Facilitating effects of transcranial direct current stimulation on motor imagery brain-computer interface with robotic feedback for stroke rehabilitation

OBJECTIVE: To investigate the efficacy and effects of transcranial direct current stimulation (tDCS) on motor imagery brain-computer interface (MI-BCI) with robotic feedback for stroke rehabilitation. DESIGN: A sham-controlled, randomized controlled trial. SETTING: Patients recruited through a hospital stroke rehabilitation program. PARTICIPANTS: Subjects (N=19) who incurred a stroke 0.8 to 4.3 years prior, with moderate to severe upper extremity functional impairment, and passed BCI screening. INTERVENTIONS: Ten sessions of 20 minutes of tDCS or sham before 1 hour of MI-BCI with robotic feedback upper limb stroke rehabilitation for 2 weeks. Each rehabilitation session comprised 8 minutes of evaluation and 1 hour of therapy. MAIN OUTCOME MEASURES: Upper extremity Fugl-Meyer Motor Assessment (FMMA) scores measured end-intervention at week 2 and follow-up at week 4, online BCI accuracies from the evaluation part, and laterality coefficients of the electroencephalogram (EEG) from the therapy part of the 10 rehabilitation sessions. RESULTS: FMMA score improved in both groups at week 4, but no intergroup differences were found at any time points. Online accuracies of the evaluation part from the tDCS group were significantly higher than those from the sham group. The EEG laterality coefficients from the therapy part of the tDCS group were significantly higher than those of the sham group. CONCLUSIONS: The results suggest a role for tDCS in facilitating motor imagery in stroke.

Prosthetic motor imaginary task classification using single channel of electroencephalography

Brain Computer Interface (BCI) is playing a very important role in human machine communications. Recent communication systems depend on the brain signals for communication. In these systems, users clearly manipulate their brain activity rather than using motor movements in order to generate signals that could be used to give commands and control any communication devices, robots or computers. In this paper, the aim was to estimate the performance of a brain computer interface (BCI) system by detecting the prosthetic motor imaginary tasks by using only a single channel of electroencephalography (EEG). The participant is asked to imagine moving his arm up or down and our system detects the movement based on the participant brain signal. Some features are extracted from the brain signal using Mel-Frequency Cepstrum Coefficient and based on these feature a Hidden Markov model is used to help in knowing if the participant imagined moving up or down. The major advantage in our method is that only one channel is needed to take the decision. Moreover, the method is online which means that it can give the decision as soon as the signal is given to the system. Hundred signals were used for testing, on average 89 % of the up down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial prosthetic limbs and wheelchairs due to it's high speed and accuracy.

Investigation of the effects of microcomputers on the work of professional accountants

