EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots

Over recent years, brain-computer interface (BCI) has emerged as an alternative communication system between the human brain and an output device. Deciphered intents, after detecting electrical signals from the human scalp, are translated into control commands used to operate external devices, computer displays and virtual objects in the real-time. BCI provides an augmentative communication by creating a muscle-free channel between the brain and the output devices, primarily for subjects having neuromotor disorders, or trauma to nervous system, notably spinal cord injuries (SCI), and subjects with unaffected sensorimotor functions but disarticulated or amputated residual limbs. This review identifies the potentials of electroencephalography (EEG) based BCI applications for locomotion and mobility rehabilitation. Patients could benefit from its advancements such as, wearable lower-limb (LL) exoskeletons, orthosis, prosthesis, wheelchairs, and assistive-robot devices. The EEG communication signals employed by the aforementioned applications that also provide feasibility for future development in the field are sensorimotor rhythms (SMR), event-related potentials (ERP) and visual evoked potentials (VEP). The review is an effort to progress the development of user's mental task related to LL for BCI reliability and confidence measures. As a novel contribution, the reviewed BCI control paradigms for wearable LL and assistive-robots are presented by a general control framework fitting in hierarchical layers. It reflects informatic interactions, between the user, the BCI operator, the shared controller, the robotic device and the environment. Each sub layer of the BCI operator is discussed in detail, highlighting the feature extraction, classification and execution methods employed by the various systems. All applications' key features and their interaction with the environment are reviewed for the EEG-based activity mode recognition, and presented in form of a table. It i

Optimizing the performances of a P300-based brain-computer interface in ambulatory conditions

Brain-computer interfaces (BCIs) enable their users to interact with their surrounding environment using the activity of their brain only, without activating any muscle. This technology provides severely disabled people with an alternative mean to communicate or control any electric device. On the other hand, BCI applications are more and more dedicated to healthier people, with the aim of giving them access to augmented reality or new rehabilitation tools. As it is noninvasive, light and relatively cheap, electroencephalography (EEG) is the most used acquisition technique to record cerebral activity of the BCI users. However, when using such type of BCI, user movements are likely to provoke motions of the measuring electrodes which can severely damage the EEG quality. Thus, current BCI technology requires that the user sits and performs as little movements as possible. This is of course a strong limitation of BCI for use in ordinary life. Very recently, preliminary studies have been published in the literature and suggest that BCI applications can be realized even in the physically moving context. In this paper, we thoroughly investigate the possibility to develop a P300-based BCI system in ambulatory condition. The study is based on experimental data recorded with seven subjects executing a visual P300 speller-like discrimination task while simultaneously walking at different speeds on a treadmill. It is demonstrated that a P300-based BCI is definitely feasible in such conditions. Different artifact correction methods are described and discussed in detail. To conclude, a recommended approach is given for the development of a real-time application. © 2011 IEEE.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Development and applications of a smartphone-based mobile electroencephalography (EEG) system

