaved evoked potentials and fMRI responses during steady-state visual stimulation , 2009, Clinical Neurophysiology.
[2] N. Logothetis,et al. Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.
[3] Wei Wu,et al. Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs , 2006, IEEE Transactions on Biomedical Engineering.
[4] Jukka Heikkonen,et al. A local neural classifier for the recognition of EEG patterns associated to mental tasks , 2002, IEEE Trans. Neural Networks.
[5] Tania S. Douglas,et al. Motion Artifact Removal for Functional Near Infrared Spectroscopy: A Comparison of Methods , 2010, IEEE Transactions on Biomedical Engineering.
[6] C. Markham,et al. Hemodynamics for Brain-Computer Interfaces , 2008, IEEE Signal Processing Magazine.
[7] Rabab K Ward,et al. A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals , 2007, Journal of neural engineering.
[8] L. Cohen,et al. Brain–computer interfaces: communication and restoration of movement in paralysis , 2007, The Journal of physiology.
[9] Keum-Shik Hong,et al. Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy , 2010, Biomedical engineering online.
[10] Lin Yang,et al. Linear and nonlinear relationships between visual stimuli, EEG and BOLD fMRI signals , 2010, NeuroImage.
[11] A. Cichocki,et al. Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives , 2010, Progress in Neurobiology.
[12] T. Chau,et al. Single-trial classification of NIRS signals during emotional induction tasks: towards a corporeal machine interface , 2009, Journal of NeuroEngineering and Rehabilitation.
[13] E. Fetz. Operant Conditioning of Cortical Unit Activity , 1969, Science.
[14] Emery N Brown,et al. Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: a Monte Carlo simulation study. , 2007, Journal of biomedical optics.
[15] G. Pfurtscheller,et al. Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.
[16] Owen Falzon,et al. The analytic common spatial patterns method for EEG-based BCI data , 2012, Journal of neural engineering.
[17] M. Thulasidas,et al. Robust classification of EEG signal for brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[18] Hellmuth Obrig,et al. Synchronization between Background Activity and Visually Evoked Potential Is Not Mirrored by Focal Hyperoxygenation: Implications for the Interpretation of Vascular Brain Imaging , 2006, The Journal of Neuroscience.
[19] A. Dale,et al. Selective averaging of rapidly presented individual trials using fMRI , 1997, Human brain mapping.
[20] T. Ono,et al. Brain Cortical Mapping by Simultaneous Recording of Functional Near Infrared Spectroscopy and Electroencephalograms from the Whole Brain During Right Median Nerve Stimulation , 2009, Brain Topography.
[21] Klaus-Robert Müller,et al. Enhanced Performance by a Hybrid Nirs–eeg Brain Computer Interface , 2022 .
[22] David A. Boas,et al. A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans , 2006, NeuroImage.
[23] Byoung-Kyong Min,et al. Neuroimaging-based approaches in the brain-computer interface. , 2010, Trends in biotechnology.
[24] Brendan Z. Allison,et al. The Hybrid BCI , 2010, Frontiers in Neuroscience.
[25] Quan Zhang,et al. Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: How well and when does it work? , 2009, NeuroImage.
[26] N. Birbaumer. Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. , 2006, Psychophysiology.
[27] W. Marsden. I and J , 2012 .
[28] Yijun Wang,et al. Brain-Computer Interfaces Based on Visual Evoked Potentials , 2008, IEEE Engineering in Medicine and Biology Magazine.
[29] A. Cichocki,et al. Optimization of SSVEP brain responses with application to eight-command Brain–Computer Interface , 2010, Neuroscience Letters.
[30] R. Leeb,et al. BCI Competition 2008 { Graz data set B , 2008 .
[31] Cuntai Guan,et al. Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.
[32] Saeid Sanei,et al. EEG signal processing , 2000, Clinical Neurophysiology.
[33] Ivan Volosyak,et al. Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces , 2007, IEEE Transactions on Biomedical Engineering.
[34] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.
[35] Cuntai Guan,et al. Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface , 2007, NeuroImage.
[36] Shirley Coyle,et al. On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces. , 2004, Physiological measurement.
[37] Bernhard Schölkopf,et al. Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals , 2006, DAGM-Symposium.
[38] G Gratton,et al. Removing the heart from the brain: compensation for the pulse artifact in the photon migration signal. , 1995, Psychophysiology.