Assessing neurosky's usability to detect attention levels in an assessment exercise

This paper presents the results of a usability evaluation of the NeuroSky's MindSet (MS). Until recently most Brain Computer Interfaces (BCI) have been designed for clinical and research purposes partly due to their size and complexity. However, a new generation of consumer-oriented BCI has appeared for the video game industry. The MS, a headset with a single electrode, is based on electro-encephalogram readings (EEG) capturing faint electrical signals generated by neural activity. The electrical signal across the electrode is measured to determine levels of attention (based on Alpha waveforms) and then translated into binary data. This paper presents the results of an evaluation to assess the usability of the MS by defining a model of attention to fuse attention signals with user-generated data in a Second Life assessment exercise. The results of this evaluation suggest that the MS provides accurate readings regarding attention, since there is a positive correlation between measured and self-reported attention levels. The results also suggest there are some usability and technical problems with its operation. Future research is presented consisting of the definition a standardized reading methodology and an algorithm to level out the natural fluctuation of users' attention levels if they are to be used as inputs

Atlas-based classification algorithms for identification of informative brain regions in fMRI data

Multi-voxel pattern analysis (MVPA) has been successfully applied to neuroimaging data due to its larger sensitivity compared to univariate traditional techniques. Although a Searchlight strategy that locally sweeps all voxels in the brain is the most extended approach to assign functional value to different regions of the brain, this method does not offer information about the directionality of the results and it does not allow studying the combined patterns of more distant voxels. In the current study, we examined two different alternatives to searchlight. First, an atlas- based local averaging (ABLA, Schrouff et al., 2013a) method, which computes the relevance of each region of an atlas from the weights obtained by a whole-brain analysis. Second, a Multiple-Kernel Learning (MKL, Rakotomamonjy et al., 2008) approach, which combines different brain regions from an atlas to build a classification model. We evaluated their performance in two different scenarios where differential neural activity between conditions was large vs. small, and employed nine different atlases to assess the influence of diverse brain parcellations. Results show that all methods are able to localize informative regions when differences were large, demonstrating stability in the identification of regions across atlases. Moreover, the sign of the weights reported by these methods provides the sensitivity of multivariate approaches and the directionality of univariate methods. However, in the second context only ABLA localizes informative regions, which indicates that MKL leads to a lower performance when differences between conditions are small. Future studies could improve their results by employing machine learning algorithms to compute individual atlases fit to the brain organization of each participant.Spanish Ministry of Science and Innovation through grant PSI2016-78236-PSpanish Ministry of Economy and Competitiveness through grant BES-2014-06960

Analisi multivariata di dati EEG per lo studio dell'inibizione di ritorno

La presente tesi si propone di esaminare l’applicazione della Multivariate pattern analysis (MVPA) su dati EEG per lo studio dell’inibizione di ritorno. L’inibizione di ritorno è un fenomeno prodotto dall’orientamento automatico dell’attenzione che si manifesta solo quando l’intervallo fra il segnale esogeno e lo stimolo target è maggiore di 200 millisecondi. In questo caso, i tempi di reazione sono rallentati quando lo stimolo-target compare nella stessa posizione del segnale esogeno. Questo processo è importante per la regolazione del comportamento e può essere influenzato da diversi fattori, come l’età e le condizioni neurologiche. Nonostante i potenziali evento-relati (ERP) siano ampiamente utilizzati negli studi sull’orientamento e controllo dell’attenzione visuo-spaziale le tecniche di analisi di questi dati risultano ancora non del tutto adeguate. L’applicazione dell’MVPA permette di ovviare ad alcuni dei limiti dell’analisi classica univariata, consentendo, tramite l’utilizzo di classificatori, di analizzare i dati con una maggiore sensibilità e accuratezza, tenendo conto dei cambiamenti che avvengono contemporaneamente fra le diverse variabili. In questo studio il disegno sperimentale prevedeva due condizioni, in una in cui lo stimolo-target appariva nella stessa posizione del segnale esogeno, nell’atra, invece, lo stimolo target appariva in una posizione differente. L’applicazione dell’MVPA consiste nell’addestrare classificatori ad ogni intervallo temporale in grado di classificare sulla base dei dati EEG a disposizione se il soggetto si trova in una o nell’atra condizione. L’accuratezza calcolata di ogni modello funge, poi, da indicatore al fine di comprendere se in quel determinato istante c’è una differenza nei correlati neuronali in grado di guidare il classificatore nel discernere le due condizioni. Questo disegno viene poi ripetuto per tre sotto condizioni che dipendono dal lasso di tempo che intercorre fra la presentazione del segnale esogeno e la presentazione del target (250 ms, 400 ms, 500 ms). I risultati hanno mostrato che l'accuratezza di classificazione è significativamente superiore al caso in diversi intervalli temporali, permettendo di identificare la dinamica temporale con cui l'attività elettrica cerebrale discrimina le due condizioni.This thesis aims to examine the application of Multivariate pattern analysis (MVPA) on EEG data to study return inhibition. Return inhibition is a phenomenon produced by the automatic orientation of attention that occurs only when the interval between the exogenous signal and the target stimulus is greater than 200 milliseconds. In this case, reaction times are slowed down when the stimulus-target appears at the same location as the exogenous signal. This process is important for the regulation of behavior and can be influenced by several factors, such as age and neurological conditions. Although event-related potentials (ERPs) are widely used in studies of orientation and control of visuospatial attention the techniques for analyzing these data are still not fully adequate. The application of MVPA makes it possible to overcome some of the limitations of classical univariate analysis by allowing, through the use of classifiers, the data to be analyzed with greater sensitivity and accuracy, taking into account changes occurring simultaneously among different variables. In this study, the experimental design involved two conditions, in one in which the stimulus-target appeared in the same location as the exogenous signal, and in the other, the target stimulus appeared in a different location. The application of MVPA is to train classifiers at each time interval that can classify on the basis of the available EEG data whether the subject is in one or the other condition. The calculated accuracy of each pattern then serves as an indicator in order to understand whether at that particular instant there is a difference in the neuronal correlates that can guide the classifier in discerning the two conditions. This design is then repeated for three subconditions depending on the time lapse between exogenous signal presentation and target presentation (250 ms, 400 ms, 500 ms). The results showed that classification accuracy is significantly higher than chance in different time intervals, allowing identification of the temporal dynamics by which brain electrical activity discriminates between the two conditions

Inferring Cognition from fMRI Brain Images

Over the last few years, functional Magnetic Resonance Imaging (fMRI) has emerged as a new and powerful method to map the cognitive states of a human subject to specific functional areas of the subject brain. Although fMRI has been widely used to determine average activation in different brain regions, the problem of automatically decoding the cognitive state from instantaneous brain activations has received little attention. In this paper, we study this prediction problem on a complex time-series dataset that relates fMRI data (brain images) with the corresponding cognitive states of the subjects while watching three 20 minute movies. This work describes the process we used to reduce the extremely high-dimensional feature space and a comparison of the models used for prediction. To solve the prediction task we explored a standard linear model frequently used by neuroscientists, as well as a k-nearest neighbor model, that now are the state-of-art in this area. Finally, we provide experimental evidence that non-linear models such as multi-layer perceptron and especially recurrent neural networks are significantly better