Electroencephalography (EEG) is a clinical and research technique used to non-invasively acquire brain activity. EEG is performed using static systems in specialist laboratories where participant mobility is constrained. It is desirable to have EEG systems which enable acquisition of brain activity outside such settings. Mobile systems seek to reduce the constraining factors of EEG device and participant mobility to enable recordings in various environments but have had limited success due to various factors including low system specification. The main aim of this thesis was to design, build, test and validate a novel smartphone-based mobile EEG system.A literature review found that the term ‘mobile EEG’ has an ambiguous meaning as researchers have used it to describe many differing degrees of participant and device mobility. A novel categorisation of mobile EEG (CoME) scheme was derived from thirty published EEG studies which defined scores for participant and device mobilities, and system specifications. The CoME scheme was subsequently applied to generate a specification for the proposed mobile EEG system which had 24 channels, sampled at 24 bit at a rate of 250 Hz. Unique aspects of the EEG system were the introduction of a smartphone into the specification, along with the use of Wi-Fi for communications. The smartphone’s processing power was used to remotely control the EEG device so as to enable EEG data capture and storage as well as electrode impedance checking via the app. This was achieved by using the Unity game engine to code an app which provided the flexibility for future development possibilities with its multi-platform support.The prototype smartphone-based waist-mounted mobile EEG system (termed ‘io:bio’) was validated against a commercial FDA clinically approved mobile system (Micromed). The power spectral frequency, amplitude and area of alpha frequency waves were determined in participants with their eyes closed in various postures: lying, sitting, standing and standing with arms raised. Since a correlation analysis to compare two systems has interpretability problems, Bland and Altman plots were utilised with a priori justified limits of agreement to statistically assess the agreement between the two EEG systems. Overall, the results found similar agreements between the io:bio and Micromed systems indicating that the systems could be used interchangeably. Utilising the io:bio and Micromed systems in a walking configuration, led to contamination of EEG channels with artifacts thought to arise from movement and muscle-related sources, and electrode displacement.To enable an event related potential (ERP) capability of the EEG system, additional coding of the smartphone app was undertaken to provide stimulus delivery and associated data marking. Using the waist-mounted io:bio system, an auditory oddball paradigm was also coded into the app, and delivery of auditory tones (standard and deviant) to the participant (sitting posture) achieved via headphones connected to the smartphone. N100, N200 and P300 ERP components were recorded in participants sitting, and larger amplitudes were found for the deviant tones compared to the standard ones. In addition, when the paradigm was tested in individual participants during walking, movement-related artifacts impacted negatively upon the quality of the ERP components, although components were discernible in the grand mean ERP.The io:bio system was redesigned into a head-mounted configuration in an attempt to reduce EEG artifacts during participant walking. The initial approach taken to redesign the system involved using electronic components populated onto a flexible PCB proved to be non-robust. Instead, the rigid PCB form of the circuitry was taken from the io:bio waist-mounted system and placed onto the rear head section of the electrode cap via a bespoke cradle. Using this head-mounted system, in a preliminary auditory oddball paradigm study, ERP responses were obtained in participants whilst walking. Initial results indicate that artifacts are reduced in this head-mounted configuration, and N100, N200 and P300 components are clearly identifiable in some channels

Desarrollo de un modelo de calidad para la evaluación de calidad en uso y de producto de sistemas que utilizan interfaces cerebrocomputador (Brain Computer-Interface)

En la actualidad, la tecnología se ha convertido en una parte fundamental en la vida cotidiana de todas las personas, con ella, se han descubierto diferentes maneras de interacción con el usuario. Entre las formas más innovadoras de interacción humanocomputador están aquellas en las que interviene la vista, la voz, interacción mediante pantallas táctiles y en los últimos años ha ganado importancia la interacción cerebrocomputador gracias a los dispositivos que promueven las interfaces cerebro computador (BCI-Brain Computer Interfaces); ya que, con esta interacción, la ciencia busca brindar ayuda a aquellas personas que sufren de ciertas discapacidades que dificultan su interacción con el entorno. Sin embargo, al implementar aplicaciones con dichas interfaces, se mejora la calidad de vida de estas personas, al permitirlas interactuar con su entorno mediante el cerebro. Por tal motivo Brain Computer Interface ha ganado interés en diversos lugares del mundo, en varios países se desarrollan nuevos sistemas centrados en este tipo de tecnología; ya que el objetivo principal de un sistema BCI, consiste en crear una conexión especializada, que permita al usuario tener un control sobre diferentes dispositivos, por ejemplo: computadoras, prótesis neurológicas, tecnologías productoras de voz, etc. El presente trabajo de titulación, propone un nuevo método para evaluar la calidad en uso y de producto, existente en los sistemas BCI, el cual tiene el nombre de BCISQUAM. Dicho método de evaluación emplea un modelo de calidad, elaborado a partir de una revisión sistemática de la literatura, en donde se analizan algunos aspectos que permiten comprender el avance científico en esta área de la ciencia y de esta manera, alcanzar una correcta evaluación del sistema BCI. Finalmente, para mostrar la factibilidad del método propuesto, se desarrolló un sistema basado que hace uso de interfaces cerebro-computador, el cual fue empleado en 2 Juan Manuel Cobos Quinde Christian Alexander Moreira Jara un cuasiexperimento para probar la viabilidad de la solución propuesta. El método de evaluación, tiene como público objetivo los ingenieros de software, cuya población se ha buscado representar con 20 ingenieros de software, como una forma de evaluación inicial al método, sin embargo, en trabajos futuros se busca alcanzar un mayor número de sujetos. Los participantes fueron preparados con los conocimientos necesarios para desarrollar el cuasiexperimento; dando como resultado que el método de evaluación es fácil de utilizar, útil y genera una intención de uso en el futuro. Además, se obtuvo información para trabajos futuros.Currently, technology has become a fundamental part of everyone's daily life, with it, different ways of interacting with the user have been discovered. Among the most innovative forms of human-computer interaction are those in which brain-computer interaction is involved, and in recent years brain-computer interaction has gained importance thanks to devices that promote brain-computer interfaces (BCI-Brain Computer Interfaces); since, with this interaction, science seeks to provide help to those people who suffer from certain disabilities that make it difficult for them to interact with the environment. However, by implementing applications with these interfaces, the quality of life of these people is improved, by allowing them to interact with their environment through the brain. For this reason, Brain Computer Interface has gained interest in various parts of the world, in several countries new systems focused on this type of interaction technology are being developed; since the main objective of a BCI system is to create a specialized connection that allows the user to have control over different devices, for example: computers, neurological prostheses, voice-producing technologies, etc. The present titling work proposes a new method to evaluate the quality in use and of the product, existing in the BCI systems, which has the name of BCIS-QUAM. Said evaluation method uses a quality model, elaborated from a systematic review, where some aspects are analyzed that allow understanding the scientific advance in this area of science and in this way, achieve a correct evaluation of the BCI system. Finally, to show the feasibility of the proposed method, a system based on braincomputer interaction interfaces was developed, which was used in a quasi-experiment to test the feasibility of the proposed solution. The evaluation method has software engineers as its target audience, whose population has been sought to represent with 20 software engineers, as a form of initial evaluation of the method, however, in future works it is sought to reach a greater number of subjects. The participants were prepared with the necessary knowledge to develop the quasi-experiment; resulting in the evaluation method being easy to use, useful and generating an intention to use in the future. In addition, information was obtained for future work.Ingeniero de SistemasCuenc

