Abstract-In our every day life, our brain is constantly processing information and paying attention, reacting accordingly, to all sorts of sensory inputs (auditory, visual, etc.). In some cases, there is a need to accurately measure a person's level of attention to monitor a sportsman performance, to detect Attention Deficit Hyperactivity Disorder (ADHD) in children, to evaluate the effectiveness of neuro-feedback treatment, etc.In this paper we propose a novel approach to extract, select and learn spectral-spatial patterns from electroencephalogram (EEG) recordings. Our approach improves over prior-art methods that was, typically, only concerned with power of specific EEG rhythms from few individual channels. In this new approach, spectral-spatial features from multichannel EEG are extracted by a two filtering stages: a filter-bank (FB) and common spatial patterns (CSP) filters. The most important features are selected by a Mutual Information (MI) based feature selection procedure and then classified using Fisher linear discriminant (FLD). The outcome is a measure of the attention level.An experimental study was conducted with 5 healthy young male subjects with their EEG recorded in various attention and non-attention conditions (opened eyes, closed eyes, reading, counting, relaxing, etc.). EEGs were used to train and evaluate the model using 4x4fold cross-validation procedure. Results indicate that the new proposed approach outperforms the prior-art methods and can achieve up to 89.4% classification accuracy rate (with an average improvement of up to 16%). We demonstrate its application with a two-players attention-based racing car computer game.

Abstract

Machine learning (ML) has widespread applications in catalyst discovery and reaction optimization. We present a theoryguided machine learning framework to evaluate the carbon monoxide (CO) conversion performance of noble metal catalysts in water-gas shift (WGS) reaction. Our study is based on an open source WGS dataset, which we modify significantly to be consistent with the chemical reaction principles. We apply state-of-the-art ML models including artificial neural networks, extreme gradient boosting to predict CO conversion percentage. These models show superior regression performance than the previously reported results in the literature. We further generalize the existing data structure by including physical, chemical and surface chemistry properties as fingerprint features that rationalize the importance of all the input features for CO conversion. We noticed that purely data-driven ML models frequently violate the thermodynamic equilibrium principle and predict unphysical CO conversion percentage. We address these two problems by developing a custom loss function and an additional activation function in our neural networks architecture. Our proposed theory-guided ML model displays high accuracy (R 2 score is 0.95 and root mean square error is 6.87) and physically robust predictions. The model also opens up promising possibilities to improve CO conversion percentage, which were previously unexplored in experiments.

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Brain-Computer Interface (BCI) is a relatively young research field which has seen a growing interest with associated number of publications over the last two decades. In this study we present the first bibliometric analysis of the BCI literature (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) from the Thomson Reuters's Institute for Scientific Information (ISI) Web of Knowledge. Thus, the main objectives of this bibliometric study are: 1) to explore the growth of BCI literature, 2) to assess if it follows Lotka's law of scientific productivity, 3) to identify authors, groups and countries contributing the most to BCI, 4) to reveal the characteristic of citation for the BCI literature, and finally, 5) to determine the core journals that published substantial portions of the literature on BCI. Results indicate that BCI literature follows a power law growth, has an average author count of 3.9 and an average page count of 7.09. More than half (52.73%) of the BCI literature is never cited, and 14 papers have been cited more than 100 times. The 3 most productive authors are leading BCI research groups, in Austria, Germany and the USA.