Multi-modal

In order to render measurements more robust, information may be provided by different modalities. Many groups appreciate multi-modal applications with fNIRS. Typical combinations are fNIRS and EEG, Eye-Tracking or fMRI, but tDCS and TMS have also been applied to concurrently modulate brain activity.

Co registering fNIRS data & structural MRI data in MATLAB Brain AnalyzIR Toolbox NIRS Toolbox

 

Ludyga, S., Mücke, M., Colledge, F. M. A., Pühse, U., & Gerber, M. (2019). “A Combined EEG-fNIRS Study Investigating Mechanisms Underlying the Association between Aerobic Fitness and Inhibitory Control in Young Adults”. Neuroscience, 419, 23-33

Vassena, E., Gerrits, R., Demanet, J., Verguts, T., & Siugzdaite, R. (2019). “Anticipation of a mentally effortful task recruits Dorsolateral Prefrontal Cortex: An fNIRS validation study”. Neuropsychologia, 123, 106-115.

R. Li, T. Nguyen, T. Potter, and Y. Zhang, “Dynamic cortical connectivity alterations associated with Alzheimer’s disease: An EEG and fNIRS integration study,” NeuroImage: Clinical, Dec. 2018.

A. Landowska, D. Roberts, P. Eachus, and A. Barrett, “Within- and Between-Session Prefrontal Cortex Response to Virtual Reality Exposure Therapy for Acrophobia,” Front Hum Neurosci, vol. 12, Nov. 2018.

M. Balconi, A. Frezza, and M. E. Vanutelli, “Emotion Regulation in Schizophrenia: A Pilot Clinical Intervention as Assessed by EEG and Optical Imaging (Functional Near-Infrared Spectroscopy),” Front Hum Neurosci, vol. 12, Oct. 2018.

O. Klempíř et al., “P 024 - Near-infrared spectroscopy patterns of cortical activity during gait in Parkinson’s disease patients treated with DBS STN,” Gait & Posture, vol. 65, pp. 273–275, Sep. 2018.

A. Lee et al., “Slow oscillations of cerebral hemodynamics changes during low-level light therapy in the elderly with and without mild cognitive impairment: An fNIRS study,” Annals of Physical and Rehabilitation Medicine, vol. 61, p. e256, Jul. 2018.

J. Shin, D.-W. Kim, K.-R. Müller, and H.-J. Hwang, “Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses,” Sensors (Basel), vol. 18, no. 6, Jun. 2018

A. Berger, N. H. Pixa, F. Steinberg, and M. Doppelmayr, “Brain Oscillatory and Hemodynamic Activity in a Bimanual Coordination Task Following Transcranial Alternating Current Stimulation (tACS): A Combined EEG-fNIRS Study,” Front Behav Neurosci, vol. 12, Apr. 2018.

M. Balconi et al., “Emotion regulation in Schizophrenia: A comparison between implicit (EEG and fNIRS) and explicit (valence) measures: Preliminary observations,” Asian Journal of Psychiatry, vol. 34, pp. 12–13, Apr. 2018.

K. Arun, K. Smitha, P. Rajesh, and C. Kesavadas, “Functional near-infrared spectroscopy is in moderate accordance with functional MRI in determining lateralisation of frontal language areas,” Neuroradiol J, vol. 31, no. 2, pp. 133–141, Apr. 2018.

J. Shin, A. von Lühmann, D.-W. Kim, J. Mehnert, H.-J. Hwang, and K.-R. Müller, “Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset,” Scientific Data, vol. 5, p. 180003, Feb. 2018.

M. A. Yaqub, S.-W. Woo, and K.-S. Hong, “Effects of HD-tDCS on Resting-State Functional Connectivity in the Prefrontal Cortex: An fNIRS Study,” Complexity, 2018. 

A. Landowska, S. Royle, P. Eachus, and D. Roberts, “Testing the Potential of Combining Functional Near-Infrared Spectroscopy with Different Virtual Reality Displays—Oculus Rift and oCtAVE,” in Augmented Reality and Virtual Reality: Empowering Human, Place and Business, T. Jung and M. C. tom Dieck, Eds. Cham: Springer International Publishing, 2018, pp. 309–321.

F. Dehais et al., “Monitoring pilot’s cognitive fatigue with engagement features in simulated and actual flight conditions using an hybrid fNIRS-EEG passive BCI,” in IEEE SMC, 2018, pp. 1–6.

