Query: Capsule Network motor

title: A deep learning method for motor fault diagnosis based on a capsule network with gate structure dilated convolutions
abstract: Motor fault diagnosis is critical to predictive maintenance of electrical motor condition monitoring, whereas the conventional motor fault diagnosis method cannot effectively diagnose conditions caused by motors' complex structure, non stationary signals and mechanical big data. To solve the mentioned problems and enhance the fault diagnostic accuracy and generalization performance under different actual motor conditions, this study proposes an efficient, noise resistant, end to end deep learning algorithm based on a novel capsule network with gate structure dilated convolutions (GDCCN) for motor fault diagnosis; such algorithm is subtly incorporated with the input gate structure of long short term memory network (LSTM) the dilated convolutions, as well as the capsule network. In the GDCCN model, the raw vibration signals are directly fed into the input gate structure of LSTM, which are employed to effectively remove noise and harvest more valuable information from the input sample. The dilated convolution is exploited in the output denoising feature maps to exponentially expand the receptive field of convolution kernel, so more redundant information can be acquired to reduce the effect of randomness. The capsule network is introduced to generate a set of vector neurons to represent an entity existing in the feature maps; as a result, more specific feature representations can be extracted, and the feature can be comprehended, thereby enhancing the diagnostic accuracy. As revealed from the experimental results, the GDCCN based intelligent motor fault diagnosis method outperforms the other classical DL algorithms in diagnosis accuracy, noise resistance, generalization and transfer learning performance of different workloads.

title: A deep learning method for motor fault diagnosis based on a capsule network with gate structure dilated convolutions

title: Multi Rhythm Capsule Network Recognition Structure for Motor Imagery Classification
abstract: Existing machine learning methods for classification and recognition of EEG motor imagery usually suffer from reduced accuracy for limited training data. To address this problem, this paper proposes a multi rhythm capsule network (FBCapsNet) that uses as little EEG information as possible with key features to classify motor imagery and further improves the classification efficiency. The network conforms to a small recognition model with only 3 acquisition channels but it can effectively use the limited data for feature learning. Based on the BCI Competition IV 2b data set, experimental results show that the proposed network can achieve 2.41% better performance than existing cutting edge methods.

title: Motor Imagery EEG Classification Using Capsule Networks+
abstract: Various convolutional neural network (CNN) based approaches have been recently proposed to improve the performance of motor imagery based brain computer interfaces (BCIs) However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery electroencephalogram (EEG) the measured signals, even from the same person, are not consistent and can be significantly distorted. To overcome these limitations, we propose to apply a capsule network (CapsNet) for learning various properties of EEG signals, thereby achieving better and more robust performance than previous CNN methods. The proposed CapsNet based framework classifies the two class motor imagery, namely right hand and left hand movements. The motor imagery EEG signals are first transformed into 2D images using the short time Fourier transform (STFT) algorithm and then used for training and testing the capsule network. The performance of the proposed framework was evaluated on the BCI competition IV 2b dataset. The proposed framework outperformed state of the art CNN based methods and various conventional machine learning approaches. The experimental results demonstrate the feasibility of the proposed approach for classification of motor imagery EEG signals.

