Brain–computer interfaces for human gait restoration

In this review article, we present more than a decade of our work on the development of brain–computer interface (BCI)systems for the restoration of walking following neurological injuries such as spinal cord injury (SCI) or stroke. Most ofthis work has been in the domain of non-invasive electroencephalogram-based BCIs, including interfacing our system witha virtual reality environment and physical prostheses. Real-time online tests are presented to demonstrate the ability ofable-bodied subjects as well as those with SCI to purposefully operate our BCI system. Extensions of this work are alsopresented and include the development of a portable low-cost BCI suitable for at-home use, our ongoing efforts to develop afully implantable BCI for the restoration of walking and leg sensation after SCI, and our novel BCI-based therapy for strokerehabilitation.     

Human–computer cooperation platform for developing real-time robotic applications

The Journal of Supercomputing - This paper presents a human–computer cooperation platform, which permits the coordination between the user and the tool to improve the development of real-time...     

Brain–computer interfaces and dualism: a problem of brain,mind, and body

The brain–computer interface (BCI) has made remarkable progress in the bridging the divide between the brain and the external environment to assist persons with severe disabilities caused by brain impairments. There is also continuing philosophical interest in BCIs which emerges from thoughtful reflection on computers, machines, and artificial intelligence. This article seeks to apply BCI perspectives to examine, challenge, and work towards a possible resolution to a persistent problem in the mind–body relationship, namely dualism. The original humanitarian goals of BCIs and the technological inventiveness result in BCIs being surprisingly useful. We begin from the neurologically impaired person, the problems encountered, and some pioneering responses from computers and machines. Secondly, the interface of mind and brain is explored via two points of clarification: direct and indirect BCIs, and the nature of thoughts. Thirdly, dualism is beset by mind–body interaction difficulties and is further questioned by the phenomena of intentions, interactions, and technology. Fourthly, animal minds and robots are explored in BCI settings again with relevance for dualism. After a brief look at other BCIs, we conclude by outlining a future BCI philosophy of brain and mind, which might appear ominous and could be possible.     

Classification of primitive shapes using brain–computer interfaces

Brain–computer interfaces (BCIs) are recent developments in alternative technologies of user interaction. The purpose of this paper is to explore the potential of BCIs as user interfaces for CAD systems. The paper describes experiments and algorithms that use the BCI to distinguish between primitive shapes that are imagined by a user. Users wear an electroencephalogram (EEG) headset and imagine the shape of a cube, sphere, cylinder, pyramid or a cone. The EEG headset collects brain activity from 14 locations on the scalp. The data is analyzed with independent component analysis (ICA) and the Hilbert–Huang Transform (HHT). The features of interest are the marginal spectra of different frequency bands (theta, alpha, beta and gamma bands) calculated from the Hilbert spectrum of each independent component. The Mann–Whitney U-test is then applied to rank the EEG electrode channels by relevance in five pair-wise classifications. The features from the highest ranking independent components form the final feature vector which is then used to train a linear discriminant classifier. Results show that this classifier can discriminate between the five basic primitive objects with an average accuracy of about 44.6% (compared to naïve classification rate of 20%) over ten subjects (accuracy range of 36%–54%). The accuracy classification changes to 39.9% when both visual and verbal cues are used. The repeatability of the feature extraction and classification was checked by conducting the experiment on 10 different days with the same participants. This shows that the BCI holds promise in creating geometric shapes in CAD systems and could be used as a novel means of user interaction.     

Multiple classifier system for EEG signal classification with application to brain–computer interfaces

In this paper, we demonstrate the use of a multiple classifier system for classification of electroencephalogram (EEG) signals. The main purpose of this paper is to apply several approaches to classify motor imageries originating from the brain in a more robust manner. For this study, dataset II from BCI competition III was used. To extract features from the brain signal, discrete wavelet transform decomposition was used. Then, several classic classifiers were implemented to be utilized in the multiple classifier system, which outperforms the reported results of other proposed methods on the dataset. Also, a variety of classifier combination methods along with genetic algorithm feature selection were evaluated and compared in order to diminish classification error. Our results suggest that an ensemble system can be employed to boost EEG classification accuracy.     

ERD analysis method in motor imagery brain–computer interfaces for accurate switch input

Motor imagery brain–computer interface (MIBCI) can control computers using MI. However, input accuracy is low, partly owing to individual variability in event-related desynchronization (ERD) detection among different subjects. In an earlier study, we determined that using a max power in the mu-band method, i.e., the peak trace method (PTM), is effective for ERD detection. In this study, we compare the PTM to the band power method to determine the most effective method for ERD detection during MI tasks. Experimental results indicate that we could detect ERD using the PTM; however, MI-state estimation was difficult. We also found that the PTM might be effective for ERD detection in subjects with MI experience.     

