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General General

Ascertaining the optimal myoelectric signal recording duration for pattern recognition based prostheses control.

In Frontiers in neuroscience ; h5-index 72.0

INTRODUCTION : Electromyogram-based pattern recognition (EMG-PR) has been widely considered an essentially intuitive control method for multifunctional upper limb prostheses. A crucial aspect of the scheme is the EMG signal recording duration (SRD) from which requisite motor tasks are characterized per time, impacting the system's overall performance. For instance, lengthy SRD inevitably introduces fatigue (that alters the muscle contraction patterns of specific limb motions) and may incur high computational costs in building the motion intent decoder, resulting in inadequate prosthetic control and controller delay in practical usage. Conversely, relatively shorter SRD may lead to reduced data collection durations that, among other advantages, allow for more convenient prosthesis recalibration protocols. Therefore, determining the optimal SRD required to characterize limb motion intents adequately that will aid intuitive PR-based control remains an open research question.

METHOD : This study systematically investigated the impact and generalizability of varying lengths of myoelectric SRD on the characterization of multiple classes of finger gestures. The investigation involved characterizing fifteen classes of finger gestures performed by eight normally limb subjects using various groups of EMG SRD including 1, 5, 10, 15, and 20 s. Two different training strategies including Between SRD and Within-SRD were implemented across three popular machine learning classifiers and three time-domain features to investigate the impact of SRD on EMG-PR motion intent decoder.

RESULT : The between-SRD strategy results which is a reflection of the practical scenario showed that an SRD greater than 5 s but less than or equal to 10 s (>5 and < = 10 s) would be required to achieve decent average finger gesture decoding accuracy for all feature-classifier combinations. Notably, lengthier SRD would incur more acquisition and implementation time and vice-versa. In inclusion, the study's findings provide insight and guidance into selecting appropriate SRD that would aid inadequate characterization of multiple classes of limb motion tasks in PR-based control schemes for multifunctional prostheses.

Asogbon Mojisola Grace, Samuel Oluwarotimi Williams, Nsugbe Ejay, Li Yongcheng, Kulwa Frank, Mzurikwao Deogratias, Chen Shixiong, Li Guanglin

2023

electromyogram (EMG), finger gestures, pattern recognition (PR), prostheses, signal recording duration (SRD)

General General

Explainable machine learning for long-term outcome prediction in two-center stroke patients after intravenous thrombolysis.

In Frontiers in neuroscience ; h5-index 72.0

OBJECTIVE : Neurological outcome prediction in patients with ischemic stroke is very critical in treatment strategy and post-stroke management. Machine learning techniques with high accuracy are increasingly being developed in the medical field. We studied the application of machine learning models to predict long-term neurological outcomes in patients with after intravenous thrombolysis.

METHODS : A retrospective cohort study was performed to review all stroke patients with intravenous thrombolysis. Patients with modified Rankin Score (mRs) less than two at three months post-thrombolysis were considered as good outcome. The clinical features between stroke patients with good and with poor outcomes were compared using three different machine learning models (Random Forest, Support Vector Machine and Logistic Regression) to identify which performed best. Two datasets from the other stroke center were included accordingly for external verification and performed with explainable AI models.

RESULTS : Of the 488 patients enrolled in this study, and 374 (76.6%) patients had favorable outcomes. Patients with higher mRs at 3 months had increased systolic pressure, blood glucose, cholesterol (TC), and 7-day National Institute of Health Stroke Scale (NIHSS) score compared to those with lower mRs. The predictability and the areas under the curves (AUC) for the random forest model was relatively higher than support vector machine and LR models. These findings were further validated in the external dataset and similar results were obtained. The explainable AI model identified the risk factors as well.

CONCLUSION : Explainable AI model is able to identify NIHSS_Day7 is independently efficient in predicting neurological outcomes in patients with ischemic stroke after intravenous thrombolysis.

Ping Zheng, Huiyu She, Min Li, Qingke Bai, Qiuyun Lu, Xu Chen

2023

NIHSS, cerebral infarction, intravenous thrombolysis, ischemic stroke, machine learning, modified rankin score

General General

Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network.

In Frontiers in neuroscience ; h5-index 72.0

Motor imagery-based brain-computer interfaces (MI-BCI) have important application values in the field of neurorehabilitation and robot control. At present, MI-BCI mostly use bilateral upper limb motor tasks, but there are relatively few studies on single upper limb MI tasks. In this work, we conducted studies on the recognition of motor imagery EEG signals of the right upper limb and proposed a multi-branch fusion convolutional neural network (MF-CNN) for learning the features of the raw EEG signals as well as the two-dimensional time-frequency maps at the same time. The dataset used in this study contained three types of motor imagery tasks: extending the arm, rotating the wrist, and grasping the object, 25 subjects were included. In the binary classification experiment between the grasping object and the arm-extending tasks, MF-CNN achieved an average classification accuracy of 78.52% and kappa value of 0.57. When all three tasks were used for classification, the accuracy and kappa value were 57.06% and 0.36, respectively. The comparison results showed that the classification performance of MF-CNN is higher than that of single CNN branch algorithms in both binary-class and three-class classification. In conclusion, MF-CNN makes full use of the time-domain and frequency-domain features of EEG, can improve the decoding accuracy of single limb motor imagery tasks, and it contributes to the application of MI-BCI in motor function rehabilitation training after stroke.

