Brain-Computer Interface: Current and Emerging Rehabilitation Applications

Brain–computer interface (BCI) is an active domain which has attracted attention of the research community in recent years. It offers huge potential as a technology which can estimate the intention of a user by analysis of brain signals and establish a communication channel directly between a human brain and an external device. Electroencephalography (EEG) is the most popular signal acquisition technique due to its ease of use and simplicity. In EEG-based BCI systems, electrodes are placed on specific positions on the scalp of the subject to record electrical activity. The BCI system consists of sequential stages of signal acquisition, its preprocessing, feature extraction and feature classification. It is an active research area which has a focus on improving classification accuracy in motor imagery-based BCI systems. The first stage in a BCI system is to acquire EEG signals from different positions of the scalp of the human subject. The acquired brain signals are preprocessed to remove artifacts before these are fed to feature the extraction stage. In this paper, independent component analysis (ICA) technique is used to remove artifacts from acquired signals. Filter bank common spatial pattern (FBCSP) technique is then used for feature extraction and feature selection. A feature classification approach based on support vector machine (SVM) is proposed in this work and its performance is enhanced by optimizing its polynomial kernel parameters. Selection of kernel parameters is done by grid search method using the fivefold cross-validation procedure. The proposed approach is then executed on publicly available data set 2a of BCI Competition IV. Results show that the proposed approach offers higher classification accuracy and lower misclassification rate as compared to other methods executed on the same dataset, as reported in literature.

Monitoring and recording of electroencephalogram (EEG) is a non-invasive procedure that is increasingly used in neuromonitoring. Patient management is assessed and tailored based on the acquired brain signals that indicates the neurological state of the patient. Conventional EEG systems are wet systems. EEG recording in itself is not painful, but scalp preparation for the electrode attachment and washing afterwards may be uncomfortable. In this paper, a soft flexible gel-free dry electroencephalogram (EEG) electrodes and dry EEG head cap is developed and proposed. The dry EEG electrodes is a hybrid packaging rigid metal snap connectors, soft and flexible polymer material, and Silver-Silver Chloride (Ag- AgCl) coating. The sensor body/ structure does not use conductive polymer and there is no peeling off of the structure that could lead to concern in signal degradation. The dry EEG head cap is a fully-adjustable one-piece stretchable material integrated with reference and ground electrodes. Impedance reading of wet system is <5 KOhms, mainly due to the use of electrolytic/ conductive paste with such system. The proposed dry electrodes have impedance readings measured at <65 KOhms, which is lower compared to commercially available dry EEG electrodes that have impedance readings between 100 KOhms and 2000 KOhms. Despite the difference in impedance readings between the conventional wet system and the proposed dry electrodes, the captured brain signals / brain wave patterns are comparable. These brain signals are important especially when utilized for brain computer interfaces (BCI) that power and drive limb orthotic for rehabilitation following a stroke. The dry electrodes passed biocompatibility tests, specifically cytotoxicity test per ISO-10993-5 and primary skin irritation tests per ISO- 10993-10.

