Corticomuscular and Intermuscular Coupling in Simple Hand Movements to Enable a Hybrid Brain–Computer Interface

2021 ◽  
Author(s):  
Emma Colamarino ◽  
Valeria de Seta ◽  
Marcella Masciullo ◽  
Febo Cincotti ◽  
Donatella Mattia ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
Muscular Activity ◽  
Hand Movements ◽  
Normal Brain ◽  
Dominant Hand ◽  
Upper Limb Rehabilitation ◽  
Grand Average ◽  
Intermuscular Coherence ◽  
Performance Area

Hybrid Brain–Computer Interfaces (BCIs) for upper limb rehabilitation after stroke should enable the reinforcement of “more normal” brain and muscular activity. Here, we propose the combination of corticomuscular coherence (CMC) and intermuscular coherence (IMC) as control features for a novel hybrid BCI for rehabilitation purposes. Multiple electroencephalographic (EEG) signals and surface electromyography (EMG) from 5 muscles per side were collected in 20 healthy participants performing finger extension (Ext) and grasping (Grasp) with both dominant and non-dominant hand. Grand average of CMC and IMC patterns showed a bilateral sensorimotor area as well as multiple muscles involvement. CMC and IMC values were used as features to classify each task versus rest and Ext versus Grasp. We demonstrated that a combination of CMC and IMC features allows for classification of both movements versus rest with better performance (Area Under the receiver operating characteristic Curve, AUC) for the Ext movement (0.97) with respect to Grasp (0.88). Classification of Ext versus Grasp also showed high performances (0.99). All in all, these preliminary findings indicate that the combination of CMC and IMC could provide for a comprehensive framework for simple hand movements to eventually be employed in a hybrid BCI system for post-stroke rehabilitation.

scholarly journals Normal tissue content impact on the GBM molecular classification

2020 ◽  
Author(s):  
Rodrigo Madurga ◽  
Noemí García-Romero ◽  
Beatriz Jiménez ◽  
Ana Collazo ◽  
Francisco Pérez-Rodríguez ◽  
...  
Keyword(s):  
Normal Tissue ◽  
In Silico Analysis ◽  
Molecular Classification ◽  
Gene Signature ◽  
Normal Brain ◽  
Normal Cells ◽  
Final Model ◽  
Tumor Subtypes ◽  
Tissue Content

Abstract Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. In this work, we present a model that tackles both the molecular classification of samples and discrimination of those with a high content of normal cells. We performed a transcriptomic in silico analysis on glioblastoma (GBM) samples (n = 810) and tested different criteria to optimize the number of genes needed for molecular classification. We used gene expression of normal brain samples (n = 555) to design an additional gene signature to detect samples with a high normal tissue content. Microdissection samples of different structures within GBM (n = 122) have been used to validate the final model. Finally, the model was tested in a cohort of 43 patients and confirmed by histology. Based on the expression of 20 genes, our model is able to discriminate samples with a high content of normal tissue and to classify the remaining ones. We have shown that taking into consideration normal cells can prevent errors in the classification and the subsequent misinterpretation of the results. Moreover, considering only samples with a low content of normal cells, we found an association between the complexity of the samples and survival for the three molecular subtypes.

scholarly journals Human Hand Anatomy-Based Prosthetic Hand

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 137
Author(s):  
Larisa Dunai ◽  
Martin Novak ◽  
Carmen García Espert
Keyword(s):  
3D Printing ◽  
Degrees Of Freedom ◽  
Use Of Force ◽  
Hand Movements ◽  
Human Hand ◽  
Prosthetic Hand ◽  
Object Surface ◽  
Hand Phalanges ◽  
High Level

The present paper describes the development of a prosthetic hand based on human hand anatomy. The hand phalanges are printed with 3D printing with Polylactic Acid material. One of the main contributions is the investigation on the prosthetic hand joins; the proposed design enables one to create personalized joins that provide the prosthetic hand a high level of movement by increasing the degrees of freedom of the fingers. Moreover, the driven wire tendons show a progressive grasping movement, being the friction of the tendons with the phalanges very low. Another important point is the use of force sensitive resistors (FSR) for simulating the hand touch pressure. These are used for the grasping stop simulating touch pressure of the fingers. Surface Electromyogram (EMG) sensors allow the user to control the prosthetic hand-grasping start. Their use may provide the prosthetic hand the possibility of the classification of the hand movements. The practical results included in the paper prove the importance of the soft joins for the object manipulation and to get adapted to the object surface. Finally, the force sensitive sensors allow the prosthesis to actuate more naturally by adding conditions and classifications to the Electromyogram sensor.

scholarly journals Evaluation of Commercial Ropes Applied as Artificial Tendons in Robotic Rehabilitation Orthoses

2020 ◽  
Vol 10 (3) ◽  
pp. 920 ◽  
Author(s):  
Guilherme de Paula Rúbio ◽  
Fernanda Márcia Rodrigues Martins Ferreira ◽  
Fabrício Henrique de Lisboa Brandão ◽  
Victor Flausino Machado ◽  
Leandro Gonzaga Tonelli ◽  
...  
Keyword(s):  
Tensile Force ◽  
Permanent Deformation ◽  
Hand Movements ◽  
Clinical Test ◽  
Robotic Rehabilitation ◽  
Upper Limb Rehabilitation ◽  
Direct Measurements ◽  
Robotic Orthosis ◽  
Design Selection ◽  
Limb Rehabilitation