Analysis of sensorimotor rhythms based on lower-limbs motor imagery for brain-computer interface

Over recent years significant advancements in the field of assistive technologies have been observed. One of the paramount needs for the development and advancement that urged researchers to contribute in the field other than congenital or diagnosed chronic disorders, is the rising number of affectees from accidents, natural calamity (due to climate change), or warfare, worldwide resulting in spinal cord injuries (SCI), neural disorder, or amputation (interception) of limbs, that impede a human to live a normal life. In addition to this, more than ten million people in the world are living with some form of handicap due to the central nervous system (CNS) disorder, which is precarious. Biomedical devices for rehabilitation are the center of research focus for many years. For people with lost motor control, or amputation, but unscathed sensory control, instigation of control signals from the source, i.e. electrophysiological signals, is vital for seamless control of assistive biomedical devices. Control signals, i.e. motion intentions, arouse    in the sensorimotor cortex of the brain that can be detected using invasive or non-invasive modality. With non-invasive modality, the electroencephalography (EEG) is used to record these motion intentions encoded in electrical activity of the cortex, and are deciphered to recognize user intent for locomotion. They are further transferred to the actuator, or end effector of the assistive device for control purposes. This can be executed via the brain-computer interface (BCI) technology. BCI is an emerging research field that establishes a real-time bidirectional connection between the human brain and a computer/output device. Amongst its diverse applications, neurorehabilitation to deliver sensory feedback and brain controlled biomedical devices for rehabilitation are most popular. While substantial literature on control of upper-limb assistive technologies controlled via BCI is there, less is known about the lower-limb (LL) control of biomedical devices for navigation or gait assistance via BCI. The types  of EEG signals compatible with an independent BCI are the oscillatory/sensorimotor rhythms (SMR) and event-related potential (ERP). These signals have successfully been used in BCIs for navigation control of assistive devices. However, ERP paradigm accounts for a voluminous setup for stimulus presentation to the user during operation of BCI assistive device. Contrary to this, the SMR does not require large setup for activation of cortical activity; it instead depends on the motor imagery (MI) that is produced synchronously or asynchronously by the user. MI is a covert cognitive process also termed kinaesthetic motor imagery (KMI) and elicits clearly after rigorous training trials, in form of event-related desynchronization (ERD) or synchronization (ERS), depending on imagery activity or resting period. It usually comprises of limb movement tasks, but is not limited to it in a BCI paradigm. In order to produce detectable features that correlate to the user¿s intent, selection of cognitive task is an important aspect to improve the performance of a BCI. MI used in BCI predominantly remains associated with the upper- limbs, particularly hands, due to the somatotopic organization of the motor cortex. The hand representation area is substantially large, in contrast to the anatomical location of the LL representation areas in the human sensorimotor cortex. The LL area is located within the interhemispheric fissure, i.e. between the mesial walls of both hemispheres of the cortex. This makes it arduous to detect EEG features prompted upon imagination of LL. Detailed investigation of the ERD/ERS in the mu and beta oscillatory rhythms during left and right LL KMI tasks is required, as the user¿s intent to walk is of paramount importance associated to everyday activity. This is an important area of research, followed by the improvisation of the already existing rehabilitation system that serves the LL affectees. Though challenging, solution to these issues is also imperative for the development of robust controllers that follow the asynchronous BCI paradigms to operate LL assistive devices seamlessly. This thesis focusses on the investigation of cortical lateralization of ERD/ERS in the SMR, based on foot dorsiflexion KMI and knee extension KMI separately. This research infers the possibility to deploy these features in real-time BCI by finding maximum possible classification accuracy from the machine learning (ML) models. EEG signal is non-stationary, as it is characterized by individual-to-individual and trial-to-trial variability, and a low signal-to-noise ratio (SNR), which is challenging. They are high in dimension with relatively low number of samples available for fitting ML models to the data. These factors account for ML methods that were developed into the tool of choice  to analyse single-trial EEG data. Hence, the selection of appropriate ML model for true detection of class label with no tradeoff of overfitting is crucial. The feature extraction part of the thesis constituted of testing the band-power (BP) and the common spatial pattern (CSP) methods individually. The study focused on the synchronous BCI paradigm. This was to ensure the exhibition of SMR for the possibility of a practically viable control system in a BCI. For the left vs. right foot KMI, the objective was to distinguish the bilateral tasks, in order to use them as unilateral commands in a 2-class