Continuous reach-to-grasp motion recognition based on an extreme learning machine algorithm using sEMG signals.

Recognizing user intention in reach-to-grasp motions is a critical challenge in rehabilitation engineering. To address this, a Machine Learning (ML) algorithm based on the Extreme Learning Machine (ELM) was developed for identifying motor actions using surface Electromyography (sEMG) during continuous reach-to-grasp movements, involving multiple Degrees of Freedom (DoFs). This study explores feature extraction methods based on time domain and autoregressive models to evaluate ELM performance under different conditions. The experimental setup encompassed variations in neuron size, time windows, validation with each muscle, increase in the number of features, comparison with five conventional ML-based classifiers, inter-subjects variability, and temporal dynamic response. To evaluate the efficacy of the proposed ELM-based method, an openly available sEMG dataset containing data from 12 participants was used. Results highlight the method's performance, achieving Accuracy above 85%, F-score above 90%, Recall above 85%, Area Under the Curve of approximately 84% and compilation times (computational cost) of less than 1 ms. These metrics significantly outperform standard methods (p < 0.05). Additionally, specific trends were found in increasing and decreasing performance in identifying specific tasks, as well as variations in the continuous transitions in the temporal dynamics response. Thus, the ELM-based method effectively identifies continuous reach-to-grasp motions through myoelectric data. These findings hold promise for practical applications. The method's success prompts future research into implementing it for more reliable and effective Human-Machine Interface (HMI) control. This can revolutionize real-time upper limb rehabilitation, enabling natural and complex Activities of Daily Living (ADLs) like object manipulation. The robust results encourages further research and innovative solutions to improve people's quality of life through more effective interventions.

EEG motor imagery classification using deep learning approaches in naïve BCI users.

Motor Imagery (MI)-Brain Computer-Interfaces (BCI) illiteracy defines that not all subjects can achieve a good performance in MI-BCI systems due to different factors related to the fatigue, substance consumption, concentration, and experience in the use. To reduce the effects of lack of experience in the use of BCI systems (naïve users), this paper presents the implementation of three Deep Learning (DL) methods with the hypothesis that the performance of BCI systems could be improved compared with baseline methods in the evaluation of naïve BCI users. The methods proposed here are based on Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM)/Bidirectional Long Short-Term Memory (BiLSTM), and a combination of CNN and LSTM used for upper limb MI signal discrimination on a dataset of 25 naïve BCI users. The results were compared with three widely used baseline methods based on the Common Spatial Pattern (CSP), Filter Bank Common Spatial Pattern (FBCSP), and Filter Bank Common Spatial-Spectral Pattern (FBCSSP), in different temporal window configurations. As results, the LSTM-BiLSTM-based approach presented the best performance, according to the evaluation metrics of Accuracy, F-score, Recall, Specificity, Precision, and ITR, with a mean performance of 80% (maximum 95%) and ITR of 10 bits/min using a temporal window of 1.5 s. The DL Methods represent a significant increase of 32% compared with the baseline methods (p< 0.05). Thus, with the outcomes of this study, it is expected to increase the controllability, usability, and reliability of the use of robotic devices in naïve BCI users.

A Low-rank Spatiotemporal based EEG Multi-Artifacts Cancellation Method for Enhanced ConvNet-DL's Motor Imagery Characterization.

Multi-channel Electroencephalograph (EEG) signal is an important source of neural information for motor imagery (MI) limb movement intent decoding. The decoded MI movement intent often serve as potential control input for brain-computer interface (BCI) based rehabilitation robots. However, the presence of multiple dynamic artifacts in EEG signal leads to serious processing challenge that affects the BCI system in practical settings. Hence, this study propose a hybrid approach based on Low-rank spatiotemporal filtering technique for concurrent elimination of multiple EEG artifacts. Afterwards, a convolutional neural network based deep learning model (ConvNet-DL) that extracts neural information from the cleaned EEG signal for MI tasks decoding was built. The proposed method was studied in comparison with existing artifact removal methods using EEG signals of transhumeral amputees who performed five different MI tasks. Remarkably, the proposed method led to significant improvements in MI task decoding accuracy for the ConvNet-DL model in the range of 8.00~13.98%, while up to 14.38% increment was recorded in terms of the MCC: Mathew correlation coefficients at p<0.05. Also, a signal to error ratio of more than 11 dB was recorded by the proposed method.Clinical Relevance- This study showed that a combination of the proposed hybrid EEG artifact removal method and ConvNet-DL can significantly improve the decoding accuracy of MI upper limb movement tasks. Our findings may provide potential control input for BCI rehabilitation robotic systems.

Entropy and Clustering Information Applied to sEMG Classification.

The EMG signal is very difficult to classify due to the stochastic complexity of its characteristics. A way to reduce the complexity of a signal is to use clusters to resize them to a smaller space and then perform the classification. A classification improvement was verified by clustering the electromyographic signal and comparing it with the possible movements that can be performed. In this study, the Agglomerative Hierarchical Clustering was used. The basic idea is to give prior information to the final classifier so the posterior classification has fewer classes, diminishing his complexity. Through the methodology applied in this article, an accuracy of more than 90% was achieved by using a time window of only 10 ms in a signal sampled at 2000 Hz. Experimentation confirms that the methods presented in this paper are competitive with other methods presented in the literature.

Shoulder Flexion Pre-Movement Recognition Through Subject-Specific Brain Regions to Command an Upper Limb Exoskeleton.

This work presents two brain-computer interfaces (BCIs) for shoulder pre-movement recognition using: 1) manual strategy for Electroencephalography (EEG) channels selection, and 2) subject-specific channels selection by applying non-negative factorization matrix (NMF). Besides, the proposed BCIs compute spatial features extracted from filtered EEG signals through Riemannian covariance matrices and a linear discriminant analysis (LDA) to discriminate both shoulder pre-movement and rest states. We studied on twenty-one healthy subjects different frequency ranges looking the best frequency band for shoulder pre-movement recognition. As a result, our BCI located automatically EEG channels on the contralateral moved limb, and enhancing the pre-movement recognition (ACC = 71.39 ± 12.68%, κ = 0.43 ± 0.25%). The ability of the proposed BCIs to select specific EEG locations more cortically related to the moved limb could benefit the neuro-rehabilitation process.

Characterization of the Cicatrization Process in Diabetic Foot Ulcers Based on the Production of Reactive Oxygen Species.

The present study aims at evaluating the correlation between the free radical formation and the healing action of lower limbs' ulcers in a randomized controlled trial with the use of an adhesive derived from natural latex associated with a light-emitting diode (LED) circuit. The sample consists of 15 participants with lower limb lesions divided into three groups: group 1 case (5 participants) received the proposed dressing system adhesive of the natural latex associated with the LED circuit; group 2 control (5 participants) received the dressings at home performed by nurses according to and established by the clinic of wounds (treated with calcium alginate or silver foam); and group 3 (5 participants) also received the dressing in their homes with the use of the dressing adhesive derived from the natural latex associated with the LED circuit. The collected data were analyzed qualitatively and quantitatively by electron paramagnetic resonance for determination of free radical formation. Kruskal-Wallis statistical test was used to evaluate the effect of treatment on the lower limb's ulcer cicatrization process and its correlation with free radical. The results obtained corroborated the hypothesis about the reduction of the quantity of these molecules in the end of treatment related to the healing wound.

Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing.

This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly ( p < 0.01 ) improved for most of the subjects ( A C C ≥ 74.79 % ) , when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry.