Motor Imagery (MI)-based Brain–Computer Interfaces (BCIs) have been widely used as an alternative communication channel to patients with severe motor disabilities, achieving high classification accuracy through machine learning techniques. Recently, deep learning techniques have spotlighted the state-of-the-art of MI-based BCIs. These techniques still lack strategies to quantify predictive uncertainty and may produce overconfident predictions. In this work, methods to enhance the performance of existing MI-based BCIs are proposed in order to obtain a more reliable system for real application scenarios. First, the Monte Carlo dropout (MCD) method is proposed on MI deep neural models to improve classification and provide uncertainty estimation. This approach was implemented using Shallow Convolutional Neural Network (SCNN-MCD) and with an ensemble model (E-SCNN-MCD). As another contribution, to discriminate MI task predictions of high uncertainty, a threshold approach is introduced and tested for both SCNN-MCD and E-SCNN-MCD approaches. The BCI Competition IV Databases 2a and 2b were used to evaluate the proposed methods for both subject-specific and non-subject-specific strategies, obtaining encouraging results for MI recognition.

Keywords Robotic knee exoskeleton; Electromyography; Trunk muscles; User intention recognition; Admittance control

Recent technological advances have changed how people interact, run businesses, learn, and use their free time. The advantages and facilities provided by electronic devices have played a major role. On the other hand, extensive use of such technology also has adverse effects on several aspects of human life (e.g., the development of societal sedentary lifestyles and new addictions). Smartphone dependency is new addiction that primarily affects the young population. The consequences may negatively impact mental and physical health (e.g., lack of attention or local pain). Health professionals rely on self-reported subjective information to assess the dependency level, requiring specialists' opinions to diagnose such a dependency. This study proposes a data-driven prediction model for smartphone dependency based on machine learning techniques using an analytical retrospective case–control approach. Different classification methods were applied, including classical and modern machine learning models. Students from a private university in Cali—Colombia (n = 1228) were tested for (i) smartphone dependency, (ii) musculoskeletal symptoms, and (iii) the Risk Factors Questionnaire. Random forest, logistic regression, and support vector machine-based classifiers exhibited the highest prediction accuracy, 76–77%, for smartphone dependency, estimated through the stratified-k-fold cross-validation technique. Results showed that self-reported information provides insight into predicting smartphone dependency correctly. Such an approach opens doors for future research aiming to include objective measures to increase accuracy and help to reduce the negative consequences of this new addiction form.

The development of Brain–Computer Interfaces based on Motor Imagery (MI) tasks is a relevant research topic worldwide. The design of accurate and reliable BCI systems remains a challenge, mainly in terms of increasing performance and usability. Classifiers based on Bayesian Neural Networks are proposed in this work by using the variational inference, aiming to analyze the uncertainty during the MI prediction. An adaptive threshold scheme is proposed here for MI classification with a reject option, and its performance on both datasets 2a and 2b from BCI Competition IV is compared with other approaches based on thresholds. The results using subject-specific and non-subject-specific training strategies are encouraging. From the uncertainty analysis, considerations for reducing computational cost are proposed for future work.

Keywords Electromyography; Upper-limb prosthesis; Low-density surface electromyography; Dexterous hand gestures; Pattern recognition

In sports, athletes might train or compete with some degree of mental fatigue that has an impact on performance. The present study investigated the effect of strategic motivational self-talk on endurance performance in mentally fatigued individuals. Twelve healthy men performed three cycling endurance tests at 80% peak power output until exhaustion. Two experimental conditions (mental fatigue [MF] and mental fatigue plus motivational self-talk [MF + M−ST]) and a control condition (CO) were set out. Mental fatigue was induced via 30 min Stroop task; an electroencephalogram of the frontal cerebral cortex was continually monitored. Affective state and perceived exertion were recorded during each session and subjective workload after the endurance exercise trials. Theta band power spectral density was significantly higher in both experimental condition (MF and MF + M−ST) compared to CO, without significant differences between MF and MF + M−ST. Endurance performance was significantly worse in MF (227 ± 63s) than in CO (329 ± 84s). However, motivational self-talk reversed the deleterious effect of MF on endurance performance as in MF + M−ST (368 ± 139s) it was significantly better than MF and not significantly different from CO. Displeasure and perceived exertion scores were similar at the end of exercise and presented similar rate of increase among conditions. Mental demand after exercise was significantly greater in MF than in CO condition. These results suggest that motivational self-talk mitigates the negative effect of MF on endurance performance due to an elevated engagement with the task. Lay summary: This study examined to what extent the deliberate and planned use of cue words to enhance motivation (i.e., motivational self-talk), influenced endurance performance in mentally fatigued individuals. The harmful impact of mental fatigue on endurance performance was mitigated by using motivational self-talk during cycling exercise.