Deep source transfer learning for the estimation of internal brain dynamics using scalp EEG

Electroencephalography (EEG) provides high temporal resolution neural data for brain-computer interfacing via noninvasive electrophysiological recording. Estimating the internal brain activity by means of source imaging techniques can further improve the spatial resolution of EEG and enhance the rel...

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Published in:	Cognitive neurodynamics Vol. 18; no. 6; pp. 3507 - 3520
Main Authors:	Yu, Haitao, Hu, Zhiwen, Zhao, Quanfa, Liu, Jing
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01.12.2024 Springer Nature B.V
Subjects:	Accuracy Acupuncture Algorithms Artificial Intelligence Biochemistry Biochips Biomedical and Life Sciences Biomedicine Brain Cognitive Psychology Computer applications Computer Science Decoding Deep learning Dynamics EEG Electroencephalography Electrophysiological recording Epilepsy Estimates Estimation Human-computer interface Imaging techniques Implants Information processing Localization Machine learning Medical imaging Neural coding Neural networks Neuroimaging Neurosciences Recurrent neural networks Research Article Seizures Sensory integration Spatial discrimination learning Spatial resolution Spatiotemporal data Stimulation Temporal resolution Thalamic nuclei Thalamus Transfer learning Deep source imaging Transfer learning Thalamocortical dynamics EEG Brain model
ISSN:	1871-4080, 1871-4099
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Abstract	Electroencephalography (EEG) provides high temporal resolution neural data for brain-computer interfacing via noninvasive electrophysiological recording. Estimating the internal brain activity by means of source imaging techniques can further improve the spatial resolution of EEG and enhance the reliability of neural decoding and brain-computer interaction. In this work, we propose a novel EEG data-driven source imaging scheme for precise and efficient estimation of macroscale spatiotemporal brain dynamics across thalamus and cortical regions with deep learning methods. A deep source imaging framework with a convolutional-recurrent neural network is designed to estimate the internal brain dynamics from high-density EEG recordings. Moreover, a brain model including 210 cortical regions and 16 thalamic nuclei is established based on human brain connectome to provide synthetic training data, which manifests intrinsic characteristics of underlying brain dynamics in spontaneous, stimulation-evoked, and pathological states. Transfer learning algorithm is further applied to the trained network to reduce the dynamical differences between synthetic and realistic EEG. Extensive experiments exhibit that the proposed deep-learning method can accurately estimate the spatial and temporal activity of brain sources and achieves superior performance compared to the state-of-the-art approaches. Moreover, the EEG data-driven source imaging framework is effective in the location of seizure onset zone in epilepsy and reconstruction of dynamical thalamocortical interactions during sensory processing of acupuncture stimulation, implying its applicability in brain-computer interfacing for neuroscience research and clinical applications.
AbstractList	Electroencephalography (EEG) provides high temporal resolution neural data for brain-computer interfacing via noninvasive electrophysiological recording. Estimating the internal brain activity by means of source imaging techniques can further improve the spatial resolution of EEG and enhance the reliability of neural decoding and brain-computer interaction. In this work, we propose a novel EEG data-driven source imaging scheme for precise and efficient estimation of macroscale spatiotemporal brain dynamics across thalamus and cortical regions with deep learning methods. A deep source imaging framework with a convolutional-recurrent neural network is designed to estimate the internal brain dynamics from high-density EEG recordings. Moreover, a brain model including 210 cortical regions and 16 thalamic nuclei is established based on human brain connectome to provide synthetic training data, which manifests intrinsic characteristics of underlying brain dynamics in spontaneous, stimulation-evoked, and pathological states. Transfer learning algorithm is further applied to the trained network to reduce the dynamical differences between synthetic and realistic EEG. Extensive experiments exhibit that the proposed deep-learning method can accurately estimate the spatial and temporal activity of brain sources and achieves superior performance compared to the state-of-the-art approaches. Moreover, the EEG data-driven source imaging framework is effective in the location of seizure onset zone in epilepsy and reconstruction of dynamical thalamocortical interactions during sensory processing of acupuncture stimulation, implying its applicability in brain-computer interfacing for neuroscience research and clinical applications.Electroencephalography (EEG) provides high temporal resolution neural data for brain-computer interfacing via