BCI for controlling/navigating a robotic/prosthetic LL for rehabilitation. Similar was the approach for left-right knee KMI. The research was based on four main experimental studies. In addition to the four studies, the research is also inclusive of the comparison of intra-cognitive tasks within the same limb, i.e. left foot vs. left knee and right foot vs. right knee tasks, respectively (Chapter 4). This added to another novel contribution towards the findings based on comparison of different tasks within the same LL. It provides basis to increase the dimensionality of control signals within one BCI paradigm, such as a BCI-controlled LL assistive device with multiple degrees of freedom (DOF) for restoration of locomotion function. This study was based on analysis of statistically significant mu ERD feature using BP feature extraction method. The first stage of this research comprised of the left vs. right foot KMI tasks, wherein the ERD/ERS that elicited in the mu-beta rhythms were analysed using BP feature extraction method (Chapter 5). Three individual features, i.e. mu ERD, beta ERD, and beta ERS were investigated on EEG topography and time-frequency (TF) maps, and average time course of power percentage, using the common average reference and bipolar reference methods. A comparative study was drawn for both references to infer the optimal method. This was followed by ML, i.e. classification of the three feature vectors (mu ERD, beta ERD, and beta ERS), using linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbour (KNN) algorithms, separately. Finally, the multiple correction statistical tests were done, in order to predict maximum possible classification accuracy amongst all paradigms for the most significant feature. All classifier models were supported with the statistical techniques of k-fold cross validation and evaluation of area under receiver-operator characteristic curves (AUC-ROC) for prediction of the true class label. The highest classification accuracy of 83.4% ± 6.72 was obtained with KNN model for beta ERS feature. The next study was based on enhancing the classification accuracy obtained from previous study. It was based on using similar cognitive tasks as study in Chapter 5, however deploying different methodology for feature extraction and classification procedure. In the second study, ERD/ERS from mu and beta rhythms were extracted using CSP and filter bank common spatial pattern (FBCSP) algorithms, to optimize the individual spatial patterns (Chapter 6). This was followed by ML process, for which the supervised logistic regression (Logreg) and LDA were deployed separately. Maximum classification accuracy resulted in 77.5% ± 4.23 with FBCSP feature vector and LDA model, with a maximum kappa coefficient of 0.55 that is in the moderate range of agreement between the two classes. The left vs. right foot discrimination results were nearly same, however the BP feature vector performed better than CSP. The third stage was based on the deployment of novel cognitive task of left vs. right knee extension KMI. Analysis of the ERD/ERS in the mu-beta rhythms was done for verification of cortical lateralization via BP feature vector (Chapter 7). Similar to Chapter 5, in this study the analysis of ERD/ERS features was done on the EEG topography and TF maps, followed by the determination of average time course and peak latency of feature occurrence. However, for this study, only mu ERD and beta ERS features were taken into consideration and the EEG recording method only comprised of common average reference. This was due to the established results from the foot study earlier, in Chapter 5, where beta ERD features showed less average amplitude. The LDA and KNN classification algorithms were employed. Unexpectedly, the left vs. right knee KMI reflected the highest accuracy of 81.04% ± 7.5 and an AUC-ROC = 0.84, strong enough to be used in a real-time BCI as two independent control features. This was using KNN model for beta ERS feature. The final study of this research followed the same paradigm as used in Chapter 6, but for left vs. right knee KMI cognitive task (Chapter 8). Primarily this study aimed at enhancing the resulting accuracy from Chapter 7, using CSP and FBCSP methods with Logreg and LDA models respectively. Results were in accordance with those of the already established foot KMI study, i.e. BP feature vector performed better than the CSP. Highest classification accuracy of 70.00% ± 2.85 with kappa score of 0.40 was obtained with Logreg using FBCSP feature vector. Results stipulated the utilization of ERD/ERS in mu and beta bands, as independent control features for discrimination of bilateral foot or the novel bilateral knee KMI tasks. Resulting classification accuracies implicate that any 2-class BCI, employing unilateral foot, or knee KMI, is suitable for real-time implementation. In conclusion, this thesis demonstrates the possible EEG pre-processing, feature extraction and classification methods to instigate a real-time BCI from the conducted studies. Following this, the critical aspects of latency in information transfer rate, SNR, and tradeoff between dimensionality and overfitting needs to be taken care of, during design of real-time BCI controller. It also highlights that there is a need for consensus over the development of standardized methods of cognitive tasks for MI based BCI. Finally, the application of wireless EEG for portable assistance is essential as it will contribute to lay the foundations of the development of independent asynchronous BCI based on SMR