A brain-computer interface (BCI) is a system that enables control of devices or communication with other persons, only through cerebral activity, without using muscles. The main application for BCIs is assistive technology for disabled persons. Examples for devices that can be controlled by BCIs are artificial limbs, spelling devices, or environment control systems. BCI research has seen renewed interest in recent years, and it has been convincingly shown that communication via a BCI is in principle feasible. However, present day systems still have shortcomings that prevent their widespread application. In part, these shortcomings are caused by limitations in the functionality of the pattern recognition algorithms used for discriminating brain signals in BCIs. Moreover, BCIs are often tested exclusively with able-bodied persons instead of conducting tests with the target user group, namely disabled persons. The goal of this thesis is to extend the functionality of pattern recognition algorithms for BCI systems and to move towards systems that are helpful for disabled users. We discuss extensions of linear discriminant analysis (LDA), which is a simple but efficient method for pattern recognition. In particular, a framework from Bayesian machine learning, the so-called evidence framework, is applied to LDA. An algorithm is obtained that learns classifiers quickly, robustly, and fully automatically. An extension of this algorithm allows to automatically reduce the number of sensors needed for acquisition of brain signals. More specifically, the algorithm allows to perform electrode selection. The algorithm for electrode selection is based on a concept known as automatic relevance determination (ARD) in Bayesian machine learning. The last part of the algorithmic development in this thesis concerns methods for computing accurate estimates of class probabilities in LDA-like classifiers. These probabilities are used to build a BCI that dynamically adapts the amount of acquired data, so that a preset, approximate bound on the probability of misclassifications is not exceeded. To test the algorithms described in this thesis, a BCI specifically tailored for disabled persons is introduced. The system uses electroencephalogram (EEG) signals and is based on the P300 evoked potential. Datasets recorded from five disabled and four able-bodied subjects are used to show that the Bayesian version of LDA outperforms plain LDA in terms of classification accuracy. Also, the impact of different static electrode configurations on classification accuracy is tested. In addition, experiments with the same datasets demonstrate that the algorithm for electrode selection is computationally efficient, yields physiologically plausible results, and improves classification accuracy over static electrode configurations. The classification accuracy is further improved by dynamically adapting the amount of acquired data. Besides the datasets recorded from disabled and able-bodied subjects, benchmark datasets from BCI competitions are used to show that the algorithms discussed in this thesis are competitive with state-of-the-art electroencephalogram (EEG) classification algorithms. While the experiments in this thesis are uniquely performed with P300 datasets, the presented algorithms might also be useful for other types of BCI systems based on the EEG. This is the case because functionalities such as robust and automatic computation of classifiers, electrode selection, and estimation of class probabilities are useful in many BCI systems. Seen from a more general point of view, many applications that rely on the classification of cerebral activity could possibly benefit from the methods developed in this thesis. Among the potential applications are interrogative polygraphy ("lie detection") and clinical applications, for example coma outcome prognosis and depth of anesthesia monitoring.

Enhancing the Social Impact of Contemporary Music with Neurotechnology

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that causes progressive loss of voluntary movement, including the ability to speak. As the disease rapidly progresses, there is a need for augmented and alternative communication that removes physical barriers required for engagement. Brain-Computer Interfaces (BCI) are a technology that allow for communication using only brain signals in response to visual stimuli. This project aimed to design an accessible and practical BCI for continuous, at-home use, and to increase BCI usability by using familiar facial stimuli to elicit a stronger brain response. The overall goal for enhancement of BCI usability is to maximize ease of use for individuals with ALS and their caregivers so that BCI technologies can make a useful impact and improve the everyday lives of people with ALS. In this project, a new miniaturized and low-cost P300 BCI system was designed using wireless and battery-operated EEG neuroimaging, modular software, and the use of an individualized BCI presentation stimuli. A 3-dimensional virtual environment platform was integrated as a navigational control output mechanism that executes commands received from the BCI to move a user's avatar in a virtual maze. Together, these form the virtual environment BCI (veBCI). The BCI stimuli presentation consisted of a custom approach of integrating different human face images with the aim of increasing user engagement and elicited brain activity, and therefore improving BCI online accuracy. The impact and effectiveness of the approach was tested via a verification BCI study in which 6 different stimuli conditions were utilized: male celebrity face, female celebrity face, male stranger face, female stranger face, a family member/close friend of the participant, and blank/no face as a control. A total of 16 volunteers (average age 56.5 and eight diagnosed with ALS) used the BCI system with all 6 face conditions in ecologically valid settings. Results indicate that this new miniaturized BCI is usable for both people with ALS and healthy controls. Significant differences existed in the amplitude of the P300 response in ALS and healthy controls. Additionally, differences between male and female participants demonstrated the potential role of personalization to optimize the visually evoked potential response and further increase BCI online accuracy. In conclusion, a home-use P300 BCI system was developed with a new generation of miniaturized, low-cost, and mobile neuroimaging running on an embedded computational platform. BCI stimuli with human face inclusion could help personalization of the BCI. Furthermore, virtual environments provide unique new ways to implement BCI output beyond typical spelling tasks and communication. Future work could focus on developing dedicated online signal processing to eliminate noise and improve online accuracy during home use.

