Online Extraction and Single Trial Analysis of Regions Contributing to Erroneous Feedback Detection

International audienceUnderstanding how the brain processes errors is an essential and active field of neuroscience. Real time extraction and analysis of error signals provide an innovative method of assessing how individuals perceive ongoing interactions without recourse to overt behaviour. This area of research is critical in modern Brain–Computer Interface (BCI) design, but may also open fruitful perspectives in cognitive neuroscience research. In this context, we sought to determine whether we can extract discriminatory error-related activity in the source space, online, and on a trial by trial basis from electroencephalography data recorded during motor imagery. Using a data driven approach, based on interpretable inverse solution algorithms, we assessed the extent to which automatically extracted error-related activity was physiologically and functionally interpretable according to performance monitoring literature. The applicability of inverse solution based methods for automatically extracting error signals, in the presence of noise generated by motor imagery, was validated by simulation. Representative regions of interest, outlining the primary generators contributing to classification, were found to correspond closely to networks involved in error detection and performance monitoring. We observed discriminative activity in non-frontal areas, demonstrating that areas outside of the medial frontal cortex can contribute to the classification of error feedback activity

Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers

We present a new approach for online handwritten signature classification and verification based on descriptors stemming from Information Theory. The proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher Information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results produced surpass state-of-the-art techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups.Comment: Submitted to PLOS On

Toward a social signaling framework : activity and emphasis in speech

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 67-70).Language is not the only form of verbal communication. Loudness, pitch, speaking rate, and other non-linguistic speech features are crucial aspects of human spoken interaction. In this thesis, we separate these speech features into two categories -- vocal Activity and vocal Emphasis -- and propose a framework for classifying high-level social behavior according to those metrics. We present experiments showing that non-linguistic speech analysis alone can account for appreciable portions of social phenomena. We report statistically significant results in measuring the persuasiveness of pitches, the effectiveness of customer service representatives, and the severity of depression. Effect sizes of these studies explain up to 60% of the sample variances and yield binary decision accuracies nearing 90%.by William T. Stoltzman.M.Eng

Semantic and emotional content of imagined representations in human occipitotemporal cortex.

Mental imagery is a critical cognitive function, clinically important, but poorly understood. When visual objects are perceived, many of their sensory, semantic and emotional properties are represented in occipitotemporal cortex. Visual imagery has been found to activate some of the same brain regions, but it was not known what properties are re-created in these regions during imagery. We therefore examined the representation during imagery for two stimuli in depth, by comparing the pattern of fMRI response to the patterns evoked by the perception of 200 diverse objects chosen to de-correlate their properties. Real-time, adaptive stimulus selection allowed efficient sampling of this broad stimulus space. Our experiments show that occipitotemporal cortex, which encoded sensory, semantic and emotional properties during perception, can robustly represent semantic and emotional properties during imagery, but that these representations depend on the object being imagined and on individual differences in style and reported vividness of imagery

Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review

A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance

Projective simulation for artificial intelligence

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.Comment: 22 pages, 18 figures. Close to published version, with footnotes retaine

An Approach to Software Development for Continuous Authentication of Smart Wearable Device Users

abstract: With the recent expansion in the use of wearable technology, a large number of users access personal data with these smart devices. The consumer market of wearables includes smartwatches, health and fitness bands, and gesture control armbands. These smart devices enable users to communicate with each other, control other devices, relax and work out more effectively. As part of their functionality, these devices store, transmit, and/or process sensitive user personal data, perhaps biological and location data, making them an abundant source of confidential user information. Thus, prevention of unauthorized access to wearables is necessary. In fact, it is important to effectively authenticate users to prevent intentional misuse or alteration of individual data. Current authentication methods for the legitimate users of smart wearable devices utilize passcodes, and graphical pattern based locks. These methods have the following problems: (1) passcodes can be stolen or copied, (2) they depend on conscious user inputs, which can be undesirable to a user, (3) they authenticate the user only at the beginning of the usage session, and (4) they do not consider user behavior or they do not adapt to evolving user behavior. In this thesis, an approach is presented for developing software for continuous authentication of the legitimate user of a smart wearable device. With this approach, the legitimate user of a smart wearable device can be authenticated based on the user's behavioral biometrics in the form of motion gestures extracted from the embedded sensors of the smart wearable device. The continuous authentication of this approach is accomplished by adapting the authentication to user's gesture pattern changes. This approach is demonstrated by using two comprehensive datasets generated by two research groups, and it is shown that this approach achieves better performance than existing methods.Dissertation/ThesisMasters Thesis Software Engineering 201

A Survey on Emotion Recognition for Human Robot Interaction

With the recent developments of technology and the advances in artificial intelligent and machine learning techniques, it becomes possible for the robot to acquire and show the emotions as a part of Human-Robot Interaction (HRI). An emotional robot can recognize the emotional states of humans so that it will be able to interact more naturally with its human counterpart in different environments. In this article, a survey on emotion recognition for HRI systems has been presented. The survey aims to achieve two objectives. Firstly, it aims to discuss the main challenges that face researchers when building emotional HRI systems. Secondly, it seeks to identify sensing channels that can be used to detect emotions and provides a literature review about recent researches published within each channel, along with the used methodologies and achieved results. Finally, some of the existing emotion recognition issues and recommendations for future works have been outlined

Authorship attribution: Performance of various features and classification methods

Authorship attribution is the process of determining the writer of a document. In literature, there are lots of classification techniques conducted in this process. In this paper we explore information retrieval methods such as tf-idf structure with support vector machines, parametric and nonparametric methods with supervised and unsupervised (clustering) classification techniques in authorship attribution. We performed various experiments with articles gathered from Turkish newspaper Milliyet. We performed experiments on different features extracted from these texts with different classifiers, and combined these results to improve our success rates. We identified which classifiers give satisfactory results on which feature sets. According to experiments, the success rates dramatically changes with different combinations, however the best among them are support vector classifier with bag of words, and Gaussian with function words. ©2007 IEEE