title: The impact of different infarction regions on functional connectivity in motor execution network
abstract: To explore the influence of the location of the infarction lesion on the resting state functional connectivity (rsFC) of the motor execution network.  A total of 144 patients with infarction of the motor pathway were involved in the study, including 97 patients with capsule stroke (CS) and 47 patients with pontine stroke (PS).50 age matched healthy subjects were enrolled.After acquiring the structural images and the resting state functional MRI data of all the subjects by 3.0 Tesla MR scanner, the functional connectivity was calculated in different regions of interest and the differences of the rsFC within the motor execution network among patients with different infarction location were compared.  Left CS patients exhibited increased rsFC in L_PMd (MNI x, y, z: 22, 13, 57) R_M1 (MNI x, y, z: 38, 22, 56) (F=3.951, P=0.022) the increased rsFC than healthy controls (P=0.031) Left CS patients exhibited increased rsFC in R_PMv (MNI x, y, z: 53, 0, 25) L_SPL (MNI x, y, z: 22, 64, 54) than healthy controls (F=4.017, P=0.021) left CS patients exhibited decreased rsFC in R_PCG (MNI x, y, z: 37, 34, 53) R_PMv (MNI x, y, z: 53, 0, 25) than healthy controls(F= 3.788, P=0.025) Right CS patients exhibited increased rsFC than healthy controls in R_PMd(MNI x, y, ) L_M1(MNI x, y, z: 38, 22, 56) (F=4.438, P=0.014) right CS patients exhibited increased rsFC than healthy controls in R_PMv(MNI x, y, z: 53, 0, 25) R_PCG(MNI x, y, z: 37, 34, 53)(F=4.830, P=0.010) right CS patients exhibited decreased rsFC in L_M1(MNI x, y, z: 38, 22, 56) L_SMA(MNI x, y, z: 5, 4, 57) than healthy controls (F= 5.102, P=0.007) And the left PS patients showed increased rsFC than healthy controls in R_DN L_SMA (F=4.939, P=0.009) left PS patients exhibited increased rsFC than healthy controls in L_DN R_SMA (F=3.431, P=0.036) left PS patients exhibited reduced rsFC than healthy controls in R_AICb R_PMd (F= 4.114, P=0.019) Right PS patients showed increased rsFC compared with healthy controls in L_DN R_M1(F=3.075, P=0.049) and increased rsFC compared with healthy controls in L_AICb R_SCb (F=3.725, P=0.027)  The infarction location poses influence on the FC alteration of the motor network after stroke.  Cerebral infarction; Motor execution network; Functional connectivity; Resting state fMRI

title: Motor network structure and function are associated with motor performance in Huntington's disease
abstract: In Huntington's disease, the relationship of brain structure, brain function and clinical measures remains incompletely understood. We asked how sensory motor network brain structure and neural activity relate to each other and to motor performance. Thirty four early stage HD and 32 age and sex matched healthy control participants underwent structural magnetic resonance imaging (MRI) diffusion tensor, and intrinsic functional connectivity MRI. Diffusivity patterns were assessed in the cortico spinal tract and the thalamus somatosensory cortex tract. For the motor network connectivity analyses the dominant M1 motor cortex region and for the basal ganglia thalamic network the thalamus were used as seeds. Region to region structural and functional connectivity was examined between thalamus and somatosensory cortex. Fractional anisotropy (FA) was higher in HD than controls in the basal ganglia, and lower in the external and internal capsule, in the thalamus, and in subcortical white matter. Between group axial and radial diffusivity differences were more prominent than differences in FA, and correlated with motor performance. Within the motor network, the insula was less connected in HD than in controls, with the degree of connection correlating with motor scores. The basal ganglia thalamic network's connectivity differed in the insula and basal ganglia. Tract specific white matter diffusivity and functional connectivity were not correlated. In HD sensory motor white matter organization and functional connectivity in a motor network were independently associated with motor performance. The lack of tract specific association of structure and function suggests that functional adaptation to structural loss differs between participants.

title: Anatomical review of the ventral capsule/ventral striatum and the nucleus accumbens to guide target selection for deep brain stimulation for obsessive compulsive disorder.
abstract: Disturbances in the brain's reward network underlie addiction, depression, and obsessive compulsive disorder (OCD) The ventral capsule/ventral striatum (VC/VS) and nucleus accumbens (NAc) region is a clinically approved target for deep brain stimulation (DBS) for OCD and investigational for other applications.  We performed a comprehensive literature review to define the clinically relevant anatomy and connectivity of the VC/VS and NAc region to guide target selection for DBS.  Architecturally and functionally, NAc is divided into the core and the shell, with each area having different connections. The shell primarily receives limbic information, and the core typically receives information from the motor system. In general, afferents from the prefrontal cortex, hippocampus, and amygdala are excitatory. The dopaminergic projections to the NAc from the ventral tegmental area modulate the balance of these excitatory inputs. At the junction of the internal capsule (IC) and the anterior commissure (AC) several important inputs to the NAc converge: the ventral amygdalofugal pathways that run parallel to and underneath the AC; the pre commissural fonical fibers that run anterior to the AC; axons from the ventral prefrontal cortex and medial orbitofrontal cortex that occupy the most ventral part of the IC and embedding within the NAc and AC; and the superolateral branch of the medial forebrain bundle located parallel to the anterior thalamic radiation in the IC.  The caudal part of the NAc passing through the IC AC junction may be an effective target for DBS intended to improve behavioral symptoms associated with OCD.