noninvasive electrophysiological recording. Estimating the internal brain activity by means of source imaging techniques can further improve the spatial resolution of EEG and enhance the reliability of neural decoding and brain-computer interaction. In this work, we propose a novel EEG data-driven source imaging scheme for precise and efficient estimation of macroscale spatiotemporal brain dynamics across thalamus and cortical regions with deep learning methods. A deep source imaging framework with a convolutional-recurrent neural network is designed to estimate the internal brain dynamics from high-density EEG recordings. Moreover, a brain model including 210 cortical regions and 16 thalamic nuclei is established based on human brain connectome to provide synthetic training data, which manifests intrinsic characteristics of underlying brain dynamics in spontaneous, stimulation-evoked, and pathological states. Transfer learning algorithm is further applied to the trained network to reduce the dynamical differences between synthetic and realistic EEG. Extensive experiments exhibit that the proposed deep-learning method can accurately estimate the spatial and temporal activity of brain sources and achieves superior performance compared to the state-of-the-art approaches. Moreover, the EEG data-driven source imaging framework is effective in the location of seizure onset zone in epilepsy and reconstruction of dynamical thalamocortical interactions during sensory processing of acupuncture stimulation, implying its applicability in brain-computer interfacing for neuroscience research and clinical applications. Electroencephalography (EEG) provides high temporal resolution neural data for brain-computer interfacing via noninvasive electrophysiological recording. Estimating the internal brain activity by means of source imaging techniques can further improve the spatial resolution of EEG and enhance the reliability of neural decoding and brain-computer interaction. In this work, we propose a novel EEG data-driven source imaging scheme for precise and efficient estimation of macroscale spatiotemporal brain dynamics across thalamus and cortical regions with deep learning methods. A deep source imaging framework with a convolutional-recurrent neural network is designed to estimate the internal brain dynamics from high-density EEG recordings. Moreover, a brain model including 210 cortical regions and 16 thalamic nuclei is established based on human brain connectome to provide synthetic training data, which manifests intrinsic characteristics of underlying brain dynamics in spontaneous, stimulation-evoked, and pathological states. Transfer learning algorithm is further applied to the trained network to reduce the dynamical differences between synthetic and realistic EEG. Extensive experiments exhibit that the proposed deep-learning method can accurately estimate the spatial and temporal activity of brain sources and achieves superior performance compared to the state-of-the-art approaches. Moreover, the EEG data-driven source imaging framework is effective in the location of seizure onset zone in epilepsy and reconstruction of dynamical thalamocortical interactions during sensory processing of acupuncture stimulation, implying its applicability in brain-computer interfacing for neuroscience research and clinical applications.
Author	Zhao, Quanfa Liu, Jing Yu, Haitao Hu, Zhiwen
Author_xml	– sequence: 1 givenname: Haitao surname: Yu fullname: Yu, Haitao organization: School of Electrical and Information Engineering, Tianjin University – sequence: 2 givenname: Zhiwen surname: Hu fullname: Hu, Zhiwen organization: School of Electrical and Information Engineering, Tianjin University – sequence: 3 givenname: Quanfa surname: Zhao fullname: Zhao, Quanfa organization: School of Electrical and Information Engineering, Tianjin University – sequence: 4 givenname: Jing surname: Liu fullname: Liu, Jing email: angel.jsea@163.com organization: Department of Neurology, Tangshan Gongren Hospital
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Keywords	Deep source imaging Transfer learning Thalamocortical dynamics EEG Brain model
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SubjectTerms	Accuracy Acupuncture Algorithms Artificial Intelligence Biochemistry Biochips Biomedical and Life Sciences Biomedicine Brain Cognitive Psychology Computer applications Computer Science Decoding Deep learning Dynamics EEG Electroencephalography Electrophysiological recording Epilepsy Estimates Estimation Human-computer interface Imaging techniques Implants Information processing Localization Machine learning Medical imaging Neural coding Neural networks Neuroimaging Neurosciences Recurrent neural networks Research Article Seizures Sensory integration Spatial discrimination learning Spatial resolution Spatiotemporal data Stimulation Temporal resolution Thalamic nuclei Thalamus Transfer learning
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Title	Deep source transfer learning for the estimation of internal brain dynamics using scalp EEG
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