Brain-computer interfaces (BCls) based on motor imagery (imagined movements, 1M) are among the most common BCls for the rehabilitation of paralyzed patients. However, it is possible that attempted movements (AM) would be more an effective alternative for 1M. Unlike 1M, AM are difficult to study outside of clinical practice. Nikulin et al. (2008) suggest that quasi-movements (QM) could help model AM in healthy participants without immobilizing interventions. QM result from the amplitude reduction of an overt movement, which leads to the practical absence of electromyography (EMG) response. The performance of QM may have features that may distance QM from AM. Here, we examined the compatibility of QM with a saccade task, which modelled visual interaction with the outside world during the practical use of a BCI. In a study involving 24 volunteers, we used electroencephalography (EEG), EMG, and conducted an extensive survey of the participants. We expected that, compared to 1M, QM in the dual-task condition would be easier and less tiring and would be accompanied by greater event-related desynchronization (ERD) of the sensorimotor rhythms. Our hypotheses were based on the assumption that like AM and unlike 1M, QM is a more external task, and so is more compatible with the saccade task. We reproduced the effect of greater ERD for QM in the dual-task condition but did not find any significant difference between the difficulty or tediousness of QM and 1M. Nevertheless, the survey data gave us important insights into the challenges participants faced when performing QM. Despite EMG values similar to 1M, the feeling of muscle tension experienced by the participants correlated with mean EMG values. The main challenge in performing QM by the participants was to make movements without an amplitude. Performing QM conflicted with the illusion of movement that was supposed to accompany them: without proprioceptive feedback, participants doubt the reality of QM. Our results can be used to improve the procedure of QM training, which should bring them closer to genuine attempts of movements in the eyes of participants.

In existing brain-computer interfaces (BCIs) a mental state classifier typically should be trained individually for every user. This means that a user has to perform rather tedious and time-consuming mental tasks before starting to use a BCI. Moreover, amount of train data that can be recorded from a single user is strongly limited, thus limiting quality of classifier training. The range of brain signal variations between the users, however, is not infinite: background and task-related signal patterns can be similar within certain groups of the users. In this work we propose a method for finding users whose data look similar from the classifier's "point of view". It allows for concatenating data from the users demonstrating similarity between their signals, so that larger training sets can be formed, or a classifier trained on one user can be applied in training of a classifier for another user.

Demonstration of faster target selection by gaze compared to computer mouse so far was limited to targets attracting attention due to their visual saliency. This task, however, can be performed much faster with modern computer vision systems. Can gaze be faster than mouse in a more “intentional” selection task: when targets and non-targets do not significantly differ by their visual features? We propose that this may be the case when targets are moving at speeds beneficial for smooth pursuit eye movements. 16 healthy participants were asked to select 20 balls numbered 1 to 20 in numerical order. Balls were moving linearly at a screen in different directions at 12°/s speed. We compared selection made using a consumer grade eye tracker and a simple smooth pursuit detection algorithm with selection made using a computer mouse, either with clicks or pursuit. Compared to both mouse selection techniques, gaze selection was significantly faster and was experienced as more convenient by all participants.

The P300-based brain-computer interface (P300 BCI) is currently a very popular topic in assistive technology development. However, only a few simple P300 BCI-based games have been designed so far. Here, we analyze the shortcomings of this BCI in gaming applications and show that solutions for overcoming them already exist, although these techniques are dispersed over several different games. Additionally, new approaches to improve the P300 BCI accuracy and flexibility are currently being proposed in the more general P300 BCI research. The P300 BCI, even in its current form, not only exhibits relatively high speed and accuracy, but also can be used without user training, after a short calibration. Taking these facts together, the broader use of the P300 BCI in BCI-controlled video games is recommended.

In this paper, sparse representation (factorization) of a data matrix is first discussed. An overcomplete basis matrix is estimated by using the K(cid:0)means method. We have proved that for the estimated overcom- plete basis matrix, the sparse solution (coefficient matrix) with minimum l1(cid:0)norm is unique with probability of one, which can be obtained using a linear programming algorithm. The comparisons of the l1(cid:0)norm so- lution and the l0(cid:0)norm solution are also presented, which can be used in recoverability analysis of blind source separation (BSS). Next, we ap- ply the sparse matrix factorization approach to BSS in the overcomplete case. Generally, if the sources are not sufficiently sparse, we perform blind separation in the time-frequency domain after preprocessing the observed data using the wavelet packets transformation. Third, an EEG experimental data analysis example is presented to illustrate the useful- ness of the proposed approach and demonstrate its performance. Two almost independent components obtained by the sparse representation method are selected for phase synchronization analysis, and their peri- ods of significant phase synchronization are found which are related to tasks. Finally, concluding remarks review the approach and state areas that require further study.