Current Issue : July - September Volume : 2013 Issue Number : 3 Articles : 6 Articles
Mental task onset detection from the continuous electroencephalogram (EEG) in real time is a critical issue in self-paced brain\r\ncomputer interface (BCI) design. The paper shows that self-paced BCI performance can be significantly improved by combining\r\na range of simple techniques including (1) constant-Q filters with varying bandwidth size depending on the center frequency,\r\ninstead of constant bandwidth filters for frequency decomposition of the EEG signal in the 6 to 36Hz band; (2) subjectspecific\r\npostprocessing parameter optimization consisting of dwell time and threshold, and (3) debiasing before postprocessing by\r\nreadjusting the classification output based on the current and previous brain states, to reduce the number of false detections. This\r\ndebiasing block is shown to be optimal when activated only in special cases which are predetermined during the training phase.\r\nAnalysis of the data recorded from seven subjects executing foot movement shows a statistically significant 10% (P < 0.05) average\r\nimprovement in true positive rate (TPR) and a 1% reduction in false positive rate (FPR) detections compared with previous work\r\non the same data....
Increasing number of research activities and different types of studies in brain-computer interface (BCI) systems show potential\r\nin this young research area. Research teams have studied features of different data acquisition techniques, brain activity patterns,\r\nfeature extraction techniques, methods of classifications, and many other aspects of a BCI system. However, conventional BCIs\r\nhave not become totally applicable, due to the lack of high accuracy, reliability, low information transfer rate, and user acceptability.\r\nA new approach to create a more reliable BCI that takes advantage of each system is to combine two or more BCI systems with\r\ndifferent brain activity patterns or different input signal sources. This type of BCI, called hybrid BCI, may reduce disadvantages\r\nof each conventional BCI system. In addition, hybrid BCIs may create more applications and possibly increase the accuracy and\r\nthe information transfer rate. However, the type of BCIs and their combinations should be considered carefully. In this paper, after\r\nintroducing several types of BCIs and their combinations, we review and discuss hybrid BCIs, different possibilities to combine\r\nthem, and their advantages and disadvantages....
There is a consensus that serious games have a significant potential as a tool for instruction. However, their effectiveness in terms\r\nof learning outcomes is still understudied mainly due to the complexity involved in assessing intangible measures. A systematic\r\napproachââ?¬â?based on established principles and guidelinesââ?¬â?is necessary to enhance the design of serious games, and many studies\r\nlack a rigorous assessment. An important aspect in the evaluation of serious games, like other educational tools, is user performance\r\nassessment. This is an important area of exploration because serious games are intended to evaluate the learning progress as well\r\nas the outcomes. This also emphasizes the importance of providing appropriate feedback to the player. Moreover, performance\r\nassessment enables adaptivity and personalization to meet individual needs in various aspects, such as learning styles, information\r\nprovision rates, feedback, and so forth. This paper first reviews related literature regarding the educational effectiveness of serious\r\ngames. It then discusses how to assess the learning impact of serious games and methods for competence and skill assessment.\r\nFinally, it suggests two major directions for future research: characterization of the playerââ?¬â?¢s activity and better integration of\r\nassessment in games....
We present a brain-computer interface (BCI) version of the famous ââ?¬Å?Connect Fourââ?¬Â. Target selection is based on brain event-related\r\nresponses measured with nine EEG sensors. Two players compete against each other using their brain activity only. Importantly,\r\nwe turned the general difficulty of producing a reliable BCI command into an advantage, by extending the game play and rules, in\r\na way that adds fun to the game and might well prove to trigger up motivation in future studies. The principle of this new BCI is\r\ndirectly inspired from our own implementation of the classical P300 Speller (Maby et al. 2010, Perrin et al. 2011).We here establish\r\na proof of principle that the same electrophysiological markers can be used to design an efficient two-player game. Experimental\r\nevaluation on two competing healthy subjects yielded an average accuracy of 82%, which is in line with our previous results on\r\nmany participants and demonstrates that the BCI ââ?¬Å?Connect Fourââ?¬Â can effectively be controlled. Interestingly, the duration of the\r\ngame is not significantly affected by the usual slowness of BCI commands. This suggests that this kind of BCI games could be of\r\ninterest to healthy players as well as to disabled people who cannot play with classical games....
Power assist systems are usually used for rehabilitation, healthcare, and so forth.This paper puts emphasis on the use of power\r\nassist systems for object transfer and thus brings a novelty in the power-assist applications. However, the interactions between\r\nthe systems and the human users are usually not satisfactory because human features are not included in the control design. In\r\nthis paper, we present the development of a 1-DOF power assist system for horizontal transfer of objects. We included human\r\nfeatures such as weight perception in the system dynamics and control. We then simulated the system using MATLAB/Simulink\r\nfor transferring objects with it and (i) determined the optimum maneuverability conditions for object transfer, (ii) determined\r\npsychophysical relationships between actual and perceived weights, and (iii) analyzed load forces and motion features. We then\r\nused the findings to design a novel adaptive control scheme to improve the interactions between the user and the system. We\r\nimplemented the novel control (simulated the system again using the novel control), the subjects evaluated the system, and the\r\nresults showed that the novel control reduced the excessive load forces and accelerations and thus improved the human-system\r\ninteractions in terms of maneuverability, safety, and so forth. Finally, we proposed to use the findings to develop power assist\r\nsystems for manipulating heavy objects in industries that may improve interactions between the systems and the users....
Introduction. Sensorimotor cortex is activated similarly during motor execution and motor imagery. The study of functional connectivity\r\nnetworks (FCNs) aims at successfully modeling the dynamics of information flow between cortical areas. Materials and\r\nMethods. Seven healthy subjects performed 4 motor tasks (real foot, imaginary foot, real hand, and imaginary hand movements),\r\nwhile electroencephalography was recorded over the sensorimotor cortex. Event-Related Desynchronization/Synchronization\r\n(ERD/ERS) of themu-rhythm was used to evaluateMI performance. Source detection and FCNs were studied with eConnectome.\r\nResults and Discussion. Four subjects produced similar ERD/ERS patterns between motor execution and imagery during both\r\nhand and foot tasks, 2 subjects only during hand tasks, and 1 subject only during foot tasks. All subjects showed the expected\r\nbrain activation in well-performed MI tasks, facilitating cortical source estimation. Preliminary functional connectivity analysis\r\nshows formation of networks on the sensorimotor cortex during motor imagery and execution. Conclusions. Cortex activation\r\nmaps depict sensorimotor cortex activation, while similar functional connectivity networks are formed in the sensorimotor cortex\r\nboth during actual and imaginary movements. eConnectome is demonstrated as an effective tool for the study of cortex activation\r\nand FCN. The implementation of FCN in motor imagery could induce promising advancements in Brain Computer Interfaces....
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