This study aims to present the design, selection and testing of commercial ropes (artificial tendons) used on robotic orthosis to perform the hand movements for stroke individuals over upper limb rehabilitation. It was determined the load applied in the rope would through direct measurements performed on four individuals after stroke using a bulb dynamometer. A tensile strength test was performed using eight commercial ropes in order to evaluate the maximum breaking force and select the most suitable to be used in this application. Finally, a pilot test was performed with a user of the device to ratify the effectiveness of the rope. The load on the cable was 12.38 kgf (121.4 N) in the stroke-affected hand, which is the maximum tensile force that the rope must to supports. Paragliding rope (DuPont™ Kevlar ® ) supporting a load of 250 N at a strain of 37 mm was selected. The clinical test proved the effectiveness of the rope, supporting the requested efforts, without presenting permanent deformation, effectively performing the participant’s finger opening.

scholarly journals Real-time classification of hand movements as a basis for intuitive control of grasp neuroprostheses

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Dmitry Amelin ◽  
Ivan Potapov ◽  
Josep Cardona Audí ◽  
Andreas Kogut ◽  
Rüdiger Rupp ◽  
...  
Keyword(s):  
Neural Networks ◽  
Standard Deviation ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Healthy Subjects ◽  
Hand Movements ◽  
Cord Injury ◽  
Field Programmable

AbstractThis paper reports on the evaluation of recurrent and convolutional neural networks as real-time grasp phase classifiers for future control of neuroprostheses for people with high spinal cord injury. A field-programmable gate array has been chosen as an implementation platform due to its form factor and ability to perform parallel computations, which are specific for the selected neural networks. Three different phases of two grasp patterns and the additional open hand pattern were predicted by means of surface Electromyography (EMG) signals (i.e. Seven classes in total). Across seven healthy subjects, CNN (Convolutional Neural Networks) and RNN (Recurrent Neural Networks) had a mean accuracy of 85.23% with a standard deviation of 4.77% and 112 µs per prediction and 83.30% with a standard deviation of 4.36% and 40 µs per prediction, respectively.

Walking by Thinking: The Brainwaves Are Crucial, Not the Muscles!

2006 ◽  
Vol 15 (5) ◽  
pp. 500-514 ◽  
Author(s):  
Robert Leeb ◽  
Claudia Keinrath ◽  
Doron Friedman ◽  
Christoph Guger ◽  
Reinhold Scherer ◽  
...  
Keyword(s):  
Communication Channel ◽  
Muscular Activity ◽  
Proof Of Concept ◽  
Closed Loop System ◽  
Head Mounted Display ◽  
Eeg Recordings ◽  
Sensorimotor Rhythms ◽  
Electroencephalogram Eeg ◽  
Healthy Participants

Healthy participants are able to move forward within a virtual environment (VE) by the imagination of foot movement. This is achieved by using a brain-computer interface (BCI) that transforms thought-modulated electroencephalogram (EEG) recordings into a control signal. A BCI establishes a communication channel between the human brain and the computer. The basic principle of the Graz-BCI is the detection and classification of motor-imagery-related EEG patterns, whereby the dynamics of sensorimotor rhythms are analyzed. A BCI is a closed-loop system and information is visually fed back to the user about the success or failure of an intended movement imagination. Feedback can be realized in different ways, from a simple moving bar graph to navigation in VEs. The goals of this work are twofold: first, to show the influence of different feedback types on the same task, and second, to demonstrate that it is possible to move through a VE (e.g., a virtual street) without any muscular activity, using only the imagination of foot movement. In the presented work, data from BCI feedback displayed on a conventional monitor are compared with data from BCI feedback in VE experiments with a head-mounted display (HMD) and in a high immersive projection environment (Cave). Results of three participants are reported to demonstrate the proof-of-concept. The data indicate that the type of feedback has an influence on the task performance, but not on the BCI classification accuracy. The participants achieved their best performances viewing feedback in the Cave. Furthermore the VE feedback provided motivation for the subjects.

scholarly journals Identification of a Blood-Based Protein Biomarker Panel for Lung Cancer Detection

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1629 ◽  
Author(s):  
Victoria El-Khoury ◽  
Anna Schritz ◽  
Sang-Yoon Kim ◽  
Antoine Lesur ◽  
Katriina Sertamo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Lung Cancer Screening ◽  
Disease Stage ◽  
Validation Dataset ◽  
Average Risk ◽  
Bootstrap Sampling ◽  
Lung Cancer Patients ◽  
Performance Area

