Delpy DT, Cope M, van der Zee P, Arridge SR, Wray S & Wyatt JS.Estimation of optical pathlength through tissue from direct time of flight measurement. Phys Med Biol 33: 1433−1442

Department of Medical Physics, University College London, UK.
Physics in Medicine and Biology (Impact Factor: 2.76). 01/1989; 33(12):1433-42. DOI: 10.1088/0031-9155/33/12/008
Source: PubMed


Quantitation of near infrared spectroscopic data in a scattering medium such as tissue requires knowledge of the optical pathlength in the medium. This can now be estimated directly from the time of flight of picosecond length light pulses. Monte Carlo modelling of light pulses in tissue has shown that the mean value of the time dispersed light pulse correlates with the pathlength used in quantitative spectroscopic calculations. This result has been verified in a phantom material. Time of flight measurements of pathlength across the rat head give a pathlength of 5.3 +/- 0.3 times the head diameter.

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Available from: John Stephen Wyatt, Jan 04, 2014
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    • "The original fNIRS signals are optical intensity signals of two wavelengths. Thus, the concentration changes of Hb and HbO can be calculated with these optical signals using the modified Beer–Lambert Law, as shown in equation (2), where ε λ1/λ2,Hb/HbO is the extinction coefficient of Hb/HbO under the corresponding wavelength, I λ1/λ2 (t 1 /t 2 ) is the light intensity at times t 1 and t 2 , and DPF is the ratio of the actual path length of the optical photon to the illuminatordetector distance [28]. Values for the differential path length factors (DPF 695 = 6.51, "
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    ABSTRACT: In order to increase the number of states classified by a brain-computer interface (BCI), we utilized a motor imagery task where subjects imagined both force and speed of hand clenching. The BCI utilized simultaneously recorded electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The time-phase-frequency feature was extracted from EEG, whereas the HbD [the difference of oxy-hemoglobin (HbO) and deoxy-hemoglobin (Hb)] feature was used to improve the classification accuracy of fNIRS. The EEG and fNIRS features were combined and optimized using the joint mutual information (JMI) feature selection criterion; then the extracted features were classified with the extreme learning machines (ELMs). In this study, the averaged classification accuracy of EEG signals achieved by the time-phase-frequency feature improved by 7%, to 18%, more than the single-type feature, and improved by 15% more than common spatial pattern (CSP) feature. The HbD feature of fNIRS signals improved the accuracy by 1%, to 4%, more than Hb, HbO, or HbT (total hemoglobin). The EEG-fNIRS feature for decoding motor imagery of both force and speed of hand clenching achieved an accuracy of 89% ± 2%, and improved the accuracy by 1% to 5% more than the sole EEG or fNIRS feature. Our novel motor imagery paradigm improves BCI performance by increasing the number of extracted commands. Both the time-phase-frequency and the HbD feature improve the classification accuracy of EEG and fNIRS signals, respectively, and the hybrid EEG-fNIRS technique achieves a higher decoding accuracy for two-class motor imagery, which may provide the framework for future multi-modal online BCI systems.
    Journal of Neural Engineering 04/2015; 12(3):036004. DOI:10.1088/1741-2560/12/3/036004 · 3.30 Impact Factor
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    • "Each participant's hemodynamic response was baseline-corrected by subtracting the mean intensity of the optical signal recorded during the 15 s from the overall hemodynamic activity. The modified Beer-Lambert equation (mBL; Delpy et al. 1988) was used to convert wavelength data to oxy-and deoxy-hemoglobin concentrations (designated as HbO and Hb, respectively, see Huppert et al. 2009, for details). Following the conversion, the time course data (all channels, all conditions) for each participant were plotted in Matlab and visually inspected for motion artifacts and signal quality; blocks containing signal changes that occurred too rapidly to be physiological (3 s or less) were removed across all channels from further analyses. "
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    ABSTRACT: Previous work shows that as compared to European Americans, Asians are more holistic (or less focused) in perceptual processing. Drawing on this evidence, we expected that extra attention control would be recruited to perform focused processing for Asians and holistic processing for European Americans. Eight Asian American and 13 European American young adults judged whether a given framed-line was the same in length as the framed-line shown on the previous trial on the basis of either an absolute or relative criterion. Their brain activities were monitored with functional near infrared spectroscopy. As predicted, Asian Americans showed an increased activation in the parietal regions during the absolute (vs. relative) task, whereas European Americans showed an increased activation in the same regions during the relative (vs. absolute) task. No comparable pattern was observed for performance. The current work provides further evidence for compensatory attention that is engaged during culturally non-preferred tasks.
    02/2015; 3(1). DOI:10.1007/s40167-015-0027-y
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    • "These exited photons are then detected by using strategically positioned detectors . Since HbO and HbR have different absorption coefficients for different wavelengths of NI light, the relationship between the exiting-photon intensity and the incident-photon intensity can be used to calculate the changes of the concentrations of HbO and HbR [c HbO (t) and c HbR (t)] along the path of the photons by applying the modified Beer-Lamberts law (Delpy et al., 1988). "
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    ABSTRACT: brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis, multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine, hidden Markov model, artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.
    Frontiers in Human Neuroscience 01/2015; DOI:10.3389/fnhum.2015.00003 · 2.99 Impact Factor
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