ABSTRACT&#13;\n&#13;\nPutri, Anita Kurnia. 2020. “Analisis Kontrastif Adverbia Tinkatan Penanda Negasi dalam Bahasa Jepang dan Bahasa Jepang”. Thesis, department of Japanese Language and Culture, Faculty of Humanities, Diponegoro University. The Advisor: Lina Rosliana, S.S., M.Hum.&#13;\n&#13;\nThe problems that will be discussed in this thesis include: 1. What is the structure and meaning of the adverb in the level of negation markers in Japanese sentences? 2. What is the structure and meaning of the adverb at the level of the negation marker in Indonesian sentences? 3. How are the adverbs of the level of negation markers similar and different in Japanese and Indonesian sentences?&#13;\n&#13;\nMeanwhile, the purpose of this study is to describe the structure and meaning of adverbs in Japanese and Indonesian sentences. After knowing the structure and meaning, we will examine the similarities and differences in the structure and meaning of the adverbial levels of the negation markers of the two languages.&#13;\n&#13;\nIn analyzing the data, this study used the agih method followed by contrastive techniques. The data were obtained from the Japanese language website and news articles in Indonesian. Then the data is presented in words or descriptive.&#13;\n&#13;\nBased on the research, the structural similarities are both followed by verbs, adjectives. While the difference, adverbs in Japanese can be followed by phrases or clauses and negative forms, Indonesian adverbial followed by nouns. In meaning, the adverbs of the level of negation markers in Japanese and Indonesian indicates the meaning of the situation, the action is under adequate. And in terms of meaning it has 7 differences.&#13;\n&#13;\nKeywords : contrastive, adverbs, teido no fukushi&#13;\n

Brain-Computer Interface (BCI) systems allow user to operate devices without muscular activation and experimental results indicate that they can induce activity-dependent plasticity. A BCI system has two key features: exploitation of brain signal changes induced by the cognitive task assigned to the user and mutual learning. With respect to the first feature, BCI users can learn to control and hence change their cortical activity, for example by performing a motor imagery task. Mutual learning, on the other hand, is set up between users, which have to learn how to control their brain activity, and the classifier used for learning and categorize brain signal changes. This mutual learning can be mediated by the use of feedback, which in turn can be discrete or continuous, abstract or realistic and so on. Motor imagery represents a promising approach for stroke rehabilitation, thus a BCI based on a motor imagery paradigm could be a useful tool. Nevertheless, in stroke rehabilitation settings two issues are emerging which are also crucial for motor imagery BCIs. The first one relates to the importance of recognize the imagery ability of the subject, the second one to the importance of feedback in re-learning of motor skills. The present study relates to this second issue and it is aimed at investigating attitudes of BCI users towards feedback. We considered a sample of six healthy males, using an EEG-based BCI and executing a Motor Imagery task without feedback (training phase) and with feedback (performance phase). A horizontal bar of varying length depicted on a computer screen was used as feedback medium and overall accuracy data were matched with neurophysiological data acquired from P z and O z . Results could be of importance for BCI use as neurorehabilitation tool in stroke rehabilitation protocols since they suggest that some subjects could need an increased cortical activation in order to deal with a stimulus like visual feedback. Moreover, they reveal a possible association between worsening in performance and increased cortical activation needed in order to cope with feedback.

Decoding brain activity for brain computer interfaces (BCI) is a challenging machine learning task. Many BCIs are designed in a synchronous setting, which allows to output a command from the brain to the computer at pre-determined, specific time points. The task becomes substantially more demanding when designing asynchronous BCIs, which allow control over the interface at any given time. Most BCIs use static classifiers, in which the readout is driven only by the current input. However, due to the complex nature of ongoing brain activity, dynamic classifiers, which capture the temporal dynamics of the input signal, are likely to be more suitable for the task. To examine this notion, we designed a classification framework based on Echo State Networks (ESNs), which have demonstrated strong performance in similar tasks in other domains. We evaluated the performance of this approach on pre-recorded EEG data of an asynchronous motor imagery (MI) BCI task, and compared our approach to a conventional method. ESN-based architectures exhibited superior performance in comparison. Our findings suggest that ESNs may prove useful in other BCI paradigms in virtue of their ability to reduce the detrimental effect of non-stationarity in brain signals.

Brain Computer Interfacing (BCI) allows user to control certain devices or objects using human brain. With the availability of various sensors, it is possible to obtain the data from the human brain which controls the physical activities in the brain itself. BCI users can access a system capable of communicating efficiently in a special manner that works based on the brain signals. BCI gives a better platform for physically challenged people to do certain significant functions in their daily routine. The use of brain imaging technologies in BCI helps to enhance the signal quality during the communication among the machines and the human beings. The paper describes some of the research works carried on BCI and its advancements to perform a better communication between the computers and the human brain, by using emerging technologies. This paper also covers the applications, challenges, and future directions of BCI.