D. Farkas, S. L. Denham, and I. Winkler, “Functional brain networks underlying idiosyncratic switching patterns in multi-stable auditory perception,” Neuropsychologia, vol. 108, pp. 82–91, Jan. 2018.

A. Landowska, S. Royle, P. Eachus, and D. Roberts, “Testing the Potential of Combining Functional Near-Infrared Spectroscopy with Different Virtual Reality Displays—Oculus Rift and oCtAVE,” in Augmented Reality and Virtual Reality, Springer, Cham, 2018, pp. 309–321. 

S. Perry et al., “Getting to the Root of Fine Motor Skill Performance in Dentistry: Brain Activity During Dental Tasks in a Virtual Reality Haptic Simulation,” J Med Internet Res, vol. 19, no. 12, Dec. 2017.

T.-J. Kim et al., “The effect of dim light at night on cerebral hemodynamic oscillations during sleep: A near-infrared spectroscopy study,” Chronobiology International, vol. 34, no. 10, pp. 1325–1338, Nov. 2017.

K. Pollmann, D. Ziegler, M. Peissner, and M. Vukelić, “A New Experimental Paradigm for Affective Research in Neuro-adaptive Technologies,” 2017, pp. 1–8.

H. Banville, R. Gupta, and T. H. Falk, “Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces,” Computational Intelligence and Neuroscience, vol. 2017, pp. 1–24, 2017.

O. Seidel, D. Carius, R. Kenville, and P. Ragert, “Motor learning in a complex balance task and associated neuroplasticity: a comparison between endurance athletes and nonathletes,” Journal of Neurophysiology, vol. 118, no. 3, pp. 1849–1860, Sep. 2017.

R. Gabbard, M. Fendley, I. A. Dar, R. Warren, and N. H. Kashou, “Utilizing functional near-infrared spectroscopy for prediction of cognitive workload in noisy work environments,” Neurophotonics, vol. 4, no. 04, p. 1, Aug. 2017.

A. Omurtag, H. Aghajani, and H. O. Keles, “Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance,” Journal of Neural Engineering, Jul. 2017.

L. Holper, F. Scholkmann, and E. Seifritz, “Prefrontal hemodynamic after-effects caused by rebreathing may predict affective states – A multimodal functional near-infrared spectroscopy study,” Brain Imaging and Behavior, vol. 11, no. 2, pp. 461–472, Apr. 2017.

J. Shin et al., “Open Access Dataset for EEG+NIRS Single-Trial Classification,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. PP, no. 99, pp. 1–1, 2016.

J. Choe, B. A. Coffman, D. T. Bergstedt, M. D. Ziegler, and M. E. Phillips, “Transcranial Direct Current Stimulation Modulates Neuronal Activity and Learning in Pilot Training,” Front. Hum. Neurosci., vol. 10, 2016.

H. Obrig, J. Mock, F. Stephan, M. Richter, M. Vignotto, and S. Rossi, “Impact of associative word learning on phonotactic processing in 6-month-old infants: A combined EEG and fNIRS study,” Developmental Cognitive Neuroscience.

L.-C. Chen, M. Stropahl, M. Schönwiesner, and S. Debener, “Enhanced visual adaptation in cochlear implant users revealed by concurrent EEG-fNIRS,” Neuroimage, Sep. 2016.

L. Zhu, A. E. Haddad, T. Zeng, Y. Wang, and L. Najafizadeh, “Assessing Optimal Electrode/Optode Arrangement in EEG-fNIRS Multi-Modal Imaging,” in Biomedical Optics 2016, 2016, p. paper–JW3A.

M.-H. Lee, B.-J. Kim, and S.-W. Lee, “Quantifying movement intentions with multimodal neuroimaging for functional electrical stimulation-based rehabilitation,” Neuroreport, vol. 27, no. 2, pp. 61–66, Jan. 2016.

H. O. Keles, R. L. Barbour, and A. Omurtag, “Hemodynamic correlates of spontaneous neural activity measured by human whole-head resting state EEG+fNIRS,” Neuroimage, vol. 138, pp. 76–87, Sep. 2016.

L. Holper, F. Scholkmann, and E. Seifritz, “Prefrontal hemodynamic after-effects caused by rebreathing may predict affective states - A multimodal functional near-infrared spectroscopy study,” Brain Imaging Behav, Mar. 2016.

