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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.

RESUMEN

Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8-19 Hz and 19-30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, andKappaindex of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.

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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.

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Physical membrane models permit to study and quantify the interactions of many external molecules with monitored and simplified systems. In this work, we have constructed artificial Langmuir single-lipid monolayers with dipalmitoylphosphatidylcholine (DPPC), dipalmitoylphosphatidylethanolamine (DPPE), dipalmitoylphosphatidylserine (DPPS), or sphingomyelin to resemble the main lipid components of the mammalian cell membranes. We determined the collapse pressure, minimum area per molecule, and maximum compression modulus (Cs-1) from surface pressure measurements in a Langmuir trough. Also, from compression/expansion isotherms, we estimated the viscoelastic properties of the monolayers. With this model, we explored the membrane molecular mechanism of toxicity of the well-known anticancer drug doxorubicin, with particular emphasis in cardiotoxicity. The results showed that doxorubicin intercalates mainly between DPPS and sphingomyelin, and less between DPPE, inducing a change in the Cs-1 of up to 34% for DPPS. The isotherm experiments suggested that doxorubicin had little effect on DPPC, partially solubilized DPPS lipids toward the bulk of the subphase, and caused a slight or large expansion in the DPPE and sphingomyelin monolayers, respectively. Furthermore, the dynamic viscoelasticity of the DPPE and DPPS membranes was greatly reduced (by 43 and 23%, respectively), while the reduction amounted only to 12% for sphingomyelin and DPPC models. In conclusion, doxorubicin intercalates into the DPPS, DPPE, and sphingomyelin, but not into the DPPC, membrane lipids, inducing a structural distortion that leads to decreased membrane stiffness and reduced compressibility modulus. These alterations may constitute a novel, early step in explaining the doxorubicin mechanism of action in mammalian cancer cells or its toxicity in non-cancer cells, with relevance to explain its cardiotoxicity.

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