The effect of breath pacing on task switching and working memory

The cortical and subcortical circuit regulating both cognition and cardiac autonomic interactions are already well established. This circuit has mainly been analyzed from cortex to heart. Thus, the heart rate variability (HRV) is usually considered a reflection of cortical activity. In this paper, we investigate whether HRV changes affect cortical activity. Short-term local autonomic changes were induced by three breathing strategies: spontaneous (Control), normal (NB) and slow paced breathing (SB). We measured the performance in two cognition domains: executive functions and processing speed. Breathing maneuvres produced three clearly differentiated autonomic states, which preconditioned the cognitive tasks. We found that the SB significantly increased the HRV low frequency (LF) power and lowered the power spectral density (PSD) peak to 0.1Hz. Meanwhile, executive function was assessed by the working memory test, whose accuracy significantly improved after SB, with no significant changes in the response times. Processing speed was assessed by a multitasking test. Consistently, the proportion of correct answers (success rate) was the only dependent variable affected by short-term and long-term breath pacing. These findings suggest that accuracy, and not timing of these two cognitive domains would benefit from short-term SB in this study population.Fil: Bonomini, Maria Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; ArgentinaFil: Calvo, Mikel Val. Universidad Nacional de Educación a Distancia; España. Universidad Politécnica de Cartagena; EspañaFil: Morcillo, Alejandro Diaz. Universidad Politécnica de Cartagena; EspañaFil: Segovia, Maria Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Vicente, Jose Manuel Ferrandez. Universidad Politécnica de Cartagena; EspañaFil: Fernández Jover, Eduardo. Universidad de Miguel Hernández; Españ

Toward an Improvement of the Analysis of Neural Coding

Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.This work has been supported in part by the Spanish national research program (MAT2015-69967-C3-1), by Research Chair Bidons Egara and by a research grant of the Spanish Blind Organization (ONCE)

Setting the Parameters for an Accurate EEG (Electroencephalography)-Based Emotion Recognition System

The development of a suitable EEG-based emotion recognition system has become a target in the last decades for BCI (Brain Computer Interface) applications. However, there are scarce algorithms and procedures for real time classification of emotions. In this work we introduce a new approach to select the appropriate parameters in order to build up a real-time emotion recognition system. We recorded the EEG-neural activity of 5 participants while they were looking and listening to an audiovisual database composed by positive and negative emotional video clips. We tested 11 different temporal window sizes, 6 ranges of frequency bands and 5 areas of interest located mainly on prefrontal and frontal brain regions. The most accurate time window segment was selected for each participant, giving us probable positive and negative emotional characteristic patterns, in terms of the most informative frequency-location pairs. Our preliminary results provide a reliable way to establish the more appropriate parameters to develop an accurate EEG-based emotion classifier in real-time

Toward an Improvement of the Analysis of Neural Coding

Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered