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A neurofeedback system to promote learner engagement (2016)

Lockwood, James; Bergin, Susan

A neurofeedback system to promote learner engagement (2016)

Lockwood, James; Bergin, Susan

Abstract:

This report describes a series of experiments that track novice programmer's engagement during two attention based tasks. The tasks required participants to watch a tutorial video on introductory programming and to attend to a simple maze game whilst wearing an electroencephalogram (EEG)device called the Emotiv EPOC. The EPOC's proprietary software includes a system which tracks emotional state (specifically: engagement, excitement, meditation, frustration, valence and long-term excitement). Using this data, a software application written in the Processing language was developed to track user's engagement levels and implement a neurofeedback based intervention when engagement fell below an acceptable level. The aim of the intervention was to prompt learners who disengaged with the task to re-engage. The intervention used during the video tutorial was to pause the video if a participant disengaged significantly. However other interventions such as slowing the video dow...

http://mural.maynoothuniversity.ie/8234/

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An analysis of EEG signals present during target search (2012)

Healy, Graham

An analysis of EEG signals present during target search (2012)

Healy, Graham

Abstract:

Recent proof-of-concept research has appeared highlighting the applicability of using Brain Computer Interface (BCI) technology to utilise a subjects visual system to classify images. This technique involves classifying a users EEG (Electroencephalography) signals as they view images presented on a screen. The premise is that images (targets) that arouse a subjects attention generate distinct brain responses, and these brain responses can then be used to label the images. Research thus far in this domain has focused on examining the tasks and paradigms that can be used to elicit these neurologically informative signals from images, and the correlates of human perception that modulate them. While success has been shown in detecting these responses in high speed presentation paradigms, there is still an open question as to what search tasks can ultimately benefit from using an EEG based BCI system. In this thesis we explore: (1) the neural signals present during visual search tasks th...

http://doras.dcu.ie/16778/

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An exploration of EEG features during recovery following stroke – implications for BCI-mediated neurorehabilitation therapy (2014)

Leamy, Darren J.; Kocijan, Jus; Domijan, Katarina; Duffin, Joseph; Roche, Richard; Comm...

An exploration of EEG features during recovery following stroke – implications for BCI-mediated neurorehabilitation therapy (2014)

Leamy, Darren J.; Kocijan, Jus; Domijan, Katarina; Duffin, Joseph; Roche, Richard; Commins, Sean; Collins, Ronan; Ward, Tomas E.

Abstract:

Background: Brain-Computer Interfaces (BCI) can potentially be used to aid in the recovery of lost motor control in a limb following stroke. BCIs are typically used by subjects with no damage to the brain therefore relatively little is known about the technical requirements for the design of a rehabilitative BCI for stroke. Methods: 32-channel electroencephalogram (EEG) was recorded during a finger-tapping task from 10 healthy subjects for one session and 5 stroke patients for two sessions approximately 6 months apart. An off-line BCI design based on Filter Bank Common Spatial Patterns (FBCSP) was implemented to test and compare the efficacy and accuracy of training a rehabilitative BCI with both stroke-affected and healthy data. Results: Stroke-affected EEG datasets have lower 10-fold cross validation results than healthy EEG datasets. When training a BCI with healthy EEG, average classification accuracy of stroke-affected EEG is lower than the average for healthy EEG. Classificati...

http://mural.maynoothuniversity.ie/6074/

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Behavioural and electrophysiological eVects of visual paired associate context manipulations during encoding and recognition in younger adults, older adults and older cognitively declined adults (2012)

Hogan, Michael; Kenney, Joanne P.M.; Roche, Richard; Keane, Michael A.; Moore, Jennifer...

