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.

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.

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.

Semi-supervised approach to identify steps, shoes, and gender of older adults under semi-naturalistic conditions using a waist-worn inertial sensor.

An emerging source of information to recognize individuals' characteristics are the walking pattern-related parameters. The elderly can be one of the populations that can benefit most from recognition-based applications, which may help to increase their possibilities of living independently at home. Approaches have been mostly focused on gait events' identification or assessment; nonetheless, such information can also be used to obtain seniors' characteristics that depend on physiological or environmental factors. These factors can be useful to provide a customized assistance based on contextual information. In this paper, we propose a method focused on seniors, to detect steps, and to recognize gender and type of shoes by using only the initial foot contact (IC) data obtained from inertial sensors during semi-controlled walking. Data were collected from 20 older adults who walked at self-speed in a natural environment. The method consists of first clustering the IC using k-means; then, a trained recurrent neural network recognizes gender, type of shoes, and the step phases (IC and other phases); to finally conduct step detection (SD) using a ruled-based method. The method recognizes gender and the type of shoes with an accuracy of 93% and 83.07%, respectively, whereas there were not misrecognitions of the step phases. SD achieved a mean absolute percentage error equal to 0.64%. The good results show that the method is appropriate for users' characteristics recognition applications without depending on assumptions based on individualities. Likewise, the method can be useful to monitor physical activity or systems aimed to keep safe older adults.

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.

A One Health view of the West Nile virus outbreak in Andalusia (Spain) in 2020.

Reports of West Nile virus (WNV) associated disease in humans were scarce in Spain until summer 2020, when 77 cases were reported, eight fatal. Most cases occurred next to the Guadalquivir River in the Sevillian villages of Puebla del Río and Coria del Río. Detection of WNV disease in humans was preceded by a large increase in the abundance of Culex perexiguus in the neighbourhood of the villages where most human cases occurred. The first WNV infected mosquitoes were captured approximately one month before the detection of the first human cases. Overall, 33 positive pools of Cx. perexiguus and one pool of Culex pipiens were found. Serology of wild birds confirmed WNV circulation inside the affected villages, that transmission to humans also occurred in urban settings and suggests that virus circulation was geographically more widespread than disease cases in humans or horses may indicate. A high prevalence of antibodies was detected in blackbirds (Turdus merula) suggesting that this species played an important role in the amplification of WNV in urban areas. Culex perexiguus was the main vector of WNV among birds in natural and agricultural areas, while its role in urban areas needs to be investigated in more detail. Culex pipiens may have played some role as bridge vector of WNV between birds and humans once the enzootic transmission cycle driven by Cx. perexiguus occurred inside the villages. Surveillance of virus in mosquitoes has the potential to detect WNV well in advance of the first human cases.

Comparative analysis of spectral and temporal combinations in CSP-based methods for decoding hand motor imagery tasks.

BACKGROUND: A widely used paradigm for brain-computer interfaces (BCI) is based on the detection of event-related (des)synchronization (ERD/S) in response to hand motor imagery (MI) tasks. The common spatial pattern (CSP) has been recognized as a powerful algorithm to design spatial filters for ERD/ERS detection. However, a limitation of CSP focus on identification only of discriminative spatial information but not the spectral one. NEW METHOD: An open problem remains in literature related to extracting the most discriminative brain patterns in MI-based BCIs using an optimal time segment and spectral information that accounts for intersubject variability. In recent years, different variants of CSP-based methods have been proposed to address the problem of decoding motor imagery tasks under the intersubject variability of frequency bands related to ERD/ERS events, including Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatio-Spectral Patterns (FBCSSP). COMPARISON WITH EXISTING METHODS: We performed a comparative study of different combinations of time segments and filter banks for three methods (CSP, FBCSP, and FBCSSP) to decode hand (right and left) motor imagery tasks using two different EEG datasets (Gigascience and BCI IVa competition). RESULTS: The best configuration corresponds to a filter bank with 3 filters (8-15 Hz, 15-22 Hz and 22-29 Hz) using a time window of 1.5 s after the trigger, which provide accuracies of approximately 74% and an estimated ITRs of approximately 7 bits/min. CONCLUSION: Discriminative information in time and spectral domains could be obtained using a convenient filter bank and a time segment configuration, to enhance the classification rate and ITR for detection of hand motor imagery tasks with CSP-related methods, to be used in the implementation of a real-time BCI system.

Estimation of Joint Angle From sEMG and Inertial Measurements Based on Deep Learning Approach.

Continuous kinematics estimation from surface electromyography (sEMG) allows more natural and intuitive human-machine collaboration. Recent research has suggested the use of multimodal inputs (sEMG signals and inertial measurements) to improve estimation performance. This work focused on assessing the use of angular velocity in combination with myoelectric signals to simultaneously and continuously predict 12 joint angles in the hand. Estimation performance was evaluated for five functional and grasping movements in 20 subjects. The proposed method is based on convolutional and recurrent neural networks using transfer learning (TL). A novel aspect was the use of a pretrained deep network model from basic joint hand movements to learn new patterns present in functional motions. A comparison was carried out with the traditional method based solely on sEMG. Although the performance of the algorithm slightly improved with the use of the multimodal combination, both strategies had similar behavior. The results indicated a significant improvement for a single task: opening a bottle with a tripod grasp.

A new proteinaceous pathogen-associated molecular pattern (PAMP) identified in Ascomycete fungi induces cell death in Solanaceae.

Pathogen-associated molecular patterns (PAMPs) are detected by plant pattern recognition receptors (PRRs), which gives rise to PAMP-triggered immunity (PTI). We characterized a novel fungal PAMP, Cell Death Inducing 1 (RcCDI1), identified in the Rhynchosporium commune transcriptome sampled at an early stage of barley (Hordeum vulgare) infection. The ability of RcCDI1 and its homologues from different fungal species to induce cell death in Nicotiana benthamiana was tested following agroinfiltration or infiltration of recombinant proteins produced by Pichia pastoris. Virus-induced gene silencing (VIGS) and transient expression of Phytophthora infestans effectors PiAVR3a and PexRD2 were used to assess the involvement of known components of PTI in N. benthamiana responses to RcCDI1. RcCDI1 was highly upregulated early during barley colonization with R. commune. RcCDI1 and its homologues from different fungal species, including Zymoseptoria tritici, Magnaporthe oryzae and Neurospora crassa, exhibited PAMP activity, inducing cell death in Solanaceae but not in other families of dicots or monocots. RcCDI1-triggered cell death was shown to require N. benthamiana Brassinosteroid insensitive 1-Associated Kinase 1 (NbBAK1), N. benthamiana suppressor of BIR1-1 (NbSOBIR1) and N. benthamiana SGT1 (NbSGT1), but was not suppressed by PiAVR3a or PexRD2. We report the identification of a novel Ascomycete PAMP, RcCDI1, recognized by Solanaceae but not by monocots, which activates cell death through a pathway that is distinct from that triggered by the oomycete PAMP INF1.