BCI Monitor Enhances Electroencephalographic and Cerebral Hemodynamic Activations During Motor Training

Wang, Zhongpeng; Zhou, Yijie; Chen, Long; Gu, Bin; Yi, Weibo; Liu, Shuang; Xu, Minpeng; Qi, Hongzhi; He, Feng; Ming, Dong

doi:10.1109/tnsre.2019.2903685

Cited by 36 publications

(29 citation statements)

References 40 publications

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“…A wearable, wireless, continuous-wave fNIRS system (Nirsmart, Danyang Huichuang Medical Equipment Co, Ltd, China) [26] was used to monitor the concentration change of oxygenated hemoglobin ( HbO 2 ). The wavelength of the near-infrared light was 760nm, and the sampling rate was 10Hz.…”

Section: Functional Near-infrared Spectroscopymentioning

confidence: 99%

Quantified Assessment of Deep Brain Stimulation on Parkinson's Patients with Task fNIRS Measurements and Functional Connectivity Analysis: A Pilot Study

Liang

et al. 2020

Preprint

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Background: Deep brain stimulation (DBS) has proved effective for Parkinsons disease (PD), but the identification of stimulation parameters relies on doctors’ subjective judgment on patient behavior.Methods: Five PD patients performed 10-meter walking tasks under different brain stimulation frequencies. During walking tests, a wearable functional near-infrared spectroscopy (fNIRS) system was used to measure the concentration change of oxygenated hemoglobin (ΔHbO2) in prefrontal cortex, parietal lobe and occipital lobe. Brain functional connectivity and global efficiency were calculated to quantify the brain activities.Results: We discovered that both the global and regional brain efficiency of all patients varied with stimulation parameters, and the DBS pattern enabling the highest brain efficiency was optimal for each patient, in accordance with the clinical assessments and DBS treatment decision made by the doctors.Conclusions: Task fNIRS assessments and brain functional connectivity analysis promise a quantified and objective solution for patient-specific optimization of DBS treatment.Trial registration: The study was approved by the Ethical Committee of Tianjin Huanhu Hospital (2019-35), and has been registered in Chinese Clinical Trial Registry (ChiCTR1900022715).

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Section: Functional Near-infrared Spectroscopymentioning

confidence: 99%

Quantified Assessment of Deep Brain Stimulation on Parkinson's Patients with Task fNIRS Measurements and Functional Connectivity Analysis: A Pilot Study

Liang

et al. 2020

Preprint

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“…By collecting fNIRS signal, Wang et al classified the grasping motion imagination of the right hand. The classification accuracy was 80.21 ± 6.7%, and the classification method was SVM [13]. One common fact of the above papers is that only the fNIRS signal is used for MI.…”

Section: Introductionmentioning

confidence: 99%

Fugl-Meyer hand motor imagination recognition for brain–computer interfaces using only fNIRS

Hong-jun

Cheng

2021

Complex Intell. Syst.

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As a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

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“…In the time-domain, the time-correlated single-photon counting technique has been investigated by researchers [23]. Regarding fNIRS signal processing, many studies argued that, besides the conventional averaging [24,25], the deconvolution method and an integrated framework method effectively extracted the event-related hemodynamic responses from the overall hemoglobin changes [25,26]. However, these methods [24][25][26] focused on filtering methods of the hemoglobin signals obtained from optical intensities rather than optical intensities' conversion process to hemoglobin signals.…”

Section: Introductionmentioning

confidence: 99%

Multi-Channel-Based Differential Pathlength Factor Estimation for Continuous-Wave fNIRS

Huang

Hong

2021

IEEE Access

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Functional near-infrared spectroscopy (fNIRS) in brain imaging needs to be robust to subjectwise variability. The use of a fixed differential pathlength factor (DPF) per wavelength for the entire brain will degrade the accuracy of hemodynamic responses. Since the tissue composition varies within the brain, correct DPF values should be used for various emitter-detector distances and brain regions. In this paper, a DPF estimation method combining a state-space model of the modified Beer-Lambert law (mBLL), a parameter model for estimating the reduced scattering coefficients, and dual square-root cubature Kalman filters (SCKFs) is proposed. To validate the proposed method, known light intensities (six channels, two wavelengths) and reference DPFs are generated using NIRFAST (a Matlab toolbox) using a presumed paradigm, known tissue properties, a Balloon model, and a finite element head model consisting of 58,818 mesh elements. Then, the DPF values are estimated using a Jacobian matrix from the head model and the mBLL. The results show that the estimated concentration changes correlate well with the reference data. Also, the estimated DPFs showed relative errors less than 1.33% maximum and 0.75% on average. A onetailed t-test revealed that the estimated DPFs matched the reference DPFs with more than 99.9% confidence. The developed method can efficiently access the actual DPFs even if emitter-detector distances vary significantly and the tissue properties are not uniform. With the developed state-space models for dual SCKFs, real-time estimation of the DPFs from one experiment to another has become plausible.INDEX TERMS Differential pathlength factor (DPF), functional near-infrared spectroscopy (fNIRS), Kalman filter, state-space method, finite element method (FEM)

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BCI Monitor Enhances Electroencephalographic and Cerebral Hemodynamic Activations During Motor Training

Cited by 36 publications

References 40 publications

Quantified Assessment of Deep Brain Stimulation on Parkinson's Patients with Task fNIRS Measurements and Functional Connectivity Analysis: A Pilot Study

Quantified Assessment of Deep Brain Stimulation on Parkinson's Patients with Task fNIRS Measurements and Functional Connectivity Analysis: A Pilot Study

Fugl-Meyer hand motor imagination recognition for brain–computer interfaces using only fNIRS

Multi-Channel-Based Differential Pathlength Factor Estimation for Continuous-Wave fNIRS

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