Information technology research over the past two decades suggests that the installation and use of computers fundamentally affects the structure and function of organisations and, m particular, the workers in these organizations. Following the release of the IBM Personal Computer in 1982, microcomputers have become an integral part of most work environments. The accounting services industry, in particular, has felt the impact of this ‘microcomputer revolution’. In Big Six accounting firms, there is almost one microcomputer for each professional accountant employed, Notwithstanding this, little research has been done on the effect of microcomputers on the work outcomes of professional accountants working in these firms. This study addresses this issue. It assesses, in an organisational setting, how accountant’ perceptions of ease of use and usefulness of microcomputers act on their computer anxieties, microcomputer attitudes and use to affect their job satisfaction and job performance. The research also examines how different types of human-computer interfaces affect the relationships between accountants' beliefs about microcomputer utility and ease of use, computer anxiety, microcomputer attitudes and microcomputer use. To attain this research objective, a conceptual model was first developed, The model indicates that work outcomes (job satisfaction and job performance) of professional accountants using microcomputers are influenced by users' perceptions of ease of use and usefulness of microcomputers via paths through (a) the level of computer anxiety experienced by users, (b) the general attitude of users toward using microcomputers, and (c) the extent to which microcomputers are used by individuals. Empirically testable propositions were derived from the model to test the postulated relationships between these constructs. The study also tested whether or not users of different human-computer interfaces reacted differently to the perceptions and anxieties they hold about microcomputers and their use in the workplace. It was argued that users of graphical interfaces, because of the characteristics of those interfaces, react differently to their perceptions and anxieties about microcomputers compared with users of command-line (or textual-based) interfaces. A passive-observational study in a field setting was used to test the model and the research propositions. Data was collected from 164 professional accountants working in a Big Six accounting firm in a metropolitan city in Australia. Structural equation modelling techniques were used to test the, hypothesised causal relationships between the components comprising the general research model. Path analysis and ordinary least squares regression was used to estimate the parameters of the model and analyse the data obtained. Multisample analysis (or stacked model analysis) using EQS was used to test the fit of the model to the data of the different human-computer interface groups and to estimate the parameters for the paths in those different groups. The results show that the research model is a good description of the data. The job satisfaction of professional accountants is directly affected by their attitude toward using microcomputers and by microcomputer use itself. However, job performance appears to be only directly affected by microcomputer attitudes. Microcomputer use does not directly affect job performance. Along with perceived ease of use and perceived usefulness, computer anxiety is shown to be an important determinant of attitudes toward using microcomputers - higher levels of computer anxiety negatively affect attitudes toward using microcomputers. Conversely, higher levels of perceived ease of use and perceived usefulness heighten individuals' positive attitudes toward using microcomputers. Perceived ease of use and perceived usefulness also indirectly affect microcomputer attitudes through their effect on computer anxiety. The results show that higher levels of perceived ease of use and perceived usefulness result in lower levels of computer anxiety. A surprising result from the study is that while perceived ease of use is shown to directly affect the level of microcomputer usage, perceived usefulness and attitude toward using microcomputers does not. The results of the multisample analysis confirm that the research model fits the stacked model and that the stacked model is a significantly better fit if specific parameters are allowed to vary between the two human-computer interface user groups. In general, these results confirm that an interaction exists between the type of human-computer interface (the variable providing the grouping) and the other variables in the model The results show a clear difference between the two groups in the way in which perceived ease of use and perceived usefulness affect microcomputer attitude. In the case of users of command-line interfaces, these variables appear to affect microcomputer attitude via an intervening variable, computer anxiety, whereas in the graphical interface user group the effect occurs directly. Related to this, the results show that perceived ease of use and perceived usefulness have a significant direct effect on computer anxiety in command-line interface users, but no effect at all for graphical interface users. Of the two exogenous variables only perceived ease of use, and that in the case of the command-line interface users, has a direct significant effect on extent of use of microcomputers. In summary, the research has contributed to the development of a theory of individual adjustment to information technology in the workplace. It identifies certain perceptions, anxieties and attitudes about microcomputers and shows how they may affect work outcomes such as job satisfaction and job performance. It also shows that microcomputer-interface types have a differential effect on some of the hypothesised relationships represented in the general model. Future replication studies could sample a broader cross-section of the microcomputer user community. Finally, the results should help Big Six accounting firms to maximise the benefits of microcomputer use by making them aware of how working with microcomputers affects job satisfaction and job performance.

Touching space : using motion capture and stereo projection to create a virtual haptics of dance

This paper describes the work of a group of artists in Australia who used real-time motion capture and 3D stereo projection to create a large-scale performance environment in which dancers seemed to "touch" the volume. This project re-versions Suzanne Langer's 1950s philosophy of dance as "virtual force" to realize the idea of a "virtual haptics" of dance that extends the dancer's physical agency literally across and through the surrounding spatial volume. The project presents a vision of interactive dance performance that "touches" space by visualizing kinematics as intentionality and agency. In doing so, we suggest the possibility of new kinds of human-computer interfaces that emphasize touch as embodied, nuanced agency that is mediated by the subtle qualities of whole-body movement, in addition to more goal-oriented, task-based gestures such as pointing or clicking.

Motor imagery data classification for BCI application using wavelet packet feature extraction

The noninvasive brain imaging modalities have provided us an extraordinary means for monitoring the working brain. Among these modalities, Electroencephalography (EEG) is the most widely used technique for measuring the brain signals under different tasks, due to its mobility, low cost, and high temporal resolution. In this paper we investigate the use of EEG signals in brain-computer interface (BCI) systems.

EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems

The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II, are employed for the experiments. Classification performance is evaluated using accuracy, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis.

Fuzzy system with tabu search learning for classification of motor imagery data

This paper introduces an approach to classify EEG signals using wavelet transform and a fuzzy standard additive model (FSAM) with tabu search learning mechanism. Wavelet coefficients are ranked based on statistics of the Wilcoxon test. The most informative coefficients are assembled to form a feature set that serves as inputs to the tabu-FSAM. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed tabu-FSAM method considerably dominates the competitive classifiers, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II.

