TY - JOUR
T1 - Neural Activity and Decoding of Action Observation Using Combined EEG and fNIRS Measurement
AU - Ge, Sheng
AU - Wang, Peng
AU - Liu, Hui
AU - Lin, Pan
AU - Gao, Junfeng
AU - Wang, Ruimin
AU - Iramina, Keiji
AU - Zhang, Quan
AU - Zheng, Wenming
N1 - Funding Information:
We thank Benjamin Knight, MSc., for editing the English text of this manuscript draft. Funding. This work was supported in part by the National Basic Research Program of China under Grant 2015CB351704, the National Nature Science Foundation of China (61473221, 61773408 and 61921004), and in part by the Fundamental Research Funds for the Central Universities of China (CZY18047).
Publisher Copyright:
© Copyright © 2019 Ge, Wang, Liu, Lin, Gao, Wang, Iramina, Zhang and Zheng.
PY - 2019/10/15
Y1 - 2019/10/15
N2 - In a social world, observing the actions of others is fundamental to understanding what they are doing, as well as their intentions and feelings. Studies of the neural basis and decoding of action observation are important for understanding action-related processes and have implications for cognitive, social neuroscience, and human-machine interaction (HMI). In the current study, we first investigated temporal-spatial dynamics during action observation using a combined 64-channel electroencephalography (EEG) and 48-channel functional near-infrared spectroscopy (fNIRS) system. We measured brain activation while 16 healthy participants observed three action tasks: (1) grasping a cup with the intention of drinking; (2) grasping a cup with the intention of moving it; and (3) touching a cup with an unclear intention. The EEG and fNIRS source analysis results revealed the dynamic involvement of both the mirror neuron system (MNS) and the theory of mind (ToM)/mentalizing network during action observation. The source analysis results suggested that the extent to which these two systems were engaged was determined by the clarity of the intention of the observed action. Based on the difference in neural activity observed among different action-observation tasks in the first experiment, we conducted a second experiment to classify the neural processes underlying action observation using a feature classification method. We constructed complex brain networks based on the EEG and fNIRS data. Fusing features from both EEG and fNIRS complex brain networks resulted in a classification accuracy of 72.7% for the three action observation tasks. This study provides a theoretical and empirical basis for elucidating the neural mechanisms of action observation and intention understanding, and a feasible method for decoding the underlying neural processes.
AB - In a social world, observing the actions of others is fundamental to understanding what they are doing, as well as their intentions and feelings. Studies of the neural basis and decoding of action observation are important for understanding action-related processes and have implications for cognitive, social neuroscience, and human-machine interaction (HMI). In the current study, we first investigated temporal-spatial dynamics during action observation using a combined 64-channel electroencephalography (EEG) and 48-channel functional near-infrared spectroscopy (fNIRS) system. We measured brain activation while 16 healthy participants observed three action tasks: (1) grasping a cup with the intention of drinking; (2) grasping a cup with the intention of moving it; and (3) touching a cup with an unclear intention. The EEG and fNIRS source analysis results revealed the dynamic involvement of both the mirror neuron system (MNS) and the theory of mind (ToM)/mentalizing network during action observation. The source analysis results suggested that the extent to which these two systems were engaged was determined by the clarity of the intention of the observed action. Based on the difference in neural activity observed among different action-observation tasks in the first experiment, we conducted a second experiment to classify the neural processes underlying action observation using a feature classification method. We constructed complex brain networks based on the EEG and fNIRS data. Fusing features from both EEG and fNIRS complex brain networks resulted in a classification accuracy of 72.7% for the three action observation tasks. This study provides a theoretical and empirical basis for elucidating the neural mechanisms of action observation and intention understanding, and a feasible method for decoding the underlying neural processes.
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U2 - 10.3389/fnhum.2019.00357
DO - 10.3389/fnhum.2019.00357
M3 - Article
AN - SCOPUS:85074302187
SN - 1662-5161
VL - 13
JO - Frontiers in Human Neuroscience
JF - Frontiers in Human Neuroscience
M1 - 357
ER -