Spatiotemporal pattern | EPFL Graph Search

Spatiotemporal patterns are patterns that occur in a wide range of natural phenoma and are characterized by a spatial and temporal patterning. The general rules of pattern formation hold. In contrast to "static", pure spatial patterns, the full complexity of spatiotemporal patterns can only be recognized over time. Any kind of traveling wave is a good example of a spatiotemporal pattern. Besides the shape and amplitude of the wave (spatial part), its time-varying position (and possibly shape) in space is an essential part of the entire pattern. The distinction between spatial and spatio-temporal patterns in nature is not clear-cut because a static, invariable pattern will never occur in the strict sense. Even rock formations will slowly change on a time-scale of tens of millions of years, therefore the distinction lies in the time scale of change in relation to human experience. Already the snapshot state of a dune will usually be taken as an example of a purely spatial pattern al

EEG-based Recognition of Covert Attentional Shifts

Catherine Brandner, Ricardo Andres Chavarriaga Lozano

Attentional shifts precede most of the perceptual processes, and therefore their recognition may constitute an interesting process for both the study of human perception as well as the design of EEG-based Brain Computer Interfaces (BCI). Several studies have focused on EEG modulations of spatial attention, pointing out to changes in both Alpha and Gamma bands (Thut et al. 2006 J. Neurosci 26:9494; Tallon-Baudry and Bertrand 1999 T Cogn Sci 3:151; Jensen et al. 2007 TINS 30:317). This work aims at further characterization of EEG correlates of attention shifts to evaluate their potential use in BCI applications. We recorded 32-channel scalp EEG while subjects covertly shift their attention towards one out of several spatial targets. Consistently with previous studies, characterization of the spatio-temporal patterns using time-frequency analysis (Morlet wavelet transformation), shows main modulations in the alpha and gamma frequency bands. Moreover, it is also shown that these modulations are characterized by intermittent episodes of activity (Palix et al. 2006 Perception 25:234). Furthermore, we assess the EEG-based recognition of spatial attention -time and target location of the shift- using a machine learning approach based on the detection of informative, episodic activity. The proposed approach is based on the hypothesis that recognition may be improved by identifying the moments where the underlying neural phenomena is more salient (e. g. the appearance of episodic oscillations related to attentional shifts), and performing the classification based mainly -if not exclusively- on the activity during these periods. Our results show accurate recognition of attentional shifts (>75%) based on the scalp EEG signal. Moreover, the proposed approach allow us to efficiently detect endogenous processes loosely synchronized to external events. These results provides evidence supporting the feasibility of using attentional processes in Brain-Computer Interfaces.

2008

Molecular architecture of the developing mouse brain

Irina Khven, Gioele La Manno

The mammalian brain develops through a complex interplay of spatial cues generated by diffusible morphogens, cell-cell interactions and intrinsic genetic programs that result in probably more than a thousand distinct cell types. A complete understanding of this process requires a systematic characterization of cell states over the entire spatiotemporal range of brain development. The ability of single-cell RNA sequencing and spatial transcriptomics to reveal the molecular heterogeneity of complex tissues has therefore been particularly powerful in the nervous system. Previous studies have explored development in specific brain regions(1-8), the whole adult brain(9) and even entire embryos(10). Here we report a comprehensive single-cell transcriptomic atlas of the embryonic mouse brain between gastrulation and birth. We identified almost eight hundred cellular states that describe a developmental program for the functional elements of the brain and its enclosing membranes, including the early neuroepithelium, region-specific secondary organizers, and both neurogenic and gliogenic progenitors. We also used in situ mRNA sequencing to map the spatial expression patterns of key developmental genes. Integrating the in situ data with our single-cell clusters revealed the precise spatial organization of neural progenitors during the patterning of the nervous system. A comprehensive single-cell transcriptomic atlas of the mouse brain between gastrulation and birth identifies hundreds of cellular states and reveals the spatiotemporal organization of brain development.

NATURE PORTFOLIO2021

Timbre and Rhythmic TRAP-TANDEM features for music information retrieval

Nicolas Scaringella

The enormous growth of digital music databases has led to a comparable growth in the need for methods that help users organize and access such information. One area in particular that has seen much recent research activity is the use of automated techniques to describe audio content and to allow for its identification, browsing and retrieval. Conventional approaches to music content description rely on features characterizing the shape of the signal spectrum in relatively short-term frames. In the context of Automatic Speech Recognition (ASR), Hermansky \cite{Hermansky_1} described an interesting alternative to short-term spectrum features, the TRAP-TANDEM approach which uses long-term band-limited features trained in a supervised fashion. We adapt this idea to the specific case of music signals and propose a generic system for the description of temporal patterns. The same system with different settings is able to extract features describing either timbre or rhythmic content. The quality of the generated features is demonstrated in a set of music retrieval experiments and compared to other state-of-the-art models.

2008

EEG-based Recognition of Covert Attentional Shifts

Catherine Brandner, Ricardo Andres Chavarriaga Lozano

2008