BCI-Based Navigation in Virtual and Real Environments

A Brain-Computer Interface (BCI) is a system that enables people to control an external device with their brain activity, without the need of any muscular activity. Researchers in the BCI field aim to develop applications to improve the quality of life of severely disabled patients, for whom a BCI can be a useful channel for interaction with their environment. Some of these systems are intended to control a mobile device (e. g. a wheelchair). Virtual Reality is a powerful tool that can provide the subjects with an opportunity to train and to test different applications in a safe environment. This technical review will focus on systems aimed at navigation, both in virtual and real environments.This work was partially supported by the Innovation, Science and Enterprise Council of the Junta de Andalucía (Spain), project P07-TIC-03310, the Spanish Ministry of Science and Innovation, project TEC 2011-26395 and by the European fund ERDF

Measurement of event-related potentials and placebo

ERP is common abbreviation for event-related brain potentials, which are measured and used in clinical practice as well as in research practice. Contemporary studies of placebo effect are often based on functional neuromagnetic resonance (fMRI), positron emission tomography (PET), and event related potentials (ERP). This paper considers an ERP instrumentation system used in experimental researches of placebo effect. This instrumentation system can be divided into four modules: electrodes and cables, conditioning module, digital measurement module, and PC module for stimulations, presentations, acquisition and data processing. The experimental oddball paradigm is supported by the software of the instrumentation

Output-Feedback Shared-Control for Fully Actuated Linear Mechanical Systems

This paper presents an output feedback shared-control algorithm for fully-actuated, linear, mechanical systems. The feasible configurations of the system are described by a group of linear inequalities which characterize a convex admissible set. The properties of the shared-control algorithm are established with a Lyapunov-like analysis. Simple numerical examples demonstrate the effectiveness of the strategy

Measurement of event-related potentials and placebo

ERP is common abbreviation for event-related brain potentials, which are measured and used in clinical practice as well as in research practice. Contemporary studies of placebo effect are often based on functional neuromagnetic resonance (fMRI), positron emission tomography (PET), and event related potentials (ERP). This paper considers an ERP instrumentation system used in experimental researches of placebo effect. This instrumentation system can be divided into four modules: electrodes and cables, conditioning module, digital measurement module, and PC module for stimulations, presentations, acquisition and data processing. The experimental oddball paradigm is supported by the software of the instrumentation. [Projekat Ministarstva nauke Republike Srbije, br. TR32019 and Provincial Secretariat for Science and Technological Development of Autonomous Province of Vojvodina (Republic of Serbia) under research grant No. 114-451-2723

Output-feedback shared-control for fully actuated linear mechanical systems

This paper presents an output feedback shared-control algorithm for fully-actuated, linear, mechanical systems. The feasible configurations of the system are described by a group of linear inequalities which characterize a convex admissible set. The properties of the shared-control algorithm are established with a Lyapunov-like analysis. Simple numerical examples demonstrate the effectiveness of the strategy

Интеллектуальное кресло-робот со вспомогательными средствами связи с использованием откликов TEP и характеристик диапазона спектра более высокого порядка

In recent years, electroencephalography-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order spectra based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i. e. Delta, Theta, Alpha, Beta, Gamma 1-1 and Gamma 2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the average value of bispectral magnitude and entropy using the bispectrum field are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71 % and the value of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.В последние годы все больше внимания уделяется навигационным и коммуникационным системам на основе электроэнцефалограммы головного мозга для сообществ с разными возможностями. Для предоставления навигационной системе вспомогательных средств связи в работе предложен настраиваемый протокол, использующий вызванные мыслительные потенциалы, чтобы помочь сообществам с разными возможностями. Представлены функции, основанные на спектрах более высокого порядка, для классификации семи основных задач, таких как Вперед, Влево, Вправо, Да, НЕТ, Помощь и Расслабление, которые можно использовать для управления креслом-роботом, а также для связи с использованием необычной парадигмы. Предлагаемая система записывает восьмиканальный беспроводной сигнал электроэнцефалографии от десяти субъектов, в то время как субъект воспринимал семь различных задач. Записанные сигналы мозговых волн предварительно обрабатываются для удаления интерференционных волн и сегментируются на сигналы шести частотных диапазонов: дельта, тета, альфа, бета, гамма 1-1 и гамма 2. Сигналы полосы частот сегментируются на выборки кадров равной длины и используются для извлечения признаков с использованием оценки биспектра. Кроме того, статистические характеристики, такие как среднее значение биспектральной величины и энтропия с использованием области биспектра, извлекаются и формируются как набор характеристик. Извлеченные наборы функций проходят десятикратную перекрестную проверку с использованием классификатора многослойной нейронной сети. Результаты показали, что энтропия модели классификатора на основе характеристик биспектральной величины имеет максимальную точность классификации 84,71 %, а среднее значение модели классификатора на основе характеристик биспектральной величины – минимальную точность классификации 68,52 %

