Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals.

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.

Doxorubicin Interaction with Lipid Monolayers Leads to Decreased Membrane Stiffness when Experiencing Compression-Expansion Dynamics.

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.

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.

Effects of the concentration level, eye fatigue and coffee consumption on the performance of a BCI system based on visual ERP-P300.

BACKGROUND: A widely used paradigm for Brain-Computer Interfaces (BCI) is based on detecting P300 Event-Related Potentials (ERPs) in response to stimulation and concentration tasks. An open challenge corresponds to maximizing the performance of a BCI by considering artifacts arising from the user's cognitive and physical conditions during task execution. NEW METHOD: In this study, an analysis of the performance of a visual BCI-P300 system was performed under the metrics of Sensitivity (Sen), Specificity (Spe), Accuracy (Acc), and Area-Under the ROC Curve (AUC), considering the main reported factors affecting the neurophysiological behavior of the P300 signal: Concentration Level, Eye Fatigue, and Coffee Consumption. COMPARISON WITH EXISTING METHODS: We compared the performance of three P300 signal detection methods (MA-LDA, CCA-RLR, and MA+CCA-RLR) using a public database (GigaScience) in different groups. Data were segmented according to three factors of interest: high and low levels of concentration, high and low eye fatigue, and coffee consumption at different times. RESULTS: The results showed a significant improvement between 3% and 6% for the metrics evaluated for identifying the P300 signal in relation to concentration levels and coffee consumption. CONCLUSION: P300 signal can be influenced by physical and mental factors during the execution of ERPs evocation tasks, which could be controlled to maximize the interface's capacity to detect the individual's intention.

Evaluation of Platelet Rich Fibrin Obtained Using Different Centrifugation Parameters as a Tool for Regenerative Medicine.

BACKGROUND: Platelet-rich fibrin (PRF) is a biomaterial widely used in the field of regenerative medicine. The purpose of this work was to analyze the structure and biomolecular characteristics of PRF through nine centrifugation parameters (CP) for its preparation, using a pool of blood samples of five volunteers. METHODS: The PRF obtained was analyzed by morphological and histological characteristics, as well as electronic and atomic force microscopy and growth factors determinations. RESULTS: A longer time of centrifugation showed taller clots and denser mesh fibrin in comparison with a short time (p < 0.05). The protocols with higher speed of centrifugation showed higher levels of PDGF-BB and VEGF. Higher levels of TGFß1 were found in protocols with a shorter centrifuge time. The mean platelet count (916.05 ± 23.73 cells x 103 cells x cm3) and its roughness (Ra) (616.5 ± 45.2 nm) did not show significant differences between different CP (p > 0.05). A significant correlation between fibrin density and levels of PDGF (r = 0.57) and VEGF (r = 0.52) was found. Additionally, the size of the clot had a significant correlation (r = -0.47) with TGFß1 levels. CONCLUSIONS: Different centrifugation parameters to obtain PRF have been reported. These results indicate that changes in the conditions to obtain PRF have a significant impact on their fibrin structure, cellular distribution, and biomolecular content, which can be decisive for its choice in the different clinical applications to be used. It is necessary to use a standardized centrifuge and protocol to guarantee high-quality PRF and clinical outcomes with less variability.