Flavio Camarrone

ERP Response Unveils Effect of Second Language Manipulation on First Language Processing

Lexical access in bilinguals has been considered either selective or non-selective and evidence exists in favor of both hypotheses. We conducted a linguistic experiment to assess whether a bilingual’s language mode influences the processing of first language information. We recorded event related potentials during a semantic priming paradigm with a covert manipulation of the second language (L2) using two types of stimulus presentations (short and long). We observed a significant facilitation of word pairs related in L2 in the short version reflected by a decrease in N400 amplitude in response to target words related to the English meaning of an inter-lingual homograph (homograph-unrelated group). This was absent in the long version, as the N400 amplitude for this group was similar to the one for the control-unrelated group. We also interviewed the participants whether they were aware of the importance of L2 in the experiment. We conclude that subjects participating in the long and short versions were in different language modes: closer to monolingual mode for the long and closer to bilingual mode for the short version; and that awareness about covert manipulation of L2 can influence the language mode, which in its turn influences the processing of the first language.

Fully Bayesian tensor-based regression

N-way (or multiway) Partial Least Squares (NPLS) regression is a successful algorithm for solving ill-conditioned and high-dimensional problems. However, the selection of the latent space dimensionality, when performed manually, becomes a critical issue in the presence of irrelevant, redundant and noisy information and can lead to overfitting, and when using cross-validation one can still not guarantee a good predictive performance. We propose a fully Bayesian N-way partial least squares regression (BNPLS) with an automatic relevance determination (ARD) prior on the factor matrices so that the number of latent components can be determined automatically without requiring specific assumptions. Using synthetic data, we compare the performance of BNPLS with conventional NPLS, standard partial least squared (PLS) and state-of-the-art higher-order PLS (HOPLS). Results show that BNPLS consistently achieves a better or comparable performance.

Cortical distribution of N400 potential in response to semantic priming with visual non-linguistic stimuli

We conducted a study on visual semantic priming using related and unrelated image pairs while simultaneously recording electroencephalography (EEG) from 27 scalp electrodes and electrocorticography (ECoG) from a mixture of deep brain and subdural grid/strip electrodes in the left and right hippocampus, the right temporo-basal and temporo-lateral cortices, and the left temporal cortex. The EEG data showed a clear centro-parietal, bi-hemispheric N400 effect in response to unrelated image-pairs compared to related ones. Although with ECoG the N400 effect was more widely spread across both hemispheres, compared to linguistic stimuli, it was relatively localized within each ECoG grid as it was present only in some electrodes and, in some cases, even had its polarity reversed. We advocate this could be due to some grids gauging dipoles at different positions when covering sulci and gyri.

Decoding Steady-State Visual Evoked Potentials From Electrocorticography

We report on a unique electrocorticography (ECoG) experiment in which Steady-State Visual Evoked Potentials (SSVEPs) to frequency- and phase-tagged stimuli were recorded from a large subdural grid covering the entire right occipital cortex of a human subject. The paradigm is popular in EEG-based Brain Computer Interfacing where selectable targets are encoded by different frequency- and/or phase-tagged stimuli. We compare the performance of two state-of-the-art SSVEP decoders on both ECoG- and scalp-recorded EEG signals, and show that ECoG-based decoding is more accurate for very short stimulation lengths (i.e., less than 1 s). Furthermore, whereas the accuracy of scalp-EEG decoding benefits from a multi-electrode approach, to address interfering EEG responses and noise, ECoG decoding enjoys only a marginal improvement as even a single electrode, placed over the posterior part of the primary visual cortex, seems to suffice. This study shows, for the first time, that EEG-based SSVEP decoders can in principle be applied to ECoG, and can be expected to yield faster decoding speeds using less electrodes.

How does the brain process mild versus strong violations in music? A pilot study using event-related potentials

Previous research has shown by using event-related potentials (ERPs) that the human brain can process and understand music at a pre-attentive level. Music-specific ERPs include the Early Right Anterior Negativity (ERAN) and a late Negativity (N5). This study aims to further investigate this issue using two types of syntactic manipulations in music: mild violations, containing no out-of-key tones and strong violations, containing out-of-key tones. We will examine whether both manipulations will elicit the same ERPs.

Fast algorithm for multiway regression

Higher-Order Partial Least Squares (HOPLS) regression is a powerful framework for dealing with ill-conditioned and multiway data. However, it relies on Higher-Order Orthogonal Iteration (HOOI) for extracting the latent factors which can be time consuming in case of large matrices. In this work, we propose an improved version of HOPLS, called fast Higher-Order Partial Least Squares (fHOPLS), which inherits the predictive property of HOPLS while demoting its time complexity by requiring less iterations for extracting the latent components. We compare fHOPLS and HOPLS on two sets of synthetic data and, for the sake of exposition, also unfolded partial least squared (PLS) and N-way PLS (NPLS). Results from the first data set confirms that the predictive performance of fHOPLS is comparable to HOPLS, and outperforms both PLS and NPLS, while results from the second data set confirm the faster computation of fHOPLS over HOPLS.

Stable Voice Clusters Identified When Using the Maximum versus Minimum Intensity Curve in the Phonetogram.

Objective: To examine whether the minimum as well as the maximum voice intensity (i.e. sound pressure level, SPL) curves of a voice range profile (VRP) are required when discovering different voice groups based on a clustering analysis. In this approach, no a priori labeling of voice types is used. Patients and Methods: VRPs of 194 (84 male and 110 female) professional singers were registered and processed. Cluster analysis was performed with the use of features related to (1) both the maximum and minimum SPL curves and (2) the maximum SPL curve only. Results: Features related to the maximum as well as the minimum SPL curves showed three clusters in both male and female voices. These clusters, or voice groups, are based on voice types with similar VRP features. However, when using features related only to the maximum SPL curve, the clusters became less obvious. Conclusion: Features related to the maximum and minimum SPL curves of a VRP are both needed in order to identify the three voice clusters.