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This paper presents a systematic analysis of a game controlled by a Brain-Computer Interface (BCI) based on Steady-State Visually Evoked Potentials (SSVEP). The objective is to understand BCI systems from the Human-Computer Interface (HCI) point of view, by observing how the users interact with the game and evaluating how the interface elements influence the system performance. The interactions of 30 volunteers with our computer game, named “Get Coins,” through a BCI based on SSVEP, have generated a database of brain signals and the corresponding responses to a questionnaire about various perceptual parameters, such as visual stimulation, acoustic feedback, background music, visual contrast, and visual fatigue. Each one of the volunteers played one match using the keyboard and four matches using the BCI, for comparison. In all matches using the BCI, the volunteers achieved the goals of the game. Eight of them achieved a perfect score in at least one of the four matches, showing the feasibility of the direct communication between the brain and the computer. Despite this successful experiment, adaptations and improvements should be implemented to make this innovative technology accessible to the end user.

Objective. Adapted from the concept of channel capacity, the information transfer rate (ITR) has been widely used to evaluate the performance of a brain–computer interface (BCI). However, its traditional formula considers the model of a discrete memoryless channel in which the transition matrix presents very particular symmetries. As an alternative to compute the ITR, this work indicates a more general closed-form expression—also based on that channel model, but with less restrictive assumptions—and, with the aid of a selection heuristic based on a wrapper algorithm, extends such formula to detect classes that deteriorate the operation of a BCI system. Approach. The benchmark is a steady-state visually evoked potential (SSVEP)-based BCI dataset with 40 frequencies/classes, in which two scenarios are tested: (1) our proposed formula is used and the classes are gradually evaluated in the order of the class labels provided with the dataset; and (2) the same formula is used but with the classes evaluated progressively by a wrapper algorithm. In both scenarios, the canonical correlation analysis (CCA) is the tool to detect SSVEPs. Main results. Before and after class selection using this alternative ITR, the average capacity among all subjects goes from 3.71 1.68 to 4.79 0.70 bits per symbol, with p -value  <0.01, and, for a supposedly BCI-illiterate subject, her/his capacity goes from 1.53 to 3.90 bits per symbol. Significance. Besides indicating a consistent formula to compute ITR, this work provides an efficient method to perform channel assessment in the context of a BCI experiment and argues that such method can be used to study BCI illiteracy.

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Heated debates involving reforms in the educational system are becoming more and more frequent in recent years, mostly due to the increasingly evident shortcomings in the educational system and its difficulties to evolve at the same pace as technological development. Since nowadays people spend much of their time interacting directly or indirectly with technological devices, one can think of using this involvement with educational purposes. Through this interaction people have easy and inexpensive access to a vast amount of information. In this sense, one can think of methodologies to improve education by focusing on the foundations of knowledge rather than the emphasis on the memorization of contents. Therefore, the aim of this work is to propose and validate an interactive content authoring system as well as a virtual classroom where lessons are taught by avatars in an attempt to make learning experience richer and more motivating to students.

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