RESUMEN
Objective.Error-related potential (ErrP) is a potential elicited in the brain when humans perceive an error. ErrPs have been researched in a variety of contexts, such as to increase the reliability of brain-computer interfaces (BCIs), increase the naturalness of human-machine interaction systems, teach systems, as well as study clinical conditions. Still, there is a significant challenge in detecting ErrP from a single trial, which may hamper its effective use. The literature presents ErrP detection accuracies quite variable across studies, which raises the question of whether this variability depends more on classification pipelines or on the quality of elicited ErrPs (mostly directly related to the underlying paradigms).Approach.With this purpose, 11 datasets have been used to compare several classification pipelines which were selected according to the studies that reported online performance above 75%. We also analyze the effects of different steps of the pipelines, such as resampling, window selection, augmentation, feature extraction, and classification.Main results.From our analysis, we have found that shrinkage-regularized linear discriminant analysis is the most robust method for classification, and for feature extraction, using Fisher criterion beamformer spatial features and overlapped window averages result in better classification performance. The overall experimental results suggest that classification accuracy is highly dependent on user tasks in BCI experiments and on signal quality (in terms of ErrP morphology, signal-to-noise ratio (SNR), and discrimination).Significance.This study contributes to the BCI research field by responding to the need for a guideline that can direct researchers in designing ErrP-based BCI tasks by accelerating the design steps.
Asunto(s)
Interfaces Cerebro-Computador , Humanos , Electroencefalografía/métodos , Reproducibilidad de los Resultados , Encéfalo , Sistemas Hombre-Máquina , AlgoritmosRESUMEN
Learning environments are a significant determinant of student behaviour, achievement and satisfaction. In this article we use students' reflective essays to identify key features of the learning environment that contributed to positive and transformative learning experiences. We explore the relationships between these features, the students' sense of safety in the learning environment (LE), the resulting learning challenge with which they could cope and their positive reports of the experience itself. Our students worked in a unique simulation of General Practice, the Safe and Effective Clinical Outcomes clinic, where they consistently reported positive experiences of learning. We analysed 77 essays from 2011 and 2012 using an immersion/crystallisation framework. Half of the students referred to the safety of the learning environment spontaneously. Students described deep learning experiences in their simulated consultations. Students valued features of the LE which contributed to a psychologically safe environment. Together with the provision of constructive support and immediate, individualised feedback this feeling of safety assisted students to find their own way through clinical dilemmas. These factors combine to make students feel relaxed and able to take on challenges that otherwise would have been overwhelming. Errors became learning opportunities and students could practice purposefully. We draw on literature from medical education, educational psychology and sociology to interpret our findings. Our results demonstrate relationships between safe learning environments, learning challenge and powerful learning experiences, justifying close attention to the construction of learning environments to promote student learning, confidence and motivation.