Neural Correlates of Human Cognition in Real-World Environments

According to embodied accounts of human cognition, the mind is at the interface of the body and the environment. For practical reasons, however, neuroscientific research on human cognition has mostly been confined to the laboratory until now. The emergence of portable brain and body imaging research methods offers an unprecedented opportunity to capture the expression of cognitive processes during active behaviours performed in real-world contexts. In the present thesis, electroencephalography (EEG) was used to investigate embodied aspects of human cognition in motion and in the real-world. This approach, however, presents new challenges in terms of signal processing because of the increased noise related to whole body movements. As the necessary signal processing tools were not well-established, the current work involved the development of new solutions to address the specific requirements of mobile EEG data before real-world brain recordings could be validly interpreted. In a series of Event Related Potential (ERP) experiments, real-world conditions were compared to traditional lab-based conditions. The neural marker of attention (P300 ERP) was recorded when participants performed an attentional task while walking through the university’s corridors versus standing in the lab. Differences in the classic P300 ERP effect show that attentional processes in the real-world are not the same as those recorded in the lab. Following up on this finding, the attenuation of the P300 effect under real-world conditions was shown to be driven by cognitive demands related to displacement through space rather than the act of walking itself. This is a demonstration, at a brain level, that when walking in the real-world, cognitive resources are reallocated to the processing of visual flow and vestibular information associated with displacement. The findings reflect the dynamic interplay between mind, body, and environment, providing innovative evidence strengthening the embodied framework of human cognition. The same dynamic interplay between body, environment and cognitive function is uniquely represented in real-world navigation. The literature on spatial navigation in humans, however, mainly involved navigating virtual reality environments often while lying on a scanner bed. Most of the evidence on the neural markers of spatial navigation comes from intracranially recorded brain oscillations in rodents. The innovation in this thesis was to investigate brain oscillations associated with cognitive function underlying real-world navigation in humans using surface electrodes. The present work demonstrates that human brain dynamics related to navigational cognitive processes can be recorded in active exploration of real-world environments. The key finding resulting from this novel approach is that real-world spatial navigation is associated with specific neural signatures underlying distinct cognitive functions. Frontal low-frequency oscillations were found to be associated with wayfinding, while parietal high-frequency oscillations were associated with spatial memory. Furthermore, these neural correlates were found to be dynamically modulated depending on the body’s contextual positioning within the environment. Therefore, these findings again provide evidence in support of the embodiment theory of cognition. The final study addressed the concern that findings might reflect walking speed variation. The existing animal literature has shown that low-frequency bands are modulated by walking speed. This study characterised the specific modulations in spectral power as a function of walking speed in humans. Critically the pattern showed no similarity to the spectral patterns found in relation to real-world spatial navigation, confirming the cognitive interpretation of this work. Taken together, these findings provide innovative real-world evidence supporting the theoretical embodiment framework. The neural correlates of attention, memory, and spatial navigation were found to be modulated by the dynamic experience of one’s environment. Beyond this work’s theoretical implications for cognitive sciences, the present findings offer new perspectives for real-world application