CES-533: Analysis of the Event-related Potentials induced by cuts in feature movies and evaluation of the possibility of using such ERPs for understanding the effects of cuts on viewers

In this paper, we analyse the Event-Related Potentials (ERPs) produced by cuts where the scenes before and after the cut are narratively related. In tests with 6 participants and 930 cuts from 5 Hollywood feature movies we found that cuts produce a large negative ERP with an onset 100 ms after a cut and a duration of 600 ms, distributed over a very large region of the scalp. The real-world nature of the stimuli makes it hard to characterise the effects of cuts on a trial-by-trial basis. However, we found that aggregating data across all electrodes and averaging the ERPs elicited by cuts across all participants (a technique we borrowed from collaborative brain-computer interfaces) produced more reliable information. In particular we were able to reveal a relationship between the length of shots and the amplitude of the corresponding ERP with longer scenes producing bigger amplitudes. We also found that amplitudes vary across and within movies, most likely as a consequence of movie directors and editors using different choices of cutting techniques. In the future, we will explore the possibility of turning these ?findings into a collaborative brain-computer interface for aiding test screening by evaluating whether specific cuts have their intended effect on viewers

CES-531: Collaborative Brain-Computer Interfaces for Target Detection and Localisation in Rapid Serial Visual Presentation

The rapid serial visual presentation protocol can be used to show images sequentially on the same spatial location at high presentation rates. We used this technique to present aerial images to participants looking for predefined targets (airplanes) at rates ranging from 5 to 12 Hz. We used linear support vector machines for the single-trial classification of event-related potentials from both individual users and pairs of users (in which case we averaged either their individual classifiers' analogue outputs before thresholding or their electroencephalographic signals associated to the same stimuli) with and without the selection of compatible pairs. We considered two tasks - the detection of targets and the identification of the visual hemifield in which targets appeared. While single users did well in both tasks, we found that pairs of participants with similar individual performance provided significant improvements. In particular, in the target-detection task we obtained median improvements in the area under the receiver operating characteristic curve (AUC) of up to 8.3% w.r.t. single-user BCIs, while in the hemifield classification task we ob- tained AUCs up to 7.7% higher than for single users. Furthermore, we found that this second system allows not just to say if a target is in on the left or the right of an image, but to also recover the target's approximate horizontal position

Crosstalk Reduction in Epimysial EMG Recordings from Transhumeral Amputees with Principal Component Analysis

Electromyographic (EMG) recordings of muscle activity using monopolar electrodes suffer from poor spatial resolution due to the crosstalk from neighbouring muscles. This effect has mainly been studied on surface EMG recordings. Here, we use Principal Component Analysis (PCA) to reduce the crosstalk in recordings from unipolar epimysial electrodes implanted in three transhumeral amputees. We show that the PCA-transformed signals have, on average, a better signal-to-noise ratio than the original unipolar recordings. Preliminary investigations show that this transformation is stable over long periods of time. If the latter is confirmed, our results show that the combination of PCA with unipolar electrodes allows for a higher number of muscles to be targeted in an implant (compared with bipolar electrodes), thus facilitating 1-to-1 proportional control of prosthetic hands

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

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

Multi-brain fusion and applications to intelligence analysis

In a rapid serial visual presentation (RSVP) images are shown at an extremely rapid pace. Yet, the images can still be parsed by the visual system to some extent. In fact, the detection of specic targets in a stream of pictures triggers a characteristic electroencephalography (EEG) response that can be recognized by a brain-computer interface (BCI) and exploited for automatic target detection. Research funded by DARPA's Neurotechnology for Intelligence Analysts program has achieved speed-ups in sifting through satellite images when adopting this approach. This paper extends the use of BCI technology from individual analysts to collaborative BCIs. We show that the integration of information in EEGs collected from multiple operators results in performance improvements compared to the single-operator case. © 2013 SPIE

A database of multi-channel intramuscular electromyogram signals during isometric hand muscles contractions

Hand movement is controlled by a large number of muscles acting on multiple joints in the hand and forearm. In a forearm amputee the control of a hand prosthesis is traditionally depending on electromyography from the remaining forearm muscles. Technical improvements have made it possible to safely and routinely implant electrodes inside the muscles and record high-quality signals from individual muscles. In this study, we present a database of intramuscular EMG signals recorded with fine-wire electrodes alongside recordings of hand forces in an isometric setup and with the addition of spike-sorted metadata. Six forearm muscles were recorded from twelve able-bodied subjects and nine forearm muscles from two subjects. The fully automated recording protocol, based on command cues, comprised a variety of hand movements, including some requiring slowly increasing/decreasing force. The recorded data can be used to develop and test algorithms for control of a prosthetic hand. Assessment of the signals was done in both quantitative and qualitative manners