T. Geall, “Could new ‘Matrix’ hat mean we can learn new skills in no time at all?,” mirror, 29-Feb-2016. [Online]. Available: http://www.mirror.co.uk/news/technology-science/science/scientists-develop-matrix-style-technique-7463286.

D. Carius, C. Andrä, M. Clauß, P. Ragert, M. Bunk, and J. Mehnert, “Hemodynamic Response Alteration As a Function of Task Complexity and Expertise—An fNIRS Study in Jugglers,” Front. Hum. Neurosci, p. 126, 2016.

A. P. Buccino, H. O. Keles, and A. Omurtag, “Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks,” PLOS ONE, vol. 11, no. 1, p. e0146610, Jan. 2016.

N. Altvater-Mackensen and T. Grossmann, “The role of left inferior frontal cortex during audiovisual speech perception in infants,” NeuroImage, vol. 133, pp. 14–20, Jun. 2016.

A. D. Zaidi et al., “Simultaneous epidural functional near-infrared spectroscopy and cortical electrophysiology as a tool for studying local neurovascular coupling in primates,” Neuroimage, vol. 120, pp. 394–399, Oct. 2015.

E. Maggioni et al., “Investigation of negative BOLD responses in human brain through NIRS technique. A visual stimulation study,” NeuroImage, vol. 108, pp. 410–422, Mar. 2015.

M.-H. Lee, S. Fazli, J. Mehnert, and S.-W. Lee, “Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI,” Pattern Recognition, vol. 48, no. 8, pp. 2725–2737, Aug. 2015.

L.-C. Chen, P. Sandmann, J. D. Thorne, C. S. Herrmann, and S. Debener, “Association of Concurrent fNIRS and EEG Signatures in Response to Auditory and Visual Stimuli,” Brain Topogr, vol. 28, no. 5, pp. 710–725, Sep. 2015.

M. Brunetti et al., “Potential determinants of efficacy of mirror therapy in stroke patients--A pilot study,” Restor. Neurol. Neurosci., vol. 33, no. 4, pp. 421–434, 2015.

M. Balconi and M. E. Vanutelli, “Emotions and BIS/BAS components affect brain activity (ERPs and fNIRS) in observing intra-species and inter-species interactions,” Brain Imaging and Behavior, vol. 10, no. 3, pp. 750–760, Aug. 2015.

M. Balconi, E. Grippa, and M. E. Vanutelli, “What hemodynamic (fNIRS), electrophysiological (EEG) and autonomic integrated measures can tell us about emotional processing,” Brain Cogn, vol. 95, pp. 67–76, Apr. 2015.

R. K. Almajidy, Y. Boudria, U. G. Hofmann, W. Besio, and K. Mankodiya, “Multimodal 2D Brain Computer Interface,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 1067–1070.

V. V. Nikulin et al., “Monochromatic ultra-slow (~0.1 Hz) oscillations in the human electroencephalogram and their relation to hemodynamics,” Neuroimage, vol. 97, pp. 71–80, Aug. 2014.

I. M. Kopton and P. Kenning, “Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research,” Front Hum Neurosci, vol. 8, Aug. 2014.

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front Hum Neurosci, vol. 8, p. 244, 2014.

E. Maggioni et al., “Coupling of fMRI and NIRS measurements in the study of negative BOLD response to intermittent photic stimulation,” in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013, pp. 1378–1381.

S. Dähne, F. Bießmann, F. C. Meinecke, J. Mehnert, S. Fazli, and K. R. Müller, “Integration of Multivariate Data Streams With Bandpower Signals,” IEEE Transactions on Multimedia, vol. 15, no. 5, pp. 1001–1013, Aug. 2013.

S. Fazli et al., “Enhanced performance by a hybrid NIRS-EEG brain computer interface,” Neuroimage, vol. 59, no. 1, pp. 519–529, Jan. 2012.

S. Fazli, J. Mehnert, J. Steinbrink, and B. Blankertz, “Using NIRS as a predictor for EEG-based BCI performance,” Conf Proc IEEE Eng Med Biol Soc, vol. 2012, pp. 4911–4914, 2012.

R. L. Barbour et al., “A programmable laboratory testbed in support of evaluation of functional brain activation and connectivity,” IEEE Trans Neural Syst Rehabil Eng, vol. 20, no. 2, pp. 170–183, Mar. 2012.

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