Touching space: Using motion capture and stereo projection to create a "virtual haptics" of dance

This paper describes the work of a group of artists in Australia who used real-time motion capture and 3D stereo projection to create a large-scale performance environment in which dancers seemed to "touch" the volume. This project re-versions Suzanne Langer's 1950s philosophy of dance as "virtual force" to realize the idea of a "virtual haptics" of dance that extends the dancer's physical agency literally across and through the surrounding spatial volume. The project presents a vision of interactive dance performance that "touches" space by visualizing kinematics as intentionality and agency. In doing so, we suggest the possibility of new kinds of human-computer interfaces that emphasize touch as embodied, nuanced agency that is mediated by the subtle qualities of whole-body movement, in addition to more goal-oriented, task-based gestures such as pointing or clicking. © 2010.

Biomedical signal filtering for noisy environments

 Luke's work addresses issue of robustly attenuating multi-source noise from surface EEG signals using a novel Adaptive-Multiple-Reference Least-Means-Squares filter (AMR-LMS). In practice, the filter successfully removes electrical interference and muscle noise generated during movement which contaminates EEG, allowing subjects to maintain maximum mobility throughout signal acquisition and during the use of a Brain Computer Interface.

Prosthetic motor imaginary task classification based on EEG quality assessment features

Brain Computer Interface (BCI) plays an important role in the communication between human and machines. This communication is based on the human brain signals. In these systems, users use their brain instead of the limbs or body movements to do tasks. The brain signals are analyzed and translated into commands to control any communication devices, robots or computers. In this paper, the aim was to enhance the performance of a brain computer interface (BCI) systems through better prosthetic motor imaginary tasks classification. The challenging part is to use only a single channel of electroencephalography (EEG). Arm movement imagination is the task of the user, where (s)he was asked to imagine moving his arm up or down. Our system detected the imagination based on the input brain signal. Some EEG quality features were extracted from the brain signal, and the Decision Tree was used to classify the participant's imagination based on the extracted features. Our system is online which means that it can give the decision as soon as the signal is given to the system (takes only 20 ms). Also, only one EEG channel is used for classification which reduces the complexity of the system which leads to fast performance. Hundred signals were used for testing, on average 97.4% of the up-down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial limbs and wheelchairs due to it's high speed and accuracy.

EEG signal analysis for BCI application using fuzzy system

An approach to EEG signal classification for brain-computer interface (BCI) application using fuzzy standard additive model is introduced in this paper. The Wilcoxon test is employed to rank wavelet coefficients. Top ranking wavelets are used to form a feature set that serves as inputs to the fuzzy classifiers. Experiments are carried out using two benchmark datasets, Ia and Ib, downloaded from the BCI competition II. Prevalent classifiers including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system are also implemented for comparisons. Experimental results show the dominance of the proposed method against competing approaches.

Multivariate adaptive autoregressive modeling and kalman filtering for motor imagery BCI

Adaptive autoregressive (AAR) modeling of the EEG time series and the AAR parameters has been widely used in Brain computer interface (BCI) systems as input features for the classification stage. Multivariate adaptive autoregressive modeling (MVAAR) also has been used in literature. This paper revisits the use of MVAAR models and propose the use of adaptive Kalman filter (AKF) for estimating the MVAAR parameters as features in a motor imagery BCI application. The AKF approach is compared to the alternative short time moving window (STMW) MVAAR parameter estimation approach. Though the two MVAAR methods show a nearly equal classification accuracy, the AKF possess the advantage of higher estimation update rates making it easily adoptable for on-line BCI systems.

‘Computer games can get your brain working’: student experience and perceptions of digital games in the classroom

There is considerable enthusiasm in many quarters for the incorporation of digital games into the classroom, and the capacity of games to engage and challenge players, present complex representations and experiences, foster collaborative learning, and promote deep learning. But while there is increasing research documenting the progress and outcomes of game-based learning, relatively little attention is paid to student perceptions and voice. In order to effectively target game-based learning pedagogy, it is important to understand students' previous experience, if any, of the use of games in the classroom, and what they made of these. In this paper, we present findings from a survey of 270 primary and secondary school students in Year Levels 4–9 (aged 9–14) in 6 Queensland schools at the start of a 3-year Australian Research Council project researching the use of digital games in school to promote literacy and learning.