Improving Pre-movement Pattern Detection with Filter Bank Selection

Pre-movement decoding plays an important role in movement detection and is able to detect movement onset with low-frequency electroencephalogram (EEG) signals before the limb moves. In related studies, pre-movement decoding with standard task-related component analysis (STRCA) has been demonstrated to be efficient for classification between movement state and resting state. However, the accuracies of STRCA differ among subbands in the frequency domain. Due to individual differences, the best subband differs among subjects and is difficult to be determined. This study aims to improve the performance of the STRCA method by a feature selection on multiple subbands and avoid the selection of best subbands. This study first compares three frequency range settings ($M_1$: subbands with equally spaced bandwidths; $M_2$: subbands whose high cut-off frequencies are twice the low cut-off frequencies; $M_3$: subbands that start at some specific fixed frequencies and end at the frequencies in an arithmetic sequence.). Then, we develop a mutual information based technique to select the features in these subbands. A binary support vector machine classifier is used to classify the selected essential features. The results show that $M_3$ is a better setting than the other two settings. With the filter banks in $M_3$, the classification accuracy of the proposed FBTRCA achieves 0.8700$\pm$0.1022, which means a significantly improved performance compared to STRCA (0.8287$\pm$0.1101) as well as to the cross validation and testing method (0.8431$\pm$0.1078)

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination\u27s complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go

On Tackling Fundamental Constraints in Brain-Computer Interface Decoding via Deep Neural Networks

A Brain-Computer Interface (BCI) is a system that provides a communication and control medium between human cortical signals and external devices, with the primary aim to assist or to be used by patients who suffer from a neuromuscular disease. Despite significant recent progress in the area of BCI, there are numerous shortcomings associated with decoding Electroencephalography-based BCI signals in real-world environments. These include, but are not limited to, the cumbersome nature of the equipment, complications in collecting large quantities of real-world data, the rigid experimentation protocol and the challenges of accurate signal decoding, especially in making a system work in real-time. Hence, the core purpose of this work is to investigate improving the applicability and usability of BCI systems, whilst preserving signal decoding accuracy. Recent advances in Deep Neural Networks (DNN) provide the possibility for signal processing to automatically learn the best representation of a signal, contributing to improved performance even with a noisy input signal. Subsequently, this thesis focuses on the use of novel DNN-based approaches for tackling some of the key underlying constraints within the area of BCI. For example, recent technological improvements in acquisition hardware have made it possible to eliminate the pre-existing rigid experimentation procedure, albeit resulting in noisier signal capture. However, through the use of a DNN-based model, it is possible to preserve the accuracy of the predictions from the decoded signals. Moreover, this research demonstrates that by leveraging DNN-based image and signal understanding, it is feasible to facilitate real-time BCI applications in a natural environment. Additionally, the capability of DNN to generate realistic synthetic data is shown to be a potential solution in reducing the requirement for costly data collection. Work is also performed in addressing the well-known issues regarding subject bias in BCI models by generating data with reduced subject-specific features. The overall contribution of this thesis is to address the key fundamental limitations of BCI systems. This includes the unyielding traditional experimentation procedure, the mandatory extended calibration stage and sustaining accurate signal decoding in real-time. These limitations lead to a fragile BCI system that is demanding to use and only suited for deployment in a controlled laboratory. Overall contributions of this research aim to improve the robustness of BCI systems and enable new applications for use in the real-world

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Brain-Computer Interfaces using Machine Learning

This thesis explores machine learning models for the analysis and classification of electroencephalographic (EEG) signals used in Brain-Computer Interface (BCI) systems. The goal is 1) to develop a system that allows users to control home-automation devices using their mind, and 2) to investigate whether it is possible to achieve this, using low-cost EEG equipment. The thesis includes both a theoretical and a practical part. In the theoretical part, we overview the underlying principles of Brain-Computer Interface systems, as well as, different approaches for the interpretation and the classification of brain signals. We also discuss the emergent launch of low-cost EEG equipment on the market and its use beyond clinical research. We then dive into more technical details that involve signal processing and classification of EEG patterns using machine leaning. Purpose of the practical part is to create a brain-computer interface that will be able to control a smart home environment. As a first step, we investigate the generalizability of different classification methods, conducting a preliminary study on two public datasets of brain encephalographic data. The obtained accuracy level of classification on 9 different subjects was similar and, in some cases, superior to the reported state of the art. Having achieved relatively good offline classification results during our study, we move on to the last part, designing and implementing an online BCI system using Python. Our system consists of three modules. The first module communicates with the MUSE (a low-cost EEG device) to acquire the EEG signals in real time, the second module process those signals using machine learning techniques and trains a learning model. The model is used by the third module, that takes control of cloud-based home automation devices. Experiments using the MUSE resulted in significantly lower classification results and revealed the limitations of the low-cost EEG signal acquisition device for online BCIs

