Given its great performance in the presence of muscle movements and the possibility of setting up measurements in realistic environments, fNIRS presents itself as an ideal candidate for the acquisition of cortical signals as reliable and representative inputs for Brain-Computer Interface investigations.
NIRx Webinar fNIRS based Brain computer Interfaces - Youtube
*We are excited to have Dr. Noman Naseer as invited speaker for this special edition webinar on fNIRS-based Brain-computer Interfaces. With over 50 peer-reviewed publications in the field of fNIRS-based BCI, Dr. Naseer will share his expertise on signal acquisition and processing for fNIRS-BCI. A practical demonstration of raw data analysis, feature extraction and classification using Matlab will also be given.
K. Li et al., “Functional Near-Infrared Spectroscopy (fNIRS) informed neurofeedback: regional-specific modulation of lateral orbitofrontal activation and cognitive flexibility,” bioRxiv, p. 511824, Jan. 2019.
L. R. Trambaiolli, C. E. Biazoli, A. M. Cravo, T. H. Falk, and J. R. Sato, “Functional near-infrared spectroscopy-based affective neurofeedback: feedback effect, illiteracy phenomena, and whole-connectivity profiles,” NPh, vol. 5, no. 3, p. 035009, Sep. 2018.
A. Janani and M. Sasikala, “Evaluation of classification performance of functional near infrared spectroscopy signals during movement execution for developing a brain-computer interface application using optimal channels,” J. Near Infrared Spectrosc., JNIRS, vol. 26, no. 4, pp. 209–221, Aug. 2018.
S. E. Kober, V. Hinterleitner, G. Bauernfeind, C. Neuper, and G. Wood, “Trainability of hemodynamic parameters: A near-infrared spectroscopy based neurofeedback study,” Biological Psychology, vol. 136, pp. 168–180, 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.
J. Shin, K.-R. Müller, and H.-J. Hwang, “Eyes-closed hybrid brain-computer interface employing frontal brain activation,” PLOS ONE, vol. 13, no. 5, p. e0196359, May 2018.
R. A. Khan, N. Naseer, N. K. Qureshi, F. M. Noori, H. Nazeer, and M. U. Khan, “fNIRS-based Neurorobotic Interface for gait rehabilitation,” Journal of NeuroEngineering and Rehabilitation, vol. 15, no. 1, p. 7, Feb. 2018.
A. Janani and M. Sasikala, “Classification of fNIRS Signals for Decoding Right- and Left-Arm Movement Execution Using SVM for BCI Applications,” in Computational Signal Processing and Analysis, 2018, pp. 315–323.
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.
K. J. Verdière, R. N. Roy, and F. Dehais, “Detecting Pilot’s Engagement Using fNIRS Connectivity Features in an Automated vs. Manual Landing Scenario,” Frontiers in Human Neuroscience, vol. 12, Jan. 2018.
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.
M. Lührs and R. Goebel, “Turbo-Satori: a neurofeedback and brain–computer interface toolbox for real-time functional near-infrared spectroscopy,” Neurophotonics, vol. 4, no. 04, p. 1, Oct. 2017.
H. Aghajani, M. Garbey, and A. Omurtag, “Measuring Mental Workload with EEG+fNIRS,” Frontiers in Human Neuroscience, vol. 11, Jul. 2017.
N. K. Qureshi, N. Naseer, F. M. Noori, H. Nazeer, R. A. Khan, and S. Saleem, “Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients,” Frontiers in Neurorobotics, vol. 11, Jul. 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.
F. M. Noori, N. Naseer, N. K. Qureshi, H. Nazeer, and R. A. Khan, “Optimal feature selection from fNIRS signals using genetic algorithms for BCI,” Neuroscience Letters, vol. 647, pp. 61–66, Apr. 2017.
M. Abtahi, A. Amiri, D. Byrd, and K. Mankodiya, “Hand Motion Detection in fNIRS Neuroimaging Data,” Healthcare, vol. 5, no. 2, p. 20, 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. Shin, K.-R. Müller, and H.-J. Hwang, “Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic,” Scientific Reports, vol. 6, p. 36203, Nov. 2016.
H. Aghajani and A. Omurtag, “Assessment of mental workload by EEG+FNIRS,” 2016, pp. 3773–3776.
N. Naseer, F. M. Noori, N. K. Qureshi, and K.-S. Hong, “Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application,” Front. Hum. Neurosci, p. 237, 2016.
K.-S. Hong and H. Santosa, “Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy,” Hearing Research, vol. 333, pp. 157–166, Mar. 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.
K. Tumanov, R. Goebel, R. Möckel, B. Sorger, and G. Weiss, “fNIRS-based BCI for Robot Control,” in Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, Richland, SC, 2015, pp. 1953–1954.
N. Naseer and K.-S. Hong, “Decoding answers to four-choice questions using functional near infrared spectroscopy,” J. Near Infrared Spectrosc, vol. 23, no. 1, pp. 23–31, 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.
M. J. Khan and K.-S. Hong, “Passive BCI based on drowsiness detection: an fNIRS study,” Biomed Opt Express, vol. 6, no. 10, pp. 4063–4078, Oct. 2015.
K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS–BCI,” Neuroscience Letters, vol. 587, pp. 87–92, Feb. 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.
W. Guo, P. Yao, X. Sheng, H. Liu, and X. Zhu, “A wireless wearable sEMG and NIRS acquisition system for an enhanced human-computer interface,” in 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014, pp. 2192–2197.
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.
C.-H. Chen, M.-S. Ho, K.-K. Shyu, K.-C. Hsu, K.-W. Wang, and P.-L. Lee, “A noninvasive brain computer interface using visually-induced near-infrared spectroscopy responses,” Neuroscience Letters, vol. 580, pp. 22–26, Sep. 2014.
X. Shu, L. Yao, X. Sheng, D. Zhang, and X. Zhu, “A hybrid BCI study: Temporal optimization for EEG single-trial classification by exploring hemodynamics from the simultaneously measured NIRS data,” in 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), 2014, pp. 914–918.
N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface,” Exp Brain Res, vol. 232, no. 2, pp. 555–564, Nov. 2013.
M. M. DiStasio and J. T. Francis, “Use of frontal lobe hemodynamics as reinforcement signals to an adaptive controller,” PLoS ONE, vol. 8, no. 7, p. e69541, 2013.
N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain?computer interface,” Neuroscience Letters, vol. 553, pp. 84–89, Oct. 2013.
S. Waldert, L. Tüshaus, C. P. Kaller, A. Aertsen, and C. Mehring, “fNIRS Exhibits Weak Tuning to Hand Movement Direction,” PLOS ONE, vol. 7, no. 11, p. e49266, Nov. 2012.
X.-S. Hu, K.-S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J Neural Eng, vol. 9, no. 2, p. 26012, Apr. 2012.
C. Herff, F. Putze, D. Heger, C. Guan, and T. Schultz, “Speaking mode recognition from functional Near Infrared Spectroscopy,” Conf Proc IEEE Eng Med Biol Soc, vol. 2012, pp. 1715–1718, 2012.
S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K.-R. Müller, and B. Blankertz, “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.
K. K. Ang, J. Yu, and C. Guan, “Extracting effective features from high density nirs-based BCI for assessing numerical cognition,” 2012, pp. 2233–2236.
V. Gottemukkula and R. Derakhshani, “Classification-guided feature selection for NIRS-based BCI,” in 2011 5th International IEEE/EMBS Conference on Neural Engineering (NER), 2011, pp. 72–75.
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