Zhang Rui, Chen Yadi, Xu Zongxin, Zhang Lipeng, Hu Yuxia, Chen Mingming

2023

brain-computer interface (BCI), convolutional neural network (CNN), deep learning, feature fusion, single upper limb motor imagery

Radiology Radiology

Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children.

In Frontiers in neuroscience ; h5-index 72.0

OBJECTIVES : Leveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classifiers to predict ADHD diagnosis based on imaging and clinical information.

METHODS : From the Adolescents Behavior Cognitive Development (ABCD) database, we included 1,798 children with ADHD diagnosis and 6,007 without ADHD. In multivariate logistic regression adjusted for age and sex, we examined the association of ADHD with different neuroimaging metrics. The neuroimaging metrics included fractional anisotropy (FA), neurite density (ND), mean-(MD), radial-(RD), and axial diffusivity (AD) of white matter (WM) tracts, cortical region thickness and surface areas from T1-MPRAGE series, and functional network connectivity correlations from resting-state fMRI.

RESULTS : Children with ADHD showed markers of pervasive reduced microstructural integrity in white matter (WM) with diminished neural density and fiber-tracks volumes - most notable in the frontal and parietal lobes. In addition, ADHD diagnosis was associated with reduced cortical volume and surface area, especially in the temporal and frontal regions. In functional MRI studies, ADHD children had reduced connectivity among default-mode network and the central and dorsal attention networks, which are implicated in concentration and attention function. The best performing combination of feature selection and machine learning classifier could achieve a receiver operating characteristics area under curve of 0.613 (95% confidence interval = 0.580-0.645) to predict ADHD diagnosis in independent validation, using a combination of multimodal imaging metrics and clinical variables.

CONCLUSION : Our study highlights the neurobiological implication of frontal lobe cortex and associate WM tracts in pathogenesis of childhood ADHD. We also demonstrated possible potentials and limitations of machine learning models to assist with ADHD diagnosis in a general population cohort based on multimodal neuroimaging metrics.

Lin Huang, Haider Stefan P, Kaltenhauser Simone, Mozayan Ali, Malhotra Ajay, Constable R Todd, Scheinost Dustin, Ment Laura R, Konrad Kerstin, Payabvash Seyedmehdi

2023

attention-deficient hyperactivity disorder, brain connectivity, cortex morphology, machine learning, white matter microstructure

Radiology Radiology

Ischemic stroke prediction of patients with carotid atherosclerotic stenosis via multi-modality fused network.

In Frontiers in neuroscience ; h5-index 72.0

Carotid atherosclerotic stenosis of the carotid artery is an important cause of ischemic cerebrovascular disease. The aim of this study was to predict the presence or absence of clinical symptoms in unknown patients by studying the existence or lack of symptoms of patients with carotid atherosclerotic stenosis. First, a deep neural network prediction model based on brain MRI imaging data of patients with multiple modalities is constructed; it uses the multi-modality features extracted from the neural network as inputs and the incidence of diagnosis as output to train the model. Then, a machine learning-based classification algorithm is developed to utilize the clinical features for comparison and evaluation. The experimental results showed that the deep learning model using imaging data could better predict the clinical symptom classification of patients. As part of preventive medicine, this study could help patients with carotid atherosclerosis narrowing to prepare for stroke prevention based on the prediction results.

Lv Peng, Yang Jing, Wang Jiacheng, Guo Yi, Tang Qiying, Magnier Baptiste, Lin Jiang, Zhou Jianjun

2023

brain MRI, carotid atherosclerotic stenosis, deep learning, diagnostic classification, ischemic stroke

General General

Kohonen neural network and symbiotic-organism search algorithm for intrusion detection of network viruses.

In Frontiers in computational neuroscience

INTRODUCTION : The development of the Internet has made life much more convenient, but forms of network intrusion have become increasingly diversified and the threats to network security are becoming much more serious. Therefore, research into intrusion detection has become very important for network security.

METHODS : In this paper, a clustering algorithm based on the symbiotic-organism search (SOS) algorithm and a Kohonen neural network is proposed.

RESULTS : The clustering accuracy of the Kohonen neural network is improved by using the SOS algorithm to optimize the weights in the Kohonen neural network.

DISCUSSION : Our approach was verified with the KDDCUP99 network intrusion data. The experimental results show that SOS-Kohonen can effectively detect intrusion. The detection rate was higher, and the false alarm rate was lower.

Zhou Guo, Miao Fahui, Tang Zhonghua, Zhou Yongquan, Luo Qifang

2023

Kohonen neural network, detection rate, false alarm rate, intrusion detection, symbiotic-organism search algorithm