Rasiowa–Sikorski deduction systems in computer science applications

A Rasiowa-Sikorski system is a sequence-type formalization of logics. The system uses invertible decomposition rules which decompose a formula into sequences of simpler formulae whose validity is equivalent to validity of the original formula. There may also be expansion rules which close indecomposable sequences under certain properties of relations appearing in the formulae, like symmetry or transitivity. Proofs are finite decomposition trees with leaves having “fundamental”, valid labels. The author describes a general method of applying the R-S formalism to develop complete deduction systems for various brands of C.S and A.I. logic, including a logic for reasoning about relative similarity, a three-valued software specification logic with McCarthy's connectives and Kleene quantifiers, a logic for nondeterministic specifications, many-sorted FOL with possibly empty carriers of some sorts, and a three-valued logic for reasoning about concurrency.     

The development of a comprehensive computer program for the study of solid–solid atomic interfaces

This paper describes the construction of a comprehensive computer program to model the structure of the silicon–silicon dioxide (Si–SiO₂) boundary layer of semiconductor devices by combining experimental data from an X-ray storage ring with graphical simulations on a multiprocessor distributed net (or a multiprocessor supercomputer). The term ‘comprehensive’ refers to the program's planned ability to incorporate all stages necessary to translate 2D X-ray spectra to the final 3D atomic image with little or no human intervention. If successful, our efforts will provide materials scientists, engineers, and manufacturers of integrated circuits with an additional tool for understanding interfacial chemistry and thereby suggest new ways to improve the chemical and electronic behavior of their devices.     

Investigation of different approaches for noise reduction in functional near-infrared spectroscopy signals for brain–computer interface applications

Functional near-infrared spectroscopy (fNIRS) is a noninvasive technique to measure the hemodynamic response from the cerebral cortex. The acquired fNIRS signal usually contains influences generated from physiological processes, also called “global” oscillations, in addition to motion artifacts that impede detection of the localized hemodynamic response due to cortical activation. Preprocessing is the fundamental step to enhance the quality of fNIRS signals corresponding to movement tasks for efficient classification of brain–computer interface (BCI) application. Various signal preprocessing approaches such as band-pass filtering, correlation-based signal improvement, median filtering, Savitzky–Golay filtering, wavelet denoising and independent component analysis (ICA) have been investigated on experimental datasets acquired during hand movement tasks and are compared to one another using artifact power attenuation and contrast-to-noise ratio (CNR) metrics. The results showed that wavelet denoising method attenuated the artifact energy of the datasets belonging to Subjects 1 and 2 as well as enhanced the CNR. In the case of Subject 1, before denoising the values of ΔHbR and ΔHbO were 0.6392 and 0.8710, respectively. Wavelet method improved these values to 0.8085 and 0.9790. In the case of Subject 2, the CNR values of ΔHbR and ΔHbO signals were improved from 0.0221 and 0.0638 to 1.1242 and 0.3460, respectively. In this study, ICA was also demonstrated to suppress noises related to physiological oscillations including Mayer wave influence and other unknown artifacts. It greatly reduced the sharp spikes present in the Subject 2 dataset. On the basis of the results obtained, it can be shown that application of such filtering algorithms for fNIRS signal could effectively classify motor tasks to develop BCI applications.

     

Human–computer cooperation for future computing

The Journal of Supercomputing -     

Modeling of distributed parameter systems for applications—A synthesized review from time–space separation

Many industrial processes belong to distributed parameter systems (DPS) that have strong spatial–temporal dynamics. Modeling of DPS is difficult but essential to simulation, control and optimization. The first-principle modeling for known DPS often leads to the partial differential equation (PDE). Because it is an infinite-dimensional system, the model reduction (MR) is very necessary for real implementation. The model reduction often works with selection of basis functions (BF). Combination of different BF and MR results in different approaches. For unknown DPS, system identification is usually used to figure out unknown structure and parameters. Using various methods, different approaches are developed. Finally, a novel kernel-based approach is proposed for the complex DPS. This paper provides a brief review of different DPS modeling methods and categorizes them from the view of time–space separation.     

