Abstract. Two experiments on categorical rhythm perception are reported, the object of which was to investigate how listeners perceive discrete rhythmic categories while listening to rhythms performed on a continuous time scale. This is studied by considering the space of all temporal patterns (all possible rhythms made up of three intervals) and how they, in perception, are partitioned into categories, ie where the boundaries of these categories are located. This process of categorisation is formalised as the mapping from the continuous space of a series of time intervals to a discrete, symbolic domain of integer-ratio sequences. The methodological framework uses concepts from mathematics and psychology (eg convexity and entropy) that allow precise characterisations of the empirical results.In the first experiment, twenty-nine participants performed an identification task with 66 rhythmic stimuli (a systematic sampling of the performance space). The results show that listeners do not just perceive the time intervals between onsets of sounds as placed in a homogeneous continuum. Instead, they can reliably identify rhythmic categories, as a chronotopic time clumping map reveals. In a second experiment, the effect of metric priming was studied by presenting the same stimuli but preceded with a duple or triple metre subdivision. It is shown that presenting patterns in the context of a metre has a large effect on rhythmic categorisation: the presence of a specific musical metre primes the perception of specific rhythmic patterns.

Brain-computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give an overview of the various steps in the BCI cycle, i.e., the loop from the measurement of brain activity, classification of data, feedback to the subject and the effect of feedback on brain activity. In this article we will review the critical steps of the BCI cycle, the present issues and state-of-the-art results. Moreover, we will develop a vision on how recently obtained results may contribute to new insights in neurocognition and, in particular, in the neural representation of perceived stimuli, intended actions and emotions. Now is the right time to explore what can be gained by embracing real-time, online BCI and by adding it to the set of experimental tools already available to the cognitive neuroscientist. We close by pointing out some unresolved issues and present our view on how BCI could become an important new tool for probing human cognition.

The fingerings used by keyboard players are determined by a range of ergonomic (anatomic/motor), cognitive, and music-interpretive constraints. We have attempted to encapsulate the most important ergonomic constraints in a model. The model, which is presently limited to isolated melodic fragments, begins by generating all possible fingerings, limited only by maximum practical spans between finger pairs. Many of the fingerings generated in this way seldom occur in piano performance. In the next stage of the model, the difficulty of each fingering is estimated according to a system of rules. Each rule represents a specific ergonomic source of difficulty. The model was subjected to a preliminary test by comparing its output with fingerings written by pianists on the scores of a selection of short Czerny studies. Most fingerings recommended by pianists were among those fingerings predicted by the model to be least difficult; but the model also predicted numerous fingerings that were not recommended by pianists. A variety of suggestions for improving the predictive power of the model are explored. A significant upsurge in psychological studies of performance in the past 15 1. Davies, Kenny, and Barbenel (1989) investigated the interface between the trumpet mouthpiece and the mouth.2. We number the fingers according to standard keyboard practice: 1 = thumb, 2 = index finger, ..., and 5 = little finger. Italics distinguish finger numbers from other numbers in the text.

Brain Computer Interfaces could be useful in rehabilitation of movement, perhaps also for gait. Until recently, research on movement related brain signals has not included measuring electroencephalography (EEG) during walking, because of the potential artifacts. We investigated if it is possible to measure the event Related Desynchronization (ERD) and event related spectral perturbations (ERSP) during walking. Six subjects walked on a treadmill with a slow speed, while EEG, electromyography (EMG) of the neck muscles and step cycle were measured. A Canonical Correlation Analysis (CCA) was used to remove EMG artifacts from the EEG signals. It was shown that this method correctly deleted EMG components. A strong ERD in the mu band and a somewhat less strong ERD in the beta band were found during walking compared to a baseline period. Furthermore, lateralized ERSPs were found, depending on the phase in the step cycle. It is concluded that this is a promising method to use in BCI research on walking. These results therefore pave the way for using brain signals related to walking in a BCI context.

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