From locomotion to dance and back : exploring rhythmic sensorimotor synchronization

Le rythme est un aspect important du mouvement et de la perception de l’environnement. Lorsque l’on danse, la pulsation musicale induit une activité neurale oscillatoire qui permet au système nerveux d’anticiper les évènements musicaux à venir. Le système moteur peut alors s’y synchroniser. Cette thèse développe de nouvelles techniques d’investigation des rythmes neuraux non strictement périodiques, tels que ceux qui régulent le tempo naturellement variable de la marche ou la perception rythmes musicaux. Elle étudie des réponses neurales reflétant la discordance entre ce que le système nerveux anticipe et ce qu’il perçoit, et qui sont nécessaire pour adapter la synchronisation de mouvements à un environnement variable. Elle montre aussi comment l’activité neurale évoquée par un rythme musical complexe est renforcée par les mouvements qui y sont synchronisés. Enfin, elle s’intéresse à ces rythmes neuraux chez des patients ayant des troubles de la marche ou de la conscience.Rhythms are central in human behaviours spanning from locomotion to music performance. In dance, self-sustaining and dynamically adapting neural oscillations entrain to the regular auditory inputs that is the musical beat. This entrainment leads to anticipation of forthcoming sensory events, which in turn allows synchronization of movements to the perceived environment. This dissertation develops novel technical approaches to investigate neural rhythms that are not strictly periodic, such as naturally tempo-varying locomotion movements and rhythms of music. It studies neural responses reflecting the discordance between what the nervous system anticipates and the actual timing of events, and that are critical for synchronizing movements to a changing environment. It also shows how the neural activity elicited by a musical rhythm is shaped by how we move. Finally, it investigates such neural rhythms in patient with gait or consciousness disorders

Addressing the challenges posed by human machine interfaces based on force sensitive resistors for powered prostheses

Despite the advancements in the mechatronics aspect of prosthetic devices, prostheses control still lacks an interface that satisfies the needs of the majority of users. The research community has put great effort into the advancements of prostheses control techniques to address users’ needs. However, most of these efforts are focused on the development and assessment of technologies in the controlled environments of laboratories. Such findings do not fully transfer to the daily application of prosthetic systems. The objectives of this thesis focus on factors that affect the use of Force Myography (FMG) controlled prostheses in practical scenarios. The first objective of this thesis assessed the use of FMG as an alternative or synergist Human Machine Interface (HMI) to the more traditional HMI, i.e. surface Electromyography (sEMG). The assessment for this study was conducted in conditions that are relatively close to the real use case of prosthetic applications. The HMI was embedded in the custom prosthetic prototype that was developed for the pilot participant of the study using an off-the-shelf prosthetic end effector. Moreover, prostheses control was assessed as the user moved their limb in a dynamic protocol.The results of the aforementioned study motivated the second objective of this thesis: to investigate the possibility of reducing the complexity of high density FMG systems without sacrificing classification accuracies. This was achieved through a design method that uses a high density FMG apparatus and feature selection to determine the number and location of sensors that can be eliminated without significantly sacrificing the system’s performance. The third objective of this thesis investigated two of the factors that contribute to increased errors in force sensitive resistor (FSR) signals used in FMG controlled prostheses: bending of force sensors and variations in the volume of the residual limb. Two studies were conducted that proposed solutions to mitigate the negative impact of these factors. The incorporation of these solutions into prosthetic devices is discussed in these studies.It was demonstrated that FMG is a promising HMI for prostheses control. The facilitation of pattern recognition with FMG showed potential for intuitive prosthetic control. Moreover, a method for the design of a system that can determine the required number of sensors and their locations on each individual to achieve a simpler system with comparable performance to high density FMG systems was proposed and tested. The effects of the two factors considered in the third objective were determined. It was also demonstrated that the proposed solutions in the studies conducted for this objective can be used to increase the accuracy of signals that are commonly used in FMG controlled prostheses