Lung cancer is the deadliest cancer worldwide, mainly due to its advanced stage at the time of diagnosis. A non-invasive method for its early detection remains mandatory to improve patients’ survival. Plasma levels of 351 proteins were quantified by Liquid Chromatography-Parallel Reaction Monitoring (LC-PRM)-based mass spectrometry in 128 lung cancer patients and 93 healthy donors. Bootstrap sampling and least absolute shrinkage and selection operator (LASSO) penalization were used to find the best protein combination for outcome prediction. The PanelomiX platform was used to select the optimal biomarker thresholds. The panel was validated in 48 patients and 49 healthy volunteers. A 6-protein panel clearly distinguished lung cancer from healthy individuals. The panel displayed excellent performance: area under the receiver operating characteristic curve (AUC) = 0.999, positive predictive value (PPV) = 0.992, negative predictive value (NPV) = 0.989, specificity = 0.989 and sensitivity = 0.992. The panel detected lung cancer independently of the disease stage. The 6-protein panel and other sub-combinations displayed excellent results in the validation dataset. In conclusion, we identified a blood-based 6-protein panel as a diagnostic tool in lung cancer. Used as a routine test for high- and average-risk individuals, it may complement currently adopted techniques in lung cancer screening.

scholarly journals Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification

2020 ◽  
Vol 10 (19) ◽  
pp. 6940 ◽  
Author(s):  
Vincenzo Taormina ◽  
Donato Cascio ◽  
Leonardo Abbene ◽  
Giuseppe Raso
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fluorescence Intensity ◽  
Characteristic Curve ◽  
Performance Comparison ◽  
Fine Tuning ◽  
Tuning Method ◽  
Effective Performance ◽  
Training Modalities

The search for anti-nucleus antibodies (ANA) represents a fundamental step in the diagnosis of autoimmune diseases. The test considered the gold standard for ANA research is indirect immunofluorescence (IIF). The best substrate for ANA detection is provided by Human Epithelial type 2 (HEp-2) cells. The first phase of HEp-2 type image analysis involves the classification of fluorescence intensity in the positive/negative classes. However, the analysis of IIF images is difficult to perform and particularly dependent on the experience of the immunologist. For this reason, the interest of the scientific community in finding relevant technological solutions to the problem has been high. Deep learning, and in particular the Convolutional Neural Networks (CNNs), have demonstrated their effectiveness in the classification of biomedical images. In this work the efficacy of the CNN fine-tuning method applied to the problem of classification of fluorescence intensity in HEp-2 images was investigated. For this purpose, four of the best known pre-trained networks were analyzed (AlexNet, SqueezeNet, ResNet18, GoogLeNet). The classifying power of CNN was investigated with different training modalities; three levels of freezing weights and scratch. Performance analysis was conducted, in terms of area under the ROC (Receiver Operating Characteristic) curve (AUC) and accuracy, using a public database. The best result achieved an AUC equal to 98.6% and an accuracy of 93.9%, demonstrating an excellent ability to discriminate between the positive/negative fluorescence classes. For an effective performance comparison, the fine-tuning mode was compared to those in which CNNs are used as feature extractors, and the best configuration found was compared with other state-of-the-art works.

The contribution of strain patterns in characterization of prostate peripheral zone lesions at transrectal ultrasonography

2012 ◽  
Vol 53 (1) ◽  
pp. 119-126 ◽  
Author(s):  
Yan Zhang ◽  
Jie Tang ◽  
Yan-mi Li ◽  
Xiang Fei ◽  
En-hui He ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Peripheral Zone ◽  
Diagnostic Value ◽  
Ultrasound Elastography ◽  
Transrectal Ultrasonography ◽  
Targeted Biopsy

Background Elasticity is an important characteristic of tissue. During an elastography examination, various strain images of lesions are observed, and a suitable classification of strain patterns (SP) may provide vital diagnostic information about lesions. Numerous studies have shown that ultrasound elastography can improve the detection of prostate cancer, but the diagnostic value of SP classification has not yet been fully evaluated. Purpose To investigate the contribution of SP on the characterization of prostate peripheral zone lesions by transrectal real-time tissue elastography (TRTE) in combination with conventional transrectal ultrasonography (TRUS). Material and Methods One hundred and seventy-one patients with suspected prostate cancer underwent TRUS and TRTE examinations. The SPs of the suspicious lesions were classified into five scores by TRTE according to the degree and distribution of strain. All findings were confirmed by transrectal systematic 12-core biopsies and targeted biopsies for suspicious areas detecting by TRUS and/or TRTE. Results One hundred and forty-eight of 171 patients had high-quality TRTE imaging and were included into the study. When a cut-off point of SP score III was used, the area under the receiver-operating characteristic curve (AUC) was, respectively, 0.75 (95% CI: 0.67–0.83), 0.85 (95% CI: 0.78–0.91) and 0.84 (95% CI: 0.77–0.91) for the diagnosis of prostate cancer by TRUS, TRTE and TRTE + TRUS. A linear tendency of SP and Gleason scores was observed in scores III-V. The detection rate of prostate cancer using TRTE-targeted biopsy (75.8%) was significantly higher than that of systematic 12-core biopsy plus TRUS-targeted biopsy (14.5%) ( P = 0.00). Conclusion This study suggests the significant contribution of SP on characterization of prostate peripheral zone lesions and the improvement of TRTE-targeted biopsy on detection of prostate cancer.

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