Diseño del sistema de control de un brazo robótico de asistencia a personas discapacitadas

La presente tesis tiene por finalidad el diseño de un sistema para comandar un brazo robótico de asistencia que estará montado sobre una silla de ruedas automatizada, y cuya operación será por medio de señales EEG, con el objetivo de asistir a pacientes postrados con limitaciones de discapacidad muscular en miembros superiores, esclerosis lateral amiotrófica, lesión de la médula espinal, entre otros. El trabajo se enfoca en la implementación de un sistema basado en el procesamiento de señales cerebrales producto de estímulos visuales modulados a frecuencias específicas, con las cuales será posible clasificar y definir comandos de movimientos básicos sobre el brazo robótico. Todo ello con el objetivo de reducir fatigas mentales producto del uso de otras técnicas, como las cognitivas, que requieren mayor esfuerzo de concentración y muchas horas de entrenamiento previo para su correcto funcionamiento. Así mismo, la investigación muestra los criterios para la implementación del sistema de generación de estímulos visuales y resultados de los experimentos durante la adquisición, el procesamiento y clasificación de las señales recolectadas a partir de un dispositivo BCI portátil, con características limitadas en precisión y ancho de banda.Tesi

Error detection and new stimulus mechanisms in brain-computer interface

Brain Computer Interfaces (BCIs) constitute a research eld whose motivation is to help disabled individuals to communicate with the environment around them directly through the electrical activity of their brain rather than by the usual muscular output mechanism of the human body. The idea of non-invasive BCI is based on collecting brain signals using medical electrodes placed on the scalp of the patient and then trying to understand what the patient is trying to do/say by automatically analysing the collected signals. In other words, BCI can be imagined as a way to compensate the damaged internal nerves that used to carry signals from the brain, by using external cables connected with the computer. Although extensive research continues to be carried out in the eld of BCI, still BCI is working only inside laboratories. This is due to the weakness of the brain signals that are acquired. It is impossible to understand always the meaning of the signals without error. The existence of errors in such systems means that it is impossible to depend totally on them to control the life of disabled individuals. One of the well-known BCI types is called the P300 paradigm. It provides individuals with a method to choose any target only by concentrating on this target while it is ashing. The ash on the screen is considered as a stimulus for the brain, and the brain's response to this stimulus is known as the P300 signal and can be detected in the acquired signals from the brain. P300-BCI is one of the most well-known paradigms in the BCI eld. One way to reduce the number of errors in any BCI system in general, and in P300 paradigms in particular, may be by using Error-related Potentials (ErrP). These ErrP signals are generated when the subject detects an error in the system. Therefore, these signals could be used as a feedback for the BCI system to verify its last response. If the BCI system, for example, generates a wrong output, then an ErrP will be generated from the subject's brain which could be exploited to generate a message that the last output generated is not correct. Another way to reduce the number of errors, in the context of P300 paradigms, may be by making the neighbour non-target items have the same job of the target item. By using this idea, whether the subject gives attention to these non-target items or not, the output will be as the subject expects. In this research, we have experimentally examined two di erent scenarios for generating ErrP signals. Having ErrP signals from two di erent scenarios makes it possible for us to see if the ErrP signals have the same characteristics under di erent scenarios. In addition, we have implemented a new P300 paradigm motivated by a BCI-based robotic control application, in which the target's neighbour items have the same job of the target itself. In this new implementation, we get better classi cation performance through an analysis that compensates for the change in the number of classes

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

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