BUSCADOR RECOLECTA

Ambisonics es un formato de sonido de esfera completa en el que, en contraste con otros formatos
envolventes, los canales no distribuyen la señal que alimenta a cada altavoz. Por ello, cuando se
desea cuantificar diferencias entre diferentes codificaciones para evaluar la calidad de diferentes
micrófonos ambisónicos, por ejemplo, la comparación directa de los niveles de cada canal no
proporciona resultados fácilmente interpretables. En esta comunicación se presenta una aplicación
en Matlab que facilita la comparación en tiempo real de señales en formato ambisónico hasta de
séptimo orden. Las diferencias en cuanto a distribución espacial de energía sonora se visualizan en
una proyección azimutal modificada que preserva las proporciones de las áreas. Para ello, se
decodifica cada señal a una malla de altavoces virtuales espaciados uniformemente. Un cálculo
posterior de los valores eficaces en cada punto permite representar la distribución de energía de
cada señal y evaluar así su imagen espacial. Se muestran, a modo de ejemplo de uso,
representaciones de diferentes órdenes de codificación para una misma escena sonora y del efecto
sobre señales de primer orden de diferentes técnicas paramétricas de upmixing., Ambisonics is a full-sphere sound format in which, in contrast to other surround formats, the channels
do not distribute the signal feeding each loudspeaker. Therefore, to quantify differences between
different encodings to evaluate the quality of different Ambisonics microphones, for example, direct
comparison of the levels of each channel does not provide easily interpretable results. In this
communication a Matlab application is presented, which facilitates the real-time comparison of signals
in Ambisonics format up to seventh order. The differences in spatial distribution of sound energy are
visualized in a modified azimuthal projection that preserves the proportions of the areas. For this
purpose, each signal is decoded to a mesh of evenly spaced virtual loudspeakers. A subsequent
calculation of the RMS values at each point makes it possible to represent the energy distribution of
each signal and thus evaluate its spatial image. As examples of use, representations of different
coding orders for the same sound scene and the effect of different parametric upmixing techniques
in first order signals are shown., Este trabajo ha sido financiado por el Ministerio de Ciencia e Innovación y la Agencia Estatal de
Investigación, Proyecto PID2020-118829RB-I00 / MCIN/ AEI / 10.13039/501100011033

Objective. Motor imagery is the mental simulation of movements. It is a common paradigm to design brain-computer interfaces (BCIs) that elicits the modulation of brain oscillatory activity similar to real, passive and induced movements. In this study, we used peripheral stimulation to provoke movements of one limb during the performance of motor imagery tasks. Unlike other works, in which induced movements are used to support the BCI operation, our goal was to test and improve the robustness of motor imagery based BCI systems to perturbations caused by artificially generated movements. Approach. We performed a BCI session with ten participants who carried out motor imagery of three limbs. In some of the trials, one of the arms was moved by neuromuscular stimulation. We analysed 2-class motor imagery classifications with and without movement perturbations. We investigated the performance decrease produced by these disturbances and designed different computational strategies to attenuate the observed classification accuracy drop. Main results. When the movement was induced in a limb not coincident with the motor imagery classes, extracting oscillatory sources of the movement imagination tasks resulted in BCI performance being similar to the control (undisturbed) condition; when the movement was induced in a limb also involved in the motor imagery tasks, the performance drop was significantly alleviated by spatially filtering out the neural noise caused by the stimulation. We also show that the loss of BCI accuracy was accompanied by weaker power of the sensorimotor rhythm. Importantly, this residual power could be used to predict whether a BCI user will perform with sufficient accuracy under the movement disturbances. Significance. We provide methods to ameliorate and even eliminate motor related afferent disturbances during the performance of motor imagery tasks. This can help improving the reliability of current motor imagery based BCI systems., C V was supported by MINECO-RyC-2014-15671 and PID2020-118829RB-I00. A R was supported by EU-EUROSTARS E!113550 and H2020-EICFETPROACT-2019-951910-MAIA. K R M was supported in part by the Institute of Information & Communications Technology Planning & Evaluation (IITP) Grants funded by the Korea Government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning) and funded by the Korea Government (No. 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University), and was partly supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A and 01IS18037A; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689. VVN was partly supported by the Basic Research Program of the National Research University Higher School of Economics (HSE University).

Instantaneous and peak frequency changes in neural oscillations have been linked to many perceptual, motor, and cognitive processes. Yet, the majority of such studies have been performed in sensor space and only occasionally in source space. Furthermore, both terms have been used interchangeably in the literature, although they do not reflect the same aspect of neural oscillations. In this paper, we discuss the relation between instantaneous frequency, peak frequency, and local frequency, the latter also known as spectral centroid. Furthermore, we propose and validate three different methods to extract source signals from multichannel data whose (instantaneous, local, or peak) frequency estimate is maximally correlated to an experimental variable of interest. Results show that the local frequency might be a better estimate of frequency variability than instantaneous frequency under conditions with low signal-to-noise ratio. Additionally, the source separation methods based on local and peak frequency estimates, called LFD and PFD respectively, provide more stable estimates than the decomposition based on instantaneous frequency. In particular, LFD and PFD are able to recover the sources of interest in simulations performed with a realistic head model, providing higher correlations with an experimental variable than multiple linear regression. Finally, we also tested all decomposition methods on real EEG data from a steady-state visual evoked potential paradigm and show that the recovered sources are located in areas similar to those previously reported in other studies, thus providing further validation of the proposed methods., C.V. was supported by the Spanish Ministry of Economy with Grant RyC 2014-15671, Spanish Ministry of Research and Innovation PID2020-118829RB-100, H2020-FETPROACT-EIC-2018-2020 Grant MAIA-951910, Diputacion Foral de Gipuzkoa Brain2Move project, Diputacion Foral de Gipuzkoa Neurocog Project, and Ikerbasque (Basque Foundation for Science). K.G. was supported by the Basque Government postdoctoral grant POS-2021-1-0007. G.N. was partially funded by the German Research Foundation (DFG, SFB936 Z3 and TRR169, B4). K.-R.M. work was supported by German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115 and 01GQ0850; by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grants funded by the Korea government (MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University and No. 2022-0-00984, Development of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation).