An Optimal Transport Based Transferable System for Detection of Erroneous Somato-Sensory Feedback from Neural Signals

This study is aimed at the detection of single-trial feedback, perceived as erroneous by the user, using a transferable classification system while conducting a motor imagery brain–computer interfacing (BCI) task. The feedback received by the users are relayed from a functional electrical stimulation (FES) device and hence are somato-sensory in nature. The BCI system designed for this study activates an electrical stimulator placed on the left hand, right hand, left foot, and right foot of the user. Trials containing erroneous feedback can be detected from the neural signals in form of the error related potential (ErrP). The inclusion of neuro-feedback during the experiments indicated the possibility that ErrP signals can be evoked when the participant perceives an error from the feedback. Hence, to detect such feedback using ErrP, a transferable (offline) decoder based on optimal transport theory is introduced herein. The offline system detects single-trial erroneous trials from the feedback period of an online neuro-feedback BCI system. The results of the FES-based feedback BCI system were compared to a similar visual-based (VIS) feedback system. Using our framework, the error detector systems for both the FES and VIS feedback paradigms achieved an F1-score of 92.66% and 83.10%, respectively, and are significantly superior to a comparative system where an optimal transport was not used. It is expected that this form of transferable and automated error detection system compounded with a motor imagery system will augment the performance of a BCI and provide a better BCI-based neuro-rehabilitation protocol that has an error control mechanism embedded into it

Development of explainable AI-based predictive models for bubbling fluidised bed gasification process

© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).In this study, seven different types of regression-based predictive modelling techniques are used to predict the product gas composition (H2, CO, CO2, CH4) and gas yield (GY) during the gasification of biomass in a fluidised bed reactor. The performance of different regression-based models is compared with the gradient boosting model(GB) to show the relative merits and demerits of the technique. Additionally, S Hapley Additive ex Planations (SHAP)-based explainable artificial intelligence (XAI) method was utilised to explain individual predictions. This study demonstrates that the prediction performance of the GB algorithm was the best among other regression based models i.e. Linear Regression (LR), Multilayer perception (MLP), Ridge Regression (RR), Least-angle regression (LARS), Random Forest (RF) and Bagging (BAG). It was found that at learning rate (lr) 0.01 and number of boosting stages (est) 1000 yielded the best result with an average root mean squared error (RMSE) of0.0597 for all outputs. The outcome of this study indicates that XAI-based methodology can be used as a viable alternative modelling paradigm in predicting the performance of a fluidised bed gasifier for an informed decision-making process.Peer reviewe

A study on the effect of Electrical Stimulation during motor imagery learning in Brain-computer interfacing

International audienceFunctional Electrical Stimulation (FES) stimulates the affected region of the human body thus providing a neu-roprosthetic interface to non-recovered muscle groups. FES in combination with Brain-computer interfacing (BCI) has a wide scope in rehabilitation because this system can directly link the cerebral motor intention of the users with its corresponding peripheral mucle activations. Such a rehabilitative system would contribute to improve the cortical and peripheral learning and thus, improve the recovery time of the patients. In this paper, we examine the effect of electrical stimulation by FES on the electroencephalography (EEG) during learning of a motor imagery task. The subjects are asked to perform four motor imagery tasks over six sessions and the features from the EEG are extracted using common spatial algorithm and decoded using linear discriminant analysis classifier. Feedback is provided in form of a visual medium and electrical stimulation representing the distance of the features from the hyperplane. Results suggest a significant improvement in the classification accuracy when the subject was induced with electrical stimulation along with visual feedback as compared to the standard visual one

Augmenting Motor Imagery Learning for Brain–Computer Interfacing Using Electrical Stimulation as Feedback

International audienceBrain-computer Interfaces (BCI) and Functional electrical stimulation (FES) contribute significantly to induce cortical learning and to elicit peripheral neuronal activation processes and thus, are highly effective to promote motor recovery. This study aims at understanding the effect of FES as a neural feedback and its influence on the learning process for motor imagery tasks while comparing its performance with a classical visual feedback protocol. The participants were randomly separated into two groups: one group was provided with visual feedback (VIS) while the other received electrical stimulation (FES) as feedback. Both groups performed various motor imagery tasks while feedback was provided in form of a bi-directional bar for VIS group and targeted electrical stimulation on the upper and lower limbs for FES group. The results shown in this paper suggest that the FES based feedback is more intuitive to the participants, hence, the superior results as compared to the visual feedback. The results suggest that the convergence of BCI with FES modality could improve the learning of the patients both in terms of accuracy and speed and provide a practical solution to the BCI learning process in rehabilitation

IoT-Based Applications in Healthcare Devices

The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things (IoT) has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up-to-date summary of the potential healthcare applications of IoT- (HIoT-) based technologies. Herein, the advancement of the application of the HIoT has been reported from the perspective of enabling technologies, healthcare services, and applications in solving various healthcare issues. Moreover, potential challenges and issues in the HIoT system are also discussed. In sum, the current study provides a comprehensive source of information regarding the different fields of application of HIoT intending to help future researchers, who have the interest to work and make advancements in the field to gain insight into the topic

Deep Learning based Prediction of EEG Motor Imagery of Stroke Patients' for Neuro-Rehabilitation Application

Due to the non-stationary nature of electroencephalography (EEG) signals, a Brain-computer Interfacing (BCI) system requires frequent calibration. This leads to intersession inconsistency which is one of the main reason that impedes the widespread adoption of non-invasive BCI for realworld applications, especially in rehabilitation and medicine. Domain adaptation and deep learning-based techniques have gained relevance in designing calibration-free BCIs to solve this issue. EEGNet is one such deep net architecture that has been successful in performing inter-subject classification, albeit on data from healthy participants. This is the first paper, which tests the performance of EEGNet on data obtained from 10 hemiparetic stroke patients while performing left and right motor imagery tasks. Results obtained on implementing EEGNet have been promising and it has comparably good performance as from expensive feature engineering-based approaches for both withinsubject and cross-subject classification. The less dependency on feature engineering techniques and the ability to extract generalized features for inter-subject classification makes EEGNet a promising deep-learning architecture for developing practically feasible solutions for BCI based neuro-rehabilitation applications

Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance

Research and development of new machine learning techniques to augment the performance of Brain-computer Interfaces (BCI) have always been an open area of interest among researchers. The need to develop robust and generalised classifiers has been one of the vital requirements in BCI for realworld application. EEGNet is a compact CNN model that had been reported to be generalised for different BCI paradigms. In this paper, we have aimed at further improving the EEGNet architecture by employing Neural Structured Learning (NSL) that taps into the relational information within the data to regularise the training of the neural network. This would allow the EEGNet to make better predictions while maintaining the structural similarity of the input. In addition to better performance, the combination of EEGNet and NSL is more robust, works well with smaller training samples and requires on separate feature engineering, thus saving the computational cost. The proposed approach had been tested on two standard motor imagery datasets: the first being a two-class motor imagery dataset from Graz University and the second is the 4-class Dataset 2a from BCI competition 2008. The accuracy has shown that our combined EEGNet an NSL approach is superior to the sole EEGNet model