Context–aware Learning for Generative Models

This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters through expectation-maximization (EM) improves over the regular unsupervised case and can approach the performances of supervised learning, despite the absence of any explicit ground-truth data labeling. By direct application of the missing information principle (MIP), the algorithms' performances are proven to range between the conventional supervised and unsupervised MLE extremities proportionally to the information content of the contextual assistance provided. The acquired benefits regard higher estimation precision, smaller standard errors, faster convergence rates, and improved classification accuracy or regression fitness shown in various scenarios while also highlighting important properties and differences among the outlined situations. Applicability is showcased with three real-world unsupervised classification scenarios employing Gaussian mixture models. Importantly, we exemplify the natural extension of this methodology to any type of generative model by deriving an equivalent context-aware algorithm for variational autoencoders (VAs), thus broadening the spectrum of applicability to unsupervised deep learning with artificial neural networks. The latter is contrasted with a neural-symbolic algorithm exploiting side information

Context-Aware Brain-Computer Interfaces

Systems using brain-generated signals can control complex, smart devices by taking into account information about the situation at hand, as well as the operator’s cognitive state

Learning from EEG Error-related Potentials in Noninvasive Brain-Computer Interfaces

We describe error-related potentials generated while a human user monitors the performance of an external agent and discuss their use for a new type of Brain-Computer Interaction. In this approach, single trial detection of error-related EEG potentials is used to infer the optimal agent behavior by decreasing the probability of agent decisions that elicited such potentials. Contrasting with traditional approaches, the user acts as a critic of an external autonomous system instead of continuously generating control commands. This sets a cognitive monitoring loop where the human directly provides information about the overall system performance that, in turn, can be used for its improvement. We show that it is possible to recognize erroneous and correct agent decisions from EEG (average recognition rates of 75.8% and 63.2%, respectively), and that the elicited signals are stable over long periods of time (from 50 to $>$600 days). Moreover, these performances allow to infer the optimal behavior of a simple agent in a Brain-Computer Interaction paradigm after a few trials

A Probabilistic Approach to Handle Missing Data for Multi-Sensory Activity Recognition

Context and activity recognition in complex scenarios is prone to data loss due to disconnections, sensor failure, transmission problems, etc. This generally implies significant changes in the recognition performance. In the case of classifier fusion faulty sensors can be removed from the recognition chain to overcome this issue. Alternatively, we can try to compensate or impute data to replace the missing signals. In this paper we proposed a probabilistic method for imputation of missing data. The proposed method is based on conditional Gaussian distribution and has been previously applied in other fields, such as speech recognition and bioinformatics, but not in for activity recognition. Our method exploits the correlation among classifier outputs to infer missing values of decision profile from available values in a probabilistic manner. We assess the method performance using two datasets in a car manufacturing and in a daily activities scenario with three different configuration of sensors. Results show the advantages of the probabilistic estimation over other common methods such as removing and clustering. The method is also applicable in other classification problems which uses fusion methods to combine decisions of classifiers

Robust self-localisation and navigation based on hippocampal place cells

A computational model of the hippocampal function in spatial learning is presented. A spatial representation is incrementally acquired during exploration. Visual and self-motion information is fed into a network of rate-coded neurons. A consistent and stable place code emerges by unsupervised Hebbian learning between place- and head direction cells. Based on this representation, goal-oriented navigation is learnt by applying a reward-based learning mechanism between the hippocampus and nucleus accumbens. The model, validated on a real and simulated robot, successfully localises itself by recalibrating its path integrator using visual input. A navigation map is learnt after about 20 trials, comparable to rats in the water maze. In contrast to previous works, this system processes realistic visual input. No compass is needed for localisation and the reward-based learning mechanism extends discrete navigation models to continuous space. The model reproduces experimental findings and suggests several neurophysiological and behavioural predictions in the rat. (c) 2005 Elsevier Ltd

Single Trial Recognition of Anticipatory Slow Cortical Potentials: The Role of Spatio-Spectral Filtering

Single trial recognition of slow cortical potentials (SCPs) from full-band EEG (FbEEG) faces different challenges to classical EEG such as noisy, high magnitude (~±100µV) infra slow oscillations (ISO) with f<0.1Hz and high frequency spatial noise from a variety of artifacts. We analyze offline the anticipation related SCPs recorded from 11 subjects over two days in a variation of the Contingent Negative Variation (CNV) paradigm with Go and No-go conditions in an assistive technology framework. The results suggest that widely used spatial filters such as Common Average Referencing (CAR) and Laplacian are sub-optimal for the single trial analysis of SCPs. We show that a spatial smoothing filter (SSF), which in combination with CAR enhances the spatially distributed SCP while attenuating high frequency spatial noise. We report, first, that a narrow band filter in the range [0.1 1]Hz captures anticipation related SCP better and effectively reduces ISOs. Second, the SSF in combination with CAR outperforms CAR-alone and Laplacian spatial filters. Third, we compare linear and quadratic classifiers calculated using optimally filtered Cz electrode potentials and report that the best methods resulted in single trial classification accuracies of 83±4%, where classifiers were trained on day 1 and tested using data from day 2, to ensure generalization capabilities across days (1-7 days)