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Background Electroencephalography (EEG)-based brain-computer interface (BCI) systems are mainly divided into three major paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). Here, we present a BCI dataset that includes the three major BCI paradigms with a large number of subjects over multiple sessions. In addition, information about the psychological and physiological conditions of BCI users was obtained using a questionnaire, and task-unrelated parameters such as resting state, artifacts, and electromyography of both arms were also recorded. We evaluated the decoding accuracies for the individual paradigms and determined performance variations across both subjects and sessions. Furthermore, we looked for more general, severe cases of BCI illiteracy than have been previously reported in the literature. Results Average decoding accuracies across all subjects and sessions were 71.1% (± 0.15), 96.7% (± 0.05), and 95.1% (± 0.09), and rates of BCI illiteracy were 53.7%, 11.1%, and 10.2% for MI, ERP, and SSVEP, respectively. Compared to the ERP and SSVEP paradigms, the MI paradigm exhibited large performance variations between both subjects and sessions. Furthermore, we found that 27.8% (15 out of 54) of users were universally BCI literate, i.e., they were able to proficiently perform all three paradigms. Interestingly, we found no universally illiterate BCI user, i.e., all participants were able to control at least one type of BCI system. Conclusions Our EEG dataset can be utilized for a wide range of BCI-related research questions. All methods for the data analysis in this study are supported with fully open-source scripts that can aid in every step of BCI technology. Furthermore, our results support previous but disjointed findings on the phenomenon of BCI illiteracy.

This chapter traces the origin of the term ‘Washington Consensus’ to a paper written for a conference in 1989 that aimed to explore how the set of ideas accepted as a basis for policy in developing countries had changed. Ten policies — covering the areas of macroeconomic discipline, microeconomic liberalization, and opening up the economy (globalization) — were asserted to be widely believed in Washington as widely needed in Latin America, and the conference papers were intended to explore the extent of agreement with those propositions in the region. The term subsequently evolved: while some people continued to use it in the original sense, it has also been identified with IMF/World Bank policies (and thus used to embrace capital account convertibility and the bipolar exchange rate doctrine as well as institution-building and anti-corruption policy) and with a belief in market fundamentalism. The chapter argues that this is a very different usage with little support (and certainly not consensus support) in Washington. It concludes by emphasizing that the reforms listed in 1989 are not adequate for the present day, and outlines desirable directions in which to supplement them.

An efficient method is given for computing the best straight line by least squares when there are statistical errors in both coordinates. Exact expressions are obtained for the variances of the slope and intercept.

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Abstract. Objective. While brain-computer interfaces (BCIs) for communication have reached considerable technical maturity, there is still a great need for state-of-the-art evaluation by end-users outside laboratory environments. To achieve this primary objective, it is necessary to augment a BCI with a series of components that allow end-users to type text effectively. Approach. This work presents the clinical evaluation of a motor imagery (MI) BCI text-speller, called BrainTree, by 6 severely disabled end-users and 10 able-bodied users. Additionally, we define a generic model of code-based BCI applications which serves as an analytical tool for evaluation and design. Main results. We show that all users achieved remarkable usability and efficiency outcomes in spelling. Furthermore, our model-based analysis highlights the added value of human-computer interaction (HCI) techniques and hybrid BCI error-handling mechanisms, and reveals the effects of BCI performances on usability and efficiency in code-based applications. Significance. This study demonstrates the usability potential of code-based MI spellers, with BrainTree being the first to be evaluated by a substantial number of end-users, establishing them as a viable, competitive alternative to other popular BCI spellers. Another major outcome of our modelbased analysis is the derivation of a 80% minimum command accuracy requirement for successful code-based application control, revising upwards previous estimates attempted in the literature.

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