Hogan, Michael; Kenney, Joanne P.M.; Roche, Richard; Keane, Michael A.; Moore, Jennifer L.; Kaiser, Jochen; Lai, Robert; Upton, Neil

Abstract:

The current study examined the EEG of young, old and old declined adults performing a visual paired associate task. In order to examine the eVects of encoding context and stimulus repetition, target pairs were presented on either detailed or white backgrounds and were repeatedly presented during both early and late phases of encoding. Results indicated an increase in P300 amplitude in the right parietal cortex from early to late stages of encoding in older declined adults, whereas both younger adults and older controls showed a reduction in P300 amplitude in this same area from early to late phase encoding. In the right hemisphere, stimuli encoded with a white background had larger P300 amplitudes than stimuli presented with a detailed background; however, in the left hemisphere, in the later stages of encoding, stimuli presented with a detailed background had larger amplitudes than stimuli presented with a white background. Behaviourally, there was better memory for congruent stimu...

http://mural.maynoothuniversity.ie/6795/

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Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task (2011)

Leamy, Darren J.; Collins, Ronan; Ward, Tomas E.

Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task (2011)

Leamy, Darren J.; Collins, Ronan; Ward, Tomas E.

Abstract:

This work serves as an initial investigation into improvements to classification accuracy of an imagined movement-based Brain Computer Interface (BCI) by combining the feature spaces of two unique measurement modalities: functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG). Our dual-modality system recorded concurrent and co-locational hemodynamic and electrical responses in the motor cortex during an imagined movement task, participated in by two subjects. Offline analysis and classification of fNIRS and EEG data was performed using leave-one-out cross-validation (LOOCV) and linear discriminant analysis (LDA). Classification of 2-dimensional fNIRS and EEG feature spaces was performed separately and then their feature spaces were combined for further classification. Results of our investigation indicate that by combining feature spaces, modest gains in classification accuracy of an imagined movement-based BCI can be achieved by employing a supplemental meas...

http://mural.maynoothuniversity.ie/4365/

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Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task (2011)

Leamy, Darren J.; Collins, Ronan; Ward, Tomas E.

Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task (2011)

Leamy, Darren J.; Collins, Ronan; Ward, Tomas E.

Abstract:

Included in Presentation

http://mural.maynoothuniversity.ie/4182/

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Eye fixation related potentials in a target search task (2011)

Healy, Graham; Smeaton, Alan F.

Eye fixation related potentials in a target search task (2011)

Healy, Graham; Smeaton, Alan F.

Abstract:

Typically BCI (Brain Computer Interfaces) are found in rehabilitative or restorative applications, often allowing users a medium of communication that is otherwise unavailable through conventional means. Recently, however, there is growing interest in using BCI to assist users in searching for images. A class of neural signals often leveraged in common BCI paradigms are ERPs (Event Related Potentials), which are present in the EEG (Electroencephalograph) signals from users in response to various sensory events. One such ERP is the P300, and is typically elicited in an oddball experiment where a subject’s attention is orientated towards a deviant stimulus among a stream of presented images. It has been shown that these types of neural responses can be used to drive an image search or labeling task, where we can rank images by examining the presence of such ERP signals in response to the display of images. To date, systems like these have been demonstrated when presenting sequences of...

http://doras.dcu.ie/16386/

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Informed perspectives on human annotation using neural signals (2016)

Healy, Graham; Gurrin, Cathal; Smeaton, Alan F.

Informed perspectives on human annotation using neural signals (2016)

Healy, Graham; Gurrin, Cathal; Smeaton, Alan F.

Abstract:

In this work we explore how neurophysiological correlates related to attention and perception can be used to better understand the image-annotation task. We explore the nature of the highly variable labelling data often seen across annotators. Our results indicate potential issues with regard to ‘how well’ a person manually annotates images and variability across annotators. We propose such issues arise in part as a result of subjectively interpretable instructions that may fail to elicit similar labelling behaviours and decision thresholds across participants. We find instances where an individual’s annotations differ from a group consensus, even though their EEG (Electroencephalography) signals indicate in fact they were likely in consensus with the group. We offer a new perspective on how EEG can be incorporated in an annotation task to reveal information not readily captured using manual annotations alone. As crowd-sourcing resources become more readily available for annotation ...

http://doras.dcu.ie/21017/

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Neural and cognitive correlates of human decision-making in domestic energy usage (2014)

Keane, Michael; Smeaton, Alan F.; Boran, Lorraine; Healy, Graham; Kennedy, Miriam; Yang...