The possibility of an effective connection between neural tissue and computers has inspired scientists and engineers to develop new ways of controlling and obtaining information from the nervous system. These applications range from `brain hacking' to neural control of artificial limbs with brain signals. Notwithstanding the significant advances in neural prosthetics in the last few decades and the success of some stimulation devices such as cochlear prosthesis, neurotechnology remains below its potential for restoring neural function in patients with nervous system disorders. One of the reasons for this limited impact can be found at the neural interface and close attention to the integration between electrodes and tissue should improve the possibility of successful outcomes.The neural interfaces research community consists of investigators working in areas such as deep brain stimulation, functional neuromuscular/electrical stimulation, auditory prostheses, cortical prostheses, neuromodulation, microelectrode array technology, brain–computer/machine interfaces. Following the success of previous neuroprostheses and neural interfaces workshops, funding (from NIH) was obtained to establish a biennial conference in the area of neural interfaces. The first Neural Interfaces Conference took place in Cleveland, OH in 2008 and several topics from this conference have been selected for publication in this special section of the Journal of Neural Engineering. Three `perspectives' review the areas of neural regeneration (Corredor and Goldberg), cochlear implants (O'Leary et al) and neural prostheses (Anderson). Seven articles focus on various aspects of neural interfacing. One of the most popular of these areas is the field of brain–computer interfaces. Fraser et al, report on a method to generate robust control with simple signal processing algorithms of signals obtained with electrodes implanted in the brain. One problem with implanted electrode arrays, however, is that they can fail to record reliably neural signals for long periods of time. McConnell et al show that by measuring the impedance of the tissue, one can evaluate the extent of the tissue response to the presence of the electrode. Another problem with the neural interface is the mismatch of the mechanical properties between electrode and tissue. Basinger et al use finite element modeling to analyze this mismatch in retinal prostheses and guide the design of new implantable devices. Electrical stimulation has been the method of choice to activate externally the nervous system. However, Zhang et al show that a novel dual hybrid device integrating electrical and optical stimulation can provide an effective interface for simultaneous recording and stimulation. By interfacing an EMG recording system and a movement detection system, Johnson and Fuglevand develop a model capable of predicting muscle activity during movement that could be important for the development of motor prostheses. Sensory restoration is another unsolved problem in neural prostheses. By developing a novel interface between the dorsal root ganglia and electrodes arrays, Gaunt et al show that it is possible to recruit afferent fibers for sensory substitution. Finally, by interfacing directly with muscles, Jung and colleagues show that stimulation of muscles involved in locomotion following spinal cord damage in rats can provide an effective treatment modality for incomplete spinal cord injury.This series of articles clearly shows that the interface is indeed one of the keys to successful therapeutic neural devices. The next Neural Interfaces Conference will take place in Los Angeles, CA in June 2010 and one can expect to see new developments in neural engineering obtained by focusing on the neural interface.

Brain-computer interfaces (BCIs) enable control of computers and other assistive devices, such as neuro-prostheses, which are used for communication, movement restoration, neuro-modulation, and muscle stimulation, by using only signals measured directly from the brain. A BCI creates a new output channel for the brain to a computer or a device. This requires retrieval of signals of interest from the brain, and its use for neuro-rehabilitation by means of interfacing the signals to a computerized device. Brain signals such as action potentials from single neurons or nerve fibers, extracellular local field potentials (LFPs), electrocorticograms, electroencephalogram and its components such as the event-related brain potentials, real-time functional magnetic resonance imaging, near-infrared spectroscopy, and magneto-encephalogram have been used. BCIs are envisaged to be useful for communication, control and self-regulation of brain function. BCIs employ neurofeedback to enable operant conditioning to allow the user to learn using it. Paralytic conditions arising from stroke or other diseases are being targeted for BCI application. Neurofeedback strategies ranging from sensory feedback to direct brain stimulation are being employed. Existing BCIs are limited in their throughput in terms of letters per minute or commands per minute, and need extensive training to use the BCI. Further, they can cause rapid fatigue due to use and have limited adaptability to changes in the patient's brain state. The challenge before BCI technology for neuro-rehabilitation today is to enable effective clinical use of BCIs with minimal effort to set up and operate.

