Optimising the number of channels in EEG-augmented image search

Recent proof-of-concept research has appeared showing the applicability of Brain Computer Interface (BCI) technology in combination with the human visual system, to classify images. The basic premise here is that images that arouse a participant’s attention generate a detectable response in their brainwaves, measurable using an electroencephalograph (EEG). When a participant is given a target class of images to search for, each image belonging to that target class presented within a stream of images should elicit a distinctly detectable neural response. Previous work in this domain has primarily focused on validating the technique on proof of concept image sets that demonstrate desired properties and on examining the capabilities of the technique at various image presentation speeds. In this paper we expand on this by examining the capability of the technique when using a reduced number of channels in the EEG, and its impact on the detection accuracy

A review of rapid serial visual presentation-based brain-computer interfaces

International audienceRapid serial visual presentation (RSVP) combined with the detection of event related brain responses facilitates the selection of relevant information contained in a stream of images presented rapidly to a human. Event related potentials (ERPs) measured non-invasively with electroencephalography (EEG) can be associated with infrequent targets amongst a stream of images. Human-machine symbiosis may be augmented by enabling human interaction with a computer, without overt movement, and/or enable optimization of image/information sorting processes involving humans. Features of the human visual system impact on the success of the RSVP paradigm, but pre-attentive processing supports the identification of target information post presentation of the information by assessing the co-occurrence or time-locked EEG potentials. This paper presents a comprehensive review and evaluation of the limited but significant literature on research in RSVP-based brain-computer interfaces (BCIs). Applications that use RSVP-based BCIs are categorized based on display mode and protocol design, whilst a range of factors influencing ERP evocation and detection are analyzed. Guidelines for using the RSVP-based BCI paradigms are recommended, with a view to further standardizing methods and enhancing the inter-relatability of experimental design to support future research and the use of RSVP-based BCIs in practice

Spatio-Temporal Approaches to Denoising and Feature Extraction in Rapid Image Triage

Ph.DDOCTOR OF PHILOSOPH

Machine Learning Methods for functional Near Infrared Spectroscopy

Identification of user state is of interest in a wide range of disciplines that fall under the umbrella of human machine interaction. Functional Near Infra-Red Spectroscopy (fNIRS) device is a relatively new device that enables inference of brain activity through non-invasively pulsing infra-red light into the brain. The fNIRS device is particularly useful as it has a better spatial resolution than the Electroencephalograph (EEG) device that is most commonly used in Human Computer Interaction studies under ecologically valid settings. But this key advantage of fNIRS device is underutilized in current literature in the fNIRS domain. We propose machine learning methods that capture this spatial nature of the human brain activity using a novel preprocessing method that uses `Region of Interest\u27 based feature extraction. Experiments show that this method outperforms the F1 score achieved previously in classifying `low\u27 vs `high\u27 valence state of a user. We further our analysis by applying a Convolutional Neural Network (CNN) to the fNIRS data, thus preserving the spatial structure of the data and treating the data similar to a series of images to be classified. Going further, we use a combination of CNN and Long Short-Term Memory (LSTM) to capture the spatial and temporal behavior of the fNIRS data, thus treating it similar to a video classification problem. We show that this method improves upon the accuracy previously obtained by valence classification methods using EEG or fNIRS devices. Finally, we apply the above model to a problem in classifying combined task-load and performance in an across-subject, across-task scenario of a Human Machine Teaming environment in order to achieve optimal productivity of the system

A hybrid generative/discriminative method for EEG evoked potential detection

I. INTRODUCTION Generative and discriminative learning approaches are two prevailing and powerful, yet different, paradigms in machine leaning. Generative learning models, such as Bayesian inference [1] attempt to model the underlying distributions of the variables in order to compute classification and regression functions. These methods provide a rich framework for learning from prior knowledge. Discriminative learning models, such as support vector machines (SVM) [2] avoid generative modeling by directly optimizing a mapping from the inputs to the desired outputs by adjusting the resulting classification boundary. These latter methods commonly demonstrate superior performance in classification. Recently, researchers have investigated the relationship between these two learning paradigms and have attempted to combine their complementary strength

The predictive value of neurobiological measures for recidivism in delinquent male young adults

Background: Neurobiological measures have been associated with delinquent behaviour, but little is known about the predictive power of these measures for criminal recidivism and whether they have incremental value over and above demographic and behavioural measures. This study examined whether selected measures of autonomic functioning, functional neuroimaging and electroencephalography predict overall and serious recidivism in a sample of 127 delinquent young adults. Methods: We assessed demographics; education and intelligence; previous delinquency and drug use; behavioural traits, including aggression and psychopathy; and neurobiological measures, including heart rate, heart rate variability, functional brain activity during an inhibition task and 2 electroencephalographic measures of error-processing. We tested longitudinal associations with recidivism using Cox proportional hazard models and predictive power using C-indexes. Results: Past offences, long-term cannabis use and reactive aggression were strongly associated with recidivism, as were resting heart rate and error-processing. In the predictive model, demographics, past delinquency, drug use and behavioural traits had moderate predictive power for overall and for serious recidivism (C-index over 30 months [fraction of pairs in the data, where the higher observed survival time was correctly predicted]: C30 = 0.68 and 0.75, respectively). Neurobiological measures significantly improved predictive power (C30 = 0.72 for overall recidivism and C30 = 0.80 for serious recidivism). Limitations: Findings cannot be generalized to females, and follow-up was limited to 4 years. Conclusion: Demographic and behavioural characteristics longitudinally predicted recidivism in delinquent male young adults, and neurobiological measures improved the models. This led to good predictive function, particularly for serious recidivism. Importantly, the most feasible measures (autonomic functioning and electroencephalography) proved to be useful neurobiological predictors.</p

Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfaces

The N2pc is a lateralised Event-Related Potential (ERP) that signals a shift of attention towards the location of a potential object of interest. We propose a single-trial target-localisation collaborative Brain-Computer Interface (cBCI) that exploits this ERP to automatically approximate the horizontal position of targets in aerial images. Images were presented by means of the rapid serial visual presentation technique at rates of 5, 6 and 10 Hz. We created three different cBCIs and tested a participant selection method in which groups are formed according to the similarity of participants’ performance. The N2pc that is elicited in our experiments contains information about the position of the target along the horizontal axis. Moreover, combining information from multiple participants provides absolute median improvements in the area under the receiver operating characteristic curve of up to 21% (for groups of size 3) with respect to single-user BCIs. These improvements are bigger when groups are formed by participants with similar individual performance, and much of this effect can be explained using simple theoretical models. Our results suggest that BCIs for automated triaging can be improved by integrating two classification systems: one devoted to target detection and another to detect the attentional shifts associated with lateral targets