Deep learning classification model of mental workload levels using EEG signals

Understanding and improving humance performance, especially in situations that require safety, productivity, and well-being, relies on categorising mental workload (MWL). Traditional methods for measuring MWL, such as in driving and piloting, have given us some understanding, but these methods must accurately distinguish between low and high workload levels. Excessive work can tyre participants, while insufficient work can make them bored and inefficient. Traditional MWL assessment tools, such as questionnaires, sometimes make it harder for people to manage their MWL, especially when they struggle to express or understand their thoughts and feelings. The recent work shift to neurophysiological signals, specifically electroencephalogram (EEG), provides a promising way to measure brain activity related to MWL non-invasively. Advanced techniques such as deep learning have made it easier to study EEG signals in more detail. Our goal was to develop a clear and consistent approach for using EEG signals to classify MWL effectively. Our approach focused on each process stage, from preparing the data to evaluating the model and addressing common mistakes and misunderstandings in current techniques. The first study addresses the challenges of using EEG data contaminated by artefacts for assessing MWL. EEG signal artefacts, such as eye movement or muscle activity, can skew MWL assessment. Recently, there has been significant progress in using deep learning models to interpret EEG signals, but the challenge remains. The preprocessing pipeline for EEG artefact removal is broad and inconsistently adopted; some pipelines are time-consuming and contain human intervention steps, so they are unsuitable for automation systems. Therefore, this study focused on automatic EEG artefact removal for deep learning analysis. Furthermore, we examined the impact of various preprocessing techniques on the effectiveness of deep learning models in classifying MWL levels. We used state-of-the-art models such as Stacked LSTM, BLSTM, and BLSTM-LSTM, and found that certain techniques—specifically, the ADJUST algorithm—significantly enhanced model performance. However, the sophisticated models could extract relevant information from raw data, indicating a reduced need for preprocessing. The second study shifted the focus to channel selection to refine the automation of MWL classification and reduce unnecessary computational expenses by using unnecessary electrodes, aligning more closely to real-world applications. We prioritised the best electrode setup focusing on brain activity related to MWL. We removed unnecessary data using Riemannian geometry, an effective method for EEG channel selection. We aimed to balance information sufficiency with computational efficiency and to reduce the number of electrodes. The study also evaluated covariance estimators for Riemannian geometry for their effectiveness in channel selection and impact on deep learning models for MWL classification, as the traditional Empirical Covariance (EC) has limitations for the EEG signal. Finally, the third study tackled a critical but frequently overlooked aspect of MWL level classification using machine learning or deep learning techniques: the temporal nature of EEG signals. We underscored that the traditional cross-validation technique violates the sequential nature of time series data, leading to data leakage, model overfitting, and inaccurate MWL assessment. Specifically, to predict the subject’s MWL level, we could not randomly split data and use future data to train the model and predict the previous MWL level. To address this problem, this study focused on the model training phase, specifically on the importance of time series cross-validation methods. We adopted the expanding window and rolling window strategies, finding that using the expanding window strategy outperformed those using the rolling window strategy. This research carefully developed a comprehensive and consistent method for classifying MWL using EEG signals. We aimed to correct misunderstandings and set a standard in brain-computer interface (BCI) systems. This will help guide future research and development efforts.Understanding and improving humance performance, especially in situations that require safety, productivity, and well-being, relies on categorising mental workload (MWL). Traditional methods for measuring MWL, such as in driving and piloting, have given us some understanding, but these methods must accurately distinguish between low and high workload levels. Excessive work can tyre participants, while insufficient work can make them bored and inefficient. Traditional MWL assessment tools, such as questionnaires, sometimes make it harder for people to manage their MWL, especially when they struggle to express or understand their thoughts and feelings. The recent work shift to neurophysiological signals, specifically electroencephalogram (EEG), provides a promising way to measure brain activity related to MWL non-invasively. Advanced techniques such as deep learning have made it easier to study EEG signals in more detail. Our goal was to develop a clear and consistent approach for using EEG signals to classify MWL effectively. Our approach focused on each process stage, from preparing the data to evaluating the model and addressing common mistakes and misunderstandings in current techniques. The first study addresses the challenges of using EEG data contaminated by artefacts for assessing MWL. EEG signal artefacts, such as eye movement or muscle activity, can skew MWL assessment. Recently, there has been significant progress in using deep learning models to interpret EEG signals, but the challenge remains. The preprocessing pipeline for EEG artefact removal is broad and inconsistently adopted; some pipelines are time-consuming and contain human intervention steps, so they are unsuitable for automation systems. Therefore, this study focused on automatic EEG artefact removal for deep learning analysis. Furthermore, we examined the impact of various preprocessing techniques on the effectiveness of deep learning models in classifying MWL levels. We used state-of-the-art models such as Stacked LSTM, BLSTM, and BLSTM-LSTM, and found that certain techniques—specifically, the ADJUST algorithm—significantly enhanced model performance. However, the sophisticated models could extract relevant information from raw data, indicating a reduced need for preprocessing. The second study shifted the focus to channel selection to refine the automation of MWL classification and reduce unnecessary computational expenses by using unnecessary electrodes, aligning more closely to real-world applications. We prioritised the best electrode setup focusing on brain activity related to MWL. We removed unnecessary data using Riemannian geometry, an effective method for EEG channel selection. We aimed to balance information sufficiency with computational efficiency and to reduce the number of electrodes. The study also evaluated covariance estimators for Riemannian geometry for their effectiveness in channel selection and impact on deep learning models for MWL classification, as the traditional Empirical Covariance (EC) has limitations for the EEG signal. Finally, the third study tackled a critical but frequently overlooked aspect of MWL level classification using machine learning or deep learning techniques: the temporal nature of EEG signals. We underscored that the traditional cross-validation technique violates the sequential nature of time series data, leading to data leakage, model overfitting, and inaccurate MWL assessment. Specifically, to predict the subject’s MWL level, we could not randomly split data and use future data to train the model and predict the previous MWL level. To address this problem, this study focused on the model training phase, specifically on the importance of time series cross-validation methods. We adopted the expanding window and rolling window strategies, finding that using the expanding window strategy outperformed those using the rolling window strategy. This research carefully developed a comprehensive and consistent method for classifying MWL using EEG signals. We aimed to correct misunderstandings and set a standard in brain-computer interface (BCI) systems. This will help guide future research and development efforts