A visual Brain-Computer Interface (BCI) speller is a system which assists disabled persons with severe neu-romuscular diseases to communicate with the external world. It acquires brain signals in response to visual stimuli shown to the person on a screen, and then analyzes in real-time to predict the desired symbol on a single trial basis. To date most BCI design paradigms have been focused on the development of a speller to communicate English or Latin based languages. Due to a lack of BCI spellers for patients speaking Cyrillic-based languages, this study presents the initial design and evaluation of a speller that contains Cyrillic alphanumeric characters. The visual BCI speller was evaluated on five healthy subjects, who showed encouraging results during both the offline training phases, as well as during the real-time BCI spelling experiments. We discuss each steps of the design in detail and share the challenges and limitation of such design approaches with possible solutions.

Event Abstract Back to Event Analysis and classification of EEG signals for brain computer interfaces Ugur Halici1*, E. Agi1, C. Özgen1 and I. Ulusoy1 1 Computer Vision and Intelligent Systems Research Laboratory, Middle East Technical University, Turkey Brain Computer Interfaces (BCIs) are systems developed in order to control devices by using only brain signals. Depending on the application, the device to be controlled might be a neuroprothesis, a wheel-chair or a computer. In BCI systems, different mental activities to be performed by the users are associated with different actions on the device to be controlled. The neurophysiologic brain signals produced as results of these mental activities are collected by a data acquisition device and converted to digital form. Electroencephalogram (EEG) is the most common technique employed for data acquisition in BCI systems today, due to its time-space resolution, its price and its easiness in use when compared to the other acquisition techniques. The digital brain signals collected are processed for feature extraction and then classified in order to decide on the associated action. A control signal produced according to the decision is used to change the state of the device to be controlled. Providing a feedback signal informing the user about the new state of the controlled device completes the BCI cycle. The early BCI systems were designed to work on pre-defined neurophysiologic features, where a feedback produced by a fixed set-up was provided to the system users. In these early BCI systems, the users should learn by themselves how to produce neurophysiologic signals in order to control the system. In several studies, it is reported that weeks, even months may be needed in order the user to learn how to produce these signals by adaptation of their brain. Recently, BCI systems in which machine learning algorithms employed for adapting the BCI system by using user specific signals are started to emerge. In several studies, it is shown that the adaptation of the BCI systems in this way reduces drastically the time required the user to able to produce appropriate signals and adaptation time in terms of days, even minutes are reported [1-4] The aim of this study is to analyze EEG signals collected for BCI purposes by using techniques Principal Component Analysis, Fisher Linear Discriminant Analysis, and Independent Component Analysis in order to reduce the dimension of the feature vectors obtained by sampling EEG signals in an representative manner and then classify the low dimension feature vectors obtained in this way by using Neural Networks and Support Vector Machines, which are able to learn through samples. The experimental studies are still going on for feature extraction and classification. The experimental results obtained will be presented in the final paper for comparison purposes.

EMG and EOG artifacts in brain computer interface systems: A survey

The modern commercial industry is increasingly dependent on the analysis of vast data records. Analysing big electroencephalogram (EEG) signal data (called brain signal data) plays an important role in a wide variety of applications such as healthcare practices, brain computer interface (BCI) systems, innovative education, privacy and security, and biometrics. The key objective of this paper is to establish a methodological framework for identifying communicative intentions of motor disabled people from EEG data for application in BCI systems. The proposed framework is designed based on common spatial pattern (CSP) data method and optimized ensemble (OE) machine learning method for the application of BCI technologies. The CSP method is used for discovering important features from EEG data and finally the extracted features are fed as an input to optimized ensemble (OE) method. The proposed method was tested on BCI Competition III dataset IVa, which contains motor imagery-based EEG signal data. The experimental results show that our proposed method can handle brain signal big data for identifying communicative intentions for an advanced BCI system. We compared the performance of our proposed method with several other existing methods. In comparison with other established methods, our method achieves higher classification accuracy performance. This research assists the experts in processing and analysing EEG signals for BCI applications. It also supports technologists to create a new EEG data analyser for BCI systems.KeywordsElectroencephalographyBrain computer interfaceCommon spatial patternMachine learning