@article{Rybar2021,

title = {Decoding of semantic categories of imagined concepts of animals and tools in fNIRS},

author = {Milan Rybar and Riccardo Poli and Ian Daly},

year  = {2021},

date = {2021-03-08},

journal = {Journal of Neural Engineering},

keywords = {BCI, fNIRS, Semantic decoding},

pubstate = {published},

tppubtype = {article}

}

@article{Bauernfeind2014,

title = {Separating heart and brain: On the reduction of physiological noise from multichannel functional near-infrared spectroscopy (fNIRS) signals},

author = {Gunther Bauernfeind and Selina Wriessnegger and Ian Daly and Gernot Müller-Putz},

doi = {10.1088/1741-2560/11/5/056010},

year  = {2014},

date = {2014-09-11},

journal = {Journal of Neural Engineering},

volume = {11},

number = {5},

pages = {1-18},

abstract = {Objective. Functional near-infrared spectroscopy (fNIRS) is an emerging technique for the in vivo assessment of functional activity of the cerebral cortex as well as in the field of brain–computer interface (BCI) research. A common challenge for the utilization of fNIRS in these areas is a stable and reliable investigation of the spatio-temporal hemodynamic patterns. However, the recorded patterns may be influenced and superimposed by signals generated from physiological processes, resulting in an inaccurate estimation of the cortical activity. Up to now only a few studies have investigated these influences, and still less has been attempted to remove/reduce these influences. The present study aims to gain insights into the reduction of physiological rhythms in hemodynamic signals (oxygenated hemoglobin (oxy-Hb), deoxygenated hemoglobin (deoxy-Hb)). Approach. We introduce the use of three different signal processing approaches (spatial filtering, a common average reference (CAR) method; independent component analysis (ICA); and transfer function (TF) models) to reduce the influence of respiratory and blood pressure (BP) rhythms on the hemodynamic responses. Main results. All approaches produce large reductions in BP and respiration influences on the oxy-Hb signals and, therefore, improve the contrast-to-noise ratio (CNR). In contrast, for deoxy-Hb signals CAR and ICA did not improve the CNR. However, for the TF approach, a CNR-improvement in deoxy-Hb can also be found. Significance. The present study investigates the application of different signal processing approaches to reduce the influences of physiological rhythms on the hemodynamic responses. In addition to the identification of the best signal processing method, we also show the importance of noise reduction in fNIRS data.},

keywords = {Artefact removal, fNIRS, ICA, Mayer wave},

pubstate = {published},

tppubtype = {article}

}

@article{Pfurtscheller2012a,

title = {Coupling between intrinsic prefrontal HbO2 and central EEG beta power oscillations in the resting brain},

author = {Gert Pfurtscheller and Ian Daly and Gunther Bauernfeind and Gernot R. Muller-Putz},

url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/journal.pone_.0043640.pdf},

doi = {10.1371/journal.pone.0043640},

year  = {2012},

date = {2012-08-24},

journal = {PLOS One},

volume = {7},

number = {8},

pages = {1-9},

abstract = {There is increasing interest in the intrinsic activity in the resting brain, especially that of ultraslow and slow oscillations. Using near-infrared spectroscopy (NIRS), electroencephalography (EEG), blood pressure (BP), respiration and heart rate recordings during 5 minutes of rest, combined with cross spectral and sliding cross correlation calculations, we identified a short-lasting coupling (duration  100s) between prefrontal oxyhemoglobin (HbO2) in the frequency band between 0.07 and 0.13 Hz and central EEG alpha and/or beta power oscillations in 8 of the 9 subjects investigated. The HbO2 peaks preceded the EEG band power peaks by 3.7 s in 6 subjects, with moderate or no coupling between BP and HbO2 oscillations. HbO2 and EEG band power oscillations were approximately in phase with BP oscillations in the 2 subjects with an extremely high coupling (squared coherence >0.8) between BP and HbO2 oscillation. No coupling was identified in one subject. These results indicate that slow precentral (de)oxyhemoglobin concentration oscillations during awake rest can be temporarily coupled with EEG fluctuations in sensorimotor areas and modulate the excitability level in the brains’ motor areas, respectively. Therefore, this provides support for the idea that resting state networks fluctuate with frequencies of between 0.01 and 0.1 Hz (Mantini et.al. PNAS 2007).},

keywords = {Beta coupling, EEG, fNIRS, Resting state},

pubstate = {published},

tppubtype = {article}

}