Effective EEG analysis for advanced AI-driven motor imagery BCI systems

Developing effective signal processing for brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) involves factoring in three aspects of functionality: classification performance, execution time, and the number of data channels used. The contributions in this thesis are centered on these three issues. Contributions are focused on the classification of motor imagery (MI) data, which is generated during imagined movements. Typically, EEG time-series data is segmented for data augmentation or to mimic buffering that happens in an online BCI. A multi-segment decision fusion approach is presented, which takes consecutive temporal segments of EEG data, and uses decision fusion to boost classification performance. It was computationally lightweight and improved the performance of four conventional classifiers. Also, an analysis of the contributions of electrodes from different scalp regions is presented, and a subset of channels is recommended. Sparse learning (SL) classifiers have exhibited strong classification performance in the literature. However, they are computationally expensive. To reduce the test-set execution times, a novel EEG classification pipeline consisting of a genetic-algorithm (GA) for channel selection and a dictionary-based SL module for classification, called GABSLEEG, is presented. Subject-specific channel selection was carried out, in which the channels are selected based on training data from the subject. Using the GA-recommended subset of EEG channels reduced the execution time by 60% whilst preserving classification performance. Although subject-specific channel selection is widely used in the literature, effective subject-independent channel selection, in which channels are detected using data from other subjects, is an ideal aim because it leads to lower training latency and reduces the number of electrodes needed. A novel convolutional neural network (CNN)-based subject-independent channels selection method is presented, called the integrated channel selection (ICS) layer. It performed on-a-par with or better than subject-specific channel selection. It was computationally efficient, operating 12-17 times faster than the GA channel selection module. The ICS layer method was versatile, performing well with two different CNN architectures and datasets.Developing effective signal processing for brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) involves factoring in three aspects of functionality: classification performance, execution time, and the number of data channels used. The contributions in this thesis are centered on these three issues. Contributions are focused on the classification of motor imagery (MI) data, which is generated during imagined movements. Typically, EEG time-series data is segmented for data augmentation or to mimic buffering that happens in an online BCI. A multi-segment decision fusion approach is presented, which takes consecutive temporal segments of EEG data, and uses decision fusion to boost classification performance. It was computationally lightweight and improved the performance of four conventional classifiers. Also, an analysis of the contributions of electrodes from different scalp regions is presented, and a subset of channels is recommended. Sparse learning (SL) classifiers have exhibited strong classification performance in the literature. However, they are computationally expensive. To reduce the test-set execution times, a novel EEG classification pipeline consisting of a genetic-algorithm (GA) for channel selection and a dictionary-based SL module for classification, called GABSLEEG, is presented. Subject-specific channel selection was carried out, in which the channels are selected based on training data from the subject. Using the GA-recommended subset of EEG channels reduced the execution time by 60% whilst preserving classification performance. Although subject-specific channel selection is widely used in the literature, effective subject-independent channel selection, in which channels are detected using data from other subjects, is an ideal aim because it leads to lower training latency and reduces the number of electrodes needed. A novel convolutional neural network (CNN)-based subject-independent channels selection method is presented, called the integrated channel selection (ICS) layer. It performed on-a-par with or better than subject-specific channel selection. It was computationally efficient, operating 12-17 times faster than the GA channel selection module. The ICS layer method was versatile, performing well with two different CNN architectures and datasets