Neural and cognitive correlates of human decision-making in domestic energy usage (2014)

Keane, Michael; Smeaton, Alan F.; Boran, Lorraine; Healy, Graham; Kennedy, Miriam; Yang, Yang; Gurrin, Cathal

Abstract:

Decision-making is a central component of every facet of human life, and is generally understood to be either conscious (deliberate) or automatic (non-deliberate). There has been little research to date on decision-making in the context of domestic energy consumption. Our study elucidated the human processes related to decisions around domestic energy use. In particular, the study investigated the neural and cognitive triggers of decision-making which differentiate between optimal and non-optimal energy consumers. Using EEG (electroencephalography) to assess brain function, we investigated brain activity associated with decisions around energy consumption and in this paper we report results from a study of 30 participants for whom we recorded their neural activity as they made decisions. As well as this, behavioural data related to cognitive processes involved were recorded. By examining this data, we aim to clarify some of the reasons why people make certain decisions about domesti...

http://doras.dcu.ie/19904/

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Optimising the number of channels in EEG-augmented image search (2011)

Healy, Graham; Smeaton, Alan F.

Optimising the number of channels in EEG-augmented image search (2011)

Healy, Graham; Smeaton, Alan F.

Abstract:

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.

http://doras.dcu.ie/16387/

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Seizure characterisation using frequency-dependent multivariate dynamics (2009)

Conlon, Thomas; Ruskin, Heather J.; Crane, Martin

Seizure characterisation using frequency-dependent multivariate dynamics (2009)

Conlon, Thomas; Ruskin, Heather J.; Crane, Martin

Abstract:

The characterisation of epileptic seizures assists in the design of targeted pharmaceutical seizure prevention techniques and pre-surgical evaluations. In this paper, we expand on recent use of multivariate techniques to study the crosscorrelation dynamics between electroencephalographic (EEG) channels. The Maximum Overlap Discrete Wavelet Transform (MODWT) is applied in order to separate the EEG channels into their underlying frequencies. The dynamics of the cross-correlation matrix between channels, at each frequency, are then analysed in terms of the eigenspectrum. By examination of the eigenspectrum, we show that it is possible to identify frequency dependent changes in the correlation structure between channels which may be indicative of seizure activity. The technique is applied to EEG epileptiform data and the results indicate that the correlation dynamics vary over time and frequency, with larger correlations between channels at high frequencies. Additionally, a redistributi...

http://doras.dcu.ie/14854/

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Single-trial detection in EEG and MEG: Keeping it linear (2003)

Parra, Lucas C.; Alvino, Chris; Tang, Akaysha; Pearlmutter, Barak A.; Yeung, Nick; Osma...

Single-trial detection in EEG and MEG: Keeping it linear (2003)

Parra, Lucas C.; Alvino, Chris; Tang, Akaysha; Pearlmutter, Barak A.; Yeung, Nick; Osman, Allen; Sajda, Paul

Abstract:

Conventional electroencephalography (EEG) and magnetoencephalography (MEG) analysis often rely on averaging over multiple trials to extract statistically relevant di7erences between two or more experimental conditions. We demonstrate that by linearly integrating information over multiple spatially distributed sensors within a prede9ned time window, one can discriminate conditions on a trial-by-trial basis with high accuracy. We restrict ourselves to a linear integration as it allows the computation of a spatial distribution of the discriminating source activity. In the present set of experiments the resulting source activity distributions correspond to functional neuroanatomy consistent with the task (e.g. contralateral sensory-motor cortex and anterior cingulate).

http://mural.maynoothuniversity.ie/5538/