Brain–computer interfacing: more than the sum of its parts

The performance of non-invasive electroencephalogram-based (EEG) brain–computer interfaces (BCIs) has improved significantly in recent years. However, remaining challenges include the non-stationarity and the low signal-to-noise ratio of the EEG, which limit the bandwidth and hence the available applications. Optimization of both individual components of BCIs and the interrelationship between them is crucial to enhance bandwidth. In other words, neuroscientific knowledge and machine learning need to be optimized by considering concepts from human–computer interaction research and usability. In this paper, we present results of ongoing relevant research in our lab that addresses several important issues for BCIs based on the detection of transient changes in oscillatory EEG activity. First, we report on the long-term stability and robustness of detection of oscillatory EEG components modulated by distinct mental tasks, and show that the use of mental task pairs “mental subtraction versus motor imagery” achieves robust and reliable performance (Cohen’s κ > 0.6) in seven out of nine subjects over a period of 4 days. Second, we report on restricted Boltzmann machines (RBMs) as promising tools for the recognition of oscillatory EEG patterns. In an off-line BCI simulation we computed average peak accuracies, averaged over ten subjects, of 80.8 ± 7.2 %. Third, we present the basic framework of the context-aware hybrid Graz-BCI that allows interacting with the massive multiplayer online role playing game World of Warcraft. We show how a more integrated design approach that considers all components of BCIs, their interrelationships, other input signals and contextual information can increase interaction efficacy.     

A human–computer interaction approach for healthcare

Universal Access in the Information Society -     

Bibliography of computer applications in the Earth Sciences, 1948–1970

A bibliography of computer applications in the earth sciences from 1948 to 1970 is presented along with an index by method, geologic subject, geographic area, geologic age, programming language, and language.     

A Model–View–Controller extension for pervasive multi-client user interfaces

This paper addresses the implementation of pervasive Java Web applications using a development approach that is based on the Model–View–Controller (MVC) design pattern. We combine the MVC methodology with a hierarchical task-based state transition model in order to achieve the distinction between the task state and the view state of an application. More precisely, we propose to add a device-independent TaskStateBean and a device-specific ViewStateBean for each task state as an extension to the J2EE Service to Worker design pattern. Furthermore, we suggest representing the task state and view state transition models as finite state automata in two sets of XML files. This paper shows that the distinction between an applications task state and view state is both intuitive and facilitates several, otherwise complex, tasks, such as changing devices on the fly.     

Are low cost Brain Computer Interface headsets ready for motor imagery applications?

Low cost electroencephalography (EEG) headset devices for brain data capturing are fast becoming a key instrument on Brain Computer Interface (BCI) applications. In spite of being a controller device initially developed for gaming, the research community has adopted them as a key element to gather EEG data. However, there have been little discussion about their performance when being compared with professional and research EEG headsets.This paper provides an assessment of one of these devices, the Emotiv EPOC, on a motor imagery problem. As a benchmark, the data and results presented for the Data Set V of the BCI Competition III have been used, which were recorded with a professional Biosemi Active 2 EEG headset. From the perspective of a final working application, it is shown that the performance of this headset is comparable to that found in professional devices when using the same number of sensors and sensor positions for a three status motor imagery cognitive process. This finding implies an increase on the number of EEG headsets the researchers and manufacturers of BCI systems applied to motor imagery problems can integrate and a reduction of their cost.As part of this paper the Emotiv EPOC recorded raw and pre-processed datasets are published to allow further improvements and comparisons.     

An Tactile ERP-Based Brain–Computer Interface for Communication

A classical visual event relative potential (ERP) brain–computer interface (BCI) system relies on visual stimuli to choose commands. Users obtain most information about their surroundings visually as well. This large amount of information can aggravate visual burden and fatigue. In our study, we proposed a novel approach to evoke ERP with a tactile stimulus. To achieve this approach, we first designed a wireless stimulus module with vibrators to provide a tactile stimulus for the system. The vibrators were located on the subject’s arm to imitate the joint motion of a robotic arm. Then, the ERP feature and the parameters of classifiers were obtained through offline experimental data analysis. Based on the analysis, the suitable electrode channels, stimulus onset asynchrony (SOA), and filter upper limit were different for different subjects. According to those outcomes, a unique classifier was designed for each subject. Finally, 10 healthy BCI-naive subjects participated in online experiments to evaluate the performance of our tactile BCI system; they achieved an accuracy range from 78.67% to 100% with an average of 89.1% and an instantaneous transmission rate (ITR) range from 7.77 to 28.70 bits/min with an average of 14.77 bits/min. The accuracy of different subjects and SOAs remained relatively stable, the ITR fluctuated mainly due to the different SOAs, and we achieved balance between ITR and accuracy.