TY - JOUR
T1 - Current state and future prospects of EEG and fNIRS in robot-assisted gait rehabilitation
T2 - A brief review
AU - Berger, Alisa
AU - Horst, Fabian
AU - Müller, Sophia
AU - Steinberg, Fabian
AU - Doppelmayr, Michael
PY - 2019/6/5
Y1 - 2019/6/5
N2 - Gait and balance impairments are frequently considered as the most significant concerns among individuals suffering from neurological diseases. Robot-assisted gait training (RAGT) has shown to be a promising neurorehabilitation intervention to improve gait recovery in patients following stroke or brain injury by potentially initiating neuroplastic changes. However, the neurophysiological processes underlying gait recovery through RAGT remain poorly understood. As non-invasive, portable neuroimaging techniques, electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provide new insights regarding the neurophysiological processes occurring during RAGT by measuring different perspectives of brain activity. Due to spatial information about changes in cortical activation patterns and the rapid temporal resolution of bioelectrical changes, more features correlated with brain activation and connectivity can be identified when using fused EEG-fNIRS, thus leading to a detailed understanding of neurophysiological mechanisms underlying motor behavior and impairments due to neurological diseases. Therefore, multi-modal integrations of EEG-fNIRS appear promising for the characterization of neurovascular coupling in brain network dynamics induced by RAGT. In this brief review, we surveyed neuroimaging studies focusing specifically on robotic gait rehabilitation. While previous studies have examined either EEG or fNIRS with respect to RAGT, a multi-modal integration of both approaches is lacking. Based on comparable studies using fused EEG-fNIRS integrations either for guiding non-invasive brain stimulation or as part of brain-machine interface paradigms, the potential of this methodologically combined approach in RAGT is discussed. Future research directions and perspectives for targeted, individualized gait recovery that optimize the outcome and efficiency of RAGT in neurorehabilitation were further derived.
AB - Gait and balance impairments are frequently considered as the most significant concerns among individuals suffering from neurological diseases. Robot-assisted gait training (RAGT) has shown to be a promising neurorehabilitation intervention to improve gait recovery in patients following stroke or brain injury by potentially initiating neuroplastic changes. However, the neurophysiological processes underlying gait recovery through RAGT remain poorly understood. As non-invasive, portable neuroimaging techniques, electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provide new insights regarding the neurophysiological processes occurring during RAGT by measuring different perspectives of brain activity. Due to spatial information about changes in cortical activation patterns and the rapid temporal resolution of bioelectrical changes, more features correlated with brain activation and connectivity can be identified when using fused EEG-fNIRS, thus leading to a detailed understanding of neurophysiological mechanisms underlying motor behavior and impairments due to neurological diseases. Therefore, multi-modal integrations of EEG-fNIRS appear promising for the characterization of neurovascular coupling in brain network dynamics induced by RAGT. In this brief review, we surveyed neuroimaging studies focusing specifically on robotic gait rehabilitation. While previous studies have examined either EEG or fNIRS with respect to RAGT, a multi-modal integration of both approaches is lacking. Based on comparable studies using fused EEG-fNIRS integrations either for guiding non-invasive brain stimulation or as part of brain-machine interface paradigms, the potential of this methodologically combined approach in RAGT is discussed. Future research directions and perspectives for targeted, individualized gait recovery that optimize the outcome and efficiency of RAGT in neurorehabilitation were further derived.
KW - Brain stimulation
KW - Brain-machine interface
KW - Electroencephalography
KW - Functional near-infrared spectroscopy
KW - Motor recovery
KW - Multi-modal approach
KW - Neurorehabilitation
KW - Robot-assisted gait training
UR - https://resolver.obvsg.at/urn:nbn:at:at-ubs:3-13510
UR - http://www.scopus.com/inward/record.url?scp=85069475774&partnerID=8YFLogxK
UR - https://pubmed.ncbi.nlm.nih.gov/31231200/
UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6561323/
UR - http://www.mendeley.com/research/current-state-future-prospects-eeg-fnirs-robotassisted-gait-rehabilitation-brief-review
U2 - 10.3389/fnhum.2019.00172
DO - 10.3389/fnhum.2019.00172
M3 - Short survey
C2 - 31231200
AN - SCOPUS:85069475774
SN - 1662-5161
VL - 13
JO - Frontiers in Human Neuroscience
JF - Frontiers in Human Neuroscience
M1 - 172
ER -