Literature DB >> 27849544

Imagined Hand Clenching Force and Speed Modulate Brain Activity and Are Classified by NIRS Combined With EEG.

Yunfa Fu, Xin Xiong, Changhao Jiang, Baolei Xu, Yongcheng Li, Hongyi Li.   

Abstract

Simultaneous acquisition of brain activity signals from the sensorimotor area using NIRS combined with EEG, imagined hand clenching force and speed modulation of brain activity, as well as 6-class classification of these imagined motor parameters by NIRS-EEG were explored. Near infrared probes were aligned with C3 and C4, and EEG electrodes were placed midway between the NIRS probes. NIRS and EEG signals were acquired from six healthy subjects during six imagined hand clenching force and speed tasks involving the right hand. The results showed that NIRS combined with EEG is effective for simultaneously measuring brain activity of the sensorimotor area. The study also showed that in the duration of (0, 10) s for imagined force and speed of hand clenching, HbO first exhibited a negative variation trend, which was followed by a negative peak. After the negative peak, it exhibited a positive variation trend with a positive peak about 6-8 s after termination of imagined movement. During (-2, 1) s, the EEG may have indicated neural processing during the preparation, execution, and monitoring of a given imagined force and speed of hand clenching. The instantaneous phase, frequency, and amplitude feature of the EEG were calculated by Hilbert transform; HbO and the difference between HbO and Hb concentrations were extracted. The features of NIRS and EEG were combined to classify three levels of imagined force [at 20/50/80% MVGF (maximum voluntary grip force)] and speed (at 0.5/1/2 Hz) of hand clenching by SVM. The average classification accuracy of the NIRS-EEG fusion feature was 0.74 ± 0.02. These results may provide increased control commands of force and speed for a brain-controlled robot based on NIRS-EEG.

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Year:  2016        PMID: 27849544     DOI: 10.1109/TNSRE.2016.2627809

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  7 in total

1.  Single-trial motor imagery electroencephalogram intention recognition by optimal discriminant hyperplane and interpretable discriminative rectangle mixture model.

Authors:  Rongrong Fu; Dong Xu; Weishuai Li; Peiming Shi
Journal:  Cogn Neurodyn       Date:  2022-01-29       Impact factor: 3.473

2.  Resting-State NIRS-EEG in Unresponsive Patients with Acute Brain Injury: A Proof-of-Concept Study.

Authors:  Marwan H Othman; Mahasweta Bhattacharya; Kirsten Møller; Søren Kjeldsen; Johannes Grand; Jesper Kjaergaard; Anirban Dutta; Daniel Kondziella
Journal:  Neurocrit Care       Date:  2021-02       Impact factor: 3.210

3.  Decoding of Walking Imagery and Idle State Using Sparse Representation Based on fNIRS.

Authors:  Hongquan Li; Anmin Gong; Lei Zhao; Wei Zhang; Fawang Wang; Yunfa Fu
Journal:  Comput Intell Neurosci       Date:  2021-02-22

4.  Motor Imagination of Lower Limb Movements at Different Frequencies.

Authors:  Yingtao Liu; Chao Chen; Abdelkader Nasreddine Belkacem; Zhiyong Wang; Longlong Cheng; Chun Wang; Yuexiao Chang; Penghai Li
Journal:  J Healthc Eng       Date:  2021-12-22       Impact factor: 2.682

5.  Acupuncture enhances brain function in patients with mild cognitive impairment: evidence from a functional-near infrared spectroscopy study.

Authors:  M N Afzal Khan; Usman Ghafoor; Ho-Ryong Yoo; Keum-Shik Hong
Journal:  Neural Regen Res       Date:  2022-08       Impact factor: 5.135

Review 6.  Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research.

Authors:  Patrick W Dans; Stevie D Foglia; Aimee J Nelson
Journal:  Brain Sci       Date:  2021-05-09

7.  Single-Trial Recognition of Imagined Forces and Speeds of Hand Clenching Based on Brain Topography and Brain Network.

Authors:  Xin Xiong; Yunfa Fu; Jian Chen; Lijun Liu; Xiabing Zhang
Journal:  Brain Topogr       Date:  2018-12-31       Impact factor: 3.020

  7 in total

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