title: Isolated motor neglect following infarction of the posterior limb of the right internal capsule: a case study with diffusion tensor imaging based tractography
abstract: Motor neglect is an impairment in the ability to initiate movement not attributable to muscle weakness. The neural network of this syndrome is not precisely defined. We present the diffusion tensor imaging (DTI) base tractography findings in an acute stroke patient presenting with isolated motor neglect following infarction in the posterior limb of the internal capsule within the anterior choroidal artery territory. A left handed 17 year old woman presented with an acute onset of motor neglect of her left arm. Motor tasks performed with the affected limb were awkward; however, the tasks could be accomplished with effort. Magnetic resonance imaging (MRI) including DTI of the brain were performed. DTI based tractography extracted the fiber tracts originating from regions of interest placed on the ischemic lesion. MRI revealed an acute ischemic infarction at the posterior part of the posterior limb of the right internal capsule within the territory of the anterior choroidal artery. DTI based tractography showed fiber tracts projecting from the lesion to the posterior part of the supplementary motor area and some fiber tracts projecting to posterior aspects of the thalamus. DTI based tractography may be a useful tool for visualizing white matter pathways in vivo following an acute infarction. Our case study supports the notion that fiber tracts connecting the posterior part of the posterior limb of the internal capsule, supplementary motor area, and posterior aspect of the thalamus are key areas of a neural network involved in motor neglect syndrome.

title: Brain Machine Interface Induced Morpho Functional Remodeling of the Neural Motor System in Severe Chronic Stroke
abstract: Brain machine interfaces (BMI) permit bypass motor system disruption by coupling contingent neuroelectric signals related to motor activity with prosthetic devices that enhance afferent and proprioceptive feedback to the somatosensory cortex. In this study, we investigated neural plasticity in the motor network of severely impaired chronic stroke patients after an EEG BMI based treatment reinforcing sensorimotor contingency of ipsilesional motor commands. Our structural connectivity analysis revealed decreased fractional anisotropy in the splenium and body of the corpus callosum, and in the contralesional hemisphere in the posterior limb of the internal capsule, the posterior thalamic radiation, and the superior corona radiata. Functional connectivity analysis showed decreased negative interhemispheric coupling between contralesional and ipsilesional sensorimotor regions, and decreased positive intrahemispheric coupling among contralesional sensorimotor regions. These findings indicate that BMI reinforcing ipsilesional brain activity and enhancing proprioceptive function of the affected hand elicits reorganization of contralesional and ipsilesional somatosensory and motor assemblies as well as afferent and efferent connection related motor circuits that support the partial re establishment of the original neurophysiology of the motor system even in severe chronic stroke.

title: Neuroanatomy, Upper Motor Nerve Signs
abstract: The initiation and coordination of movement are under the control of an immense network of nerves in the central nervous system (CNS) that originate from the cerebral cortex and course through the internal capsule, brainstem, and spinal cord. The impulses for movement are carried by nerves known as upper motor neurons (UMN) The pyramidal tract is the primary tract that propagates signals necessary for voluntary movement. The pyramidal tract divides into the corticospinal tract and the corticobulbar tract. Injury to UMNs in these tracts is common because of the large areas covered by the motor neuron pathway. Any injury to these tracts is known as UMN lesions. Damage to UMNs results in characteristic clinical manifestations colloquially termed "upper motor neuron signs" or "upper motor neuron syndrome." The symptoms include muscle weakness, spasticity, hyperreflexia, and clonus. Damage to UMNs of the corticobulbar tract can manifest as dysphagia and dysarthria. Distinguishing upper motor neuron signs from lower motor neuron signs is essential in the neurological physical exam.