In optical systems, the range of distance near the point of focus where objects are perceived sharply is referred as depth-of-field; objects outside this region are defocused and blurred. Furthermore, ophthalmology studies state that the amplitude and the latency of visual evoked potentials are affected by defocusing. In this context, this paper evaluates a novel setup for a steady-state visual evoked potential (SSVEP) brain-computer interface, in which two stimuli are presented together in the center of the user's field of view but at different distances ensuring that if one stimulus is focused on, the other one is non-focused, and vice versa. The evaluationwas conductedwith eight healthy subjects who were asked to focus on just one stimulus at a time. An average accuracy rate of 0.93 was achieved for a time window of 4 s by employing well know SSVEP detection methods. Results show that distinguishable SSVEP can be elicited by the focused stimulus regardless of the non-focused one is also present in the field of view. Finally, this approach allows users to send commands through a stimuli selection by focusing mechanism that does not demand neck, head, and/or eyeball movements.

Introduction:The main drawback of a Brain-computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) that detects the emergence of visual evoked potentials (VEP) in reaction to flickering stimuli is its muscular dependence due to users must redirect their gaze to put the target stimulus in their field of view.In this work, a novel setup is evaluated in which two stimuli are placed together in the center of users' field of view, but with dissimilar distances from them, so that the target selection is performed by focus shifting instead of head, neck and/or eyeball movements. Methods: A model of VEP generation for the novel setup was developed. The Spectral F-test based on Bartett periodogram was used to evaluate the null hypothesis of absence of effects of the non-focused stimulus (NFS) within the VEP elicited by the focused stimulus (FS).To reinforce that there is not statistical evidence to support the presence of NFS effects, the PSDA detection method was employed to find the frequency of FS. Electroencephalographic signals of nine subjects were recorded. Results: Approximately in 80% of the tests, the null hypothesis with 5% level of significance was non-rejected at the fundamental frequency of NFS. The average of the accuracy rate attained with PSDA detection method was 79.4%. Conclusion: Results of this work become further evident to state that if the focused stimulus (FS) will be able to elicit distinguishable VEP pattern regardless the non-focused stimulus (NFS) is also present.

The selection of features is generally the most difficult field to model in BCIs. Therefore, time and effort are invested in individual feature selection prior to data set training. Another great difficulty regarding the model of the BCI topology is the brain signal variability between users. How should this topology be in order to implement a system that can be used by large number of users with an optimal set of features? The proposal presented in this paper allows for obtaining feature reduction and classifier selection based on software agents. The software agents contain Genetic Algorithms (GA) and a cost function. GA used entropy and mutual information to choose the number of features. For the classifier selection a cost function was defined. Success rate and Cohen's Kappa coefficient are used as parameters to evaluate the classifiers performance. The obtained results allow finding a topology represented as a neural model for an adaptive BCI, where the number of the channels, features and the classifier are interrelated. The minimal subset of features and the optimal classifier were obtained with the adaptive BCI. Only three EEG channels were needed to obtain a success rate of 93% for the BCI competition III data set IVa.

<p>Los niños que presentan discapacidad física corren un alto riesgo de desarrollar efectos adversos en su desarrollo cognitivo, debido a su incapacidad de interactuar con el medio. Múltiples estudios han demostrado el potencial de los robots como herramientas usadas para asistir actividades lúdicas, dado que, permiten el aprendizaje de habilidades cognitivas, sociales, motrices y de lenguaje. En este ámbito de aplicación, este artículo describe un estudio con 4 niños entre 11 y 17 años de edad, con el fin de evaluar un ambiente lúdico de asistencia tecnológica de bajo costo, para la rehabilitación del miembro superior, que, durante el tratamiento con el robot, permitió demostrar los conceptos cognitivos de causalidad, negación, juego simbólico y secuenciación. Al inicio y final de la intervención, se hizo una valoración por profesionales del área de psicología y fisioterapia. También, se entrevistó a los terapeutas y padres o cuidadores, donde se observó el progreso del componente cognitivo mediante el uso operativo del robot.</p>