Collaborative Brain-Computer Interfaces in Rapid Image Presentation and Motion Pictures

The last few years have seen an increase in brain-computer interface (BCI) research for the able-bodied population. One of these new branches involves collaborative BCIs (cBCIs), in which information from several users is combined to improve the performance of a BCI system. This thesis is focused on cBCIs with the aim of increasing understanding of how they can be used to improve performance of single-user BCIs based on event-related potentials (ERPs). The objectives are: (1) to study and compare different methods of creating groups using exclusively electroencephalography (EEG) signals, (2) to develop a theoretical model to establish where the highest gains may be expected from creating groups, and (3) to analyse the information that can be extracted by merging signals from multiple users. For this, two scenarios involving real-world stimuli (images presented at high rates and movies) were studied. The first scenario consisted of a visual search task in which images were presented at high frequencies. Three modes of combining EEG recordings from different users were tested to improve the detection of different ERPs, namely the P300 (associated with the presence of events of interest) and the N2pc (associated with shifts of attention). We showed that the detection and localisation of targets can improve significantly when information from multiple viewers is combined. In the second scenario, feature movies were introduced to study variations in ERPs in response to cuts through cBCI techniques. A distinct, previously unreported, ERP appears in relation to such cuts, the amplitude of which is not modulated by visual effects such as the low-level properties of the frames surrounding the discontinuity. However, significant variations that depended on the movie were found. We hypothesise that these techniques can be used to build on the attentional theory of cinematic continuity by providing an extra source of information: the brain

Brain-Computer Interfaces for Detection and Localization of Targets in Aerial Images

Objective. The N2pc event-related potential (ERP) appears on the opposite side of the scalp with respect to the visual hemisphere where an object of interest is located. We explored the feasibility of using it to extract information on the spatial location of targets in aerial images shown by means of a rapid serial visual presentation (RSVP) protocol using single-Trial classification. Methods. Images were shown to 11 participants at a presentation rate of 5 Hz while recording electroencephalographic signals. With the resulting ERPs, we trained linear classifiers for single-Trial detection of target presence and location. We analyzed the classifiers' decisions and their raw output scores on independent test sets as well as the averages and voltage distributions of the ERPs. Results. The N2pc is elicited in RSVP presentation of complex images and can be recognized in single trials (the median area under the receiver operating characteristic curve was 0.76 for left versus right classification). Moreover, the peak amplitude of this ERP correlates with the horizontal position of the target within an image. The N2pc varies significantly depending on handedness, and these differences can be used for discriminating participants in terms of their preferred hand. Conclusion and Significance. The N2pc is elicited during RSVP presentation of real complex images and contains analogue information that can be used to roughly infer the horizontal position of targets. Furthermore, differences in the N2pc due to handedness should be taken into account when creating collaborative brain-computer interfaces

Past and Future of Multi-Mind Brain-Computer Interfaces

The great improvements in brain–computer interface (BCI) performance that are brought upon by merging brain activity from multiple users have made this a popular strategy that allows even for human augmentation. These multi-mind BCIs have contributed in changing the role of BCIs from assistive technologies for people with disabilities into tools for human enhancement. This chapter reviews the history of multi-mind BCIs that have their root in the hyperscanning technique; the collaborative and competitive approaches; and the different ways that exist to integrate the brain signals from multiple people and optimally form groups to maximize performance. The main applications of multi-mind BCIs, including control of external devices, entertainment, and decision making, are also surveyed and discussed, in order to help the reader understand what are the most promising avenues and find the gaps that are worthy of future exploration. The chapter also provides a step-by-step tutorial to the design and implementation of a multi-mind BCI, with theoretical guidelines and a sample application

Hand-movement Prediction from EMG with LSTM-Recurrent Neural Networks

In this paper we present an approach based on shallow recurrent long short-term memory neural networks for the prediction of hand kinematics for hand-prosthesis control from data acquired via high-density surface electromyography (HD-sEMG). We used 134-channel HD-sEMG recordings from seven participants while performing multiple repetitions of 13 hand movements. A CyberGlove II was used to simultaneously record 18 degrees of freedom (joint angles) used as ground truth for predicting the hand movements. Traditional features were calculated over 100 ms windows and fed to the network. Specifically we used: Mean Absolute Value (MAV), variance, and number of zero-crossings. Our results indicate that: (a) a small number of channels is sufficient to make accurate predictions, (b) many features are redundant, and MAV is sufficient for the job, (c) the simple neural network architecture we propose is effective in this task. These findings have important implications in terms of computational efficiency and memory storage, which are important considerations in relation to implementability in the typically very low-power and low-resources computers onboard of hand prostheses

Grokking-like effects in counterfactual inference

We show that a typical neural network, which ignores any covariate/feature re-balancing, can be as effective as any explicit counterfactual method. We adopt the architecture of TARNet—a simple neural network with two heads (one for treatment, one for control) which is trained with a relatively high batch size. Combined with ensemble methods, this produces competitive results in four counterfactual inference benchmarks: IHDP, NEWS, JOBS, and TWINS. Our results indicate that relatively simple methods might be good enough for counterfactual prediction, with quality constraints coming from hyperparameter tuning. Our analysis indicates that the reason behind the observed phenomenon might be “grokking”, a recently developed theory

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

Parasocial relationships on YouTube reduce prejudice towards mental health issues

Intergroup contact has long been established as a way to reduce prejudice among society, but in-person interventions can be resource intensive and limited in reach. Parasocial relationships (PSRs) might navigate these problems by reaching large audiences with minimal resources and have been shown to help reduce prejudice in an extended version of contact theory. However, previous studies have shown inconsistent success. We assessed whether parasocial interventions reduce prejudice towards people with mental health issues by first creating a new PSR with a YouTube creator disclosing their experiences with borderline personality disorder. Our intervention successfully reduced explicit prejudice and intergroup anxiety. We corroborated these effects through causal analyses, where lower prejudice levels were mediated by the strength of parasocial bond. Preliminary findings suggest that this lower prejudice is sustained over time. Our results support the parasocial contact hypothesis and provide an organic method to passively reduce prejudice on a large scale

People perceive parasocial relationships to be effective at fulfilling emotional needs

People regularly form one-sided, “parasocial” relationships (PSRs) with targets incapable of returning the sentiment. Past work has shown that people engage with PSRs to support complex psychological needs (e.g., feeling less lonely after watching a favorite movie). However, we do not know how people rate these relationships relative to traditional two-sided relationships in terms of their effectiveness in supporting psychological needs. The current research (Ntotal = 3085) examined how PSRs help people fulfil emotion regulation needs. In Studies 1 and 2, participants felt that both their YouTube creator and non-YouTube creator PSRs were more effective at fulfilling their emotional needs than in-person acquaintances, albeit less effective than close others. In Study 3, people with high self-esteem thought PSRs would be responsive to their needs when their sociometer was activated, just as they do with two-sided relationships

SEEDS, simultaneous recordings of high-density EMG and finger joint angles during multiple hand movements

We present the SurfacE Electromyographic with hanD kinematicS (SEEDS) database. It contains electromyographic (EMG) signals and hand kinematics recorded from the forearm muscles of 25 non-disabled subjects while performing 13 different movements at normal and slow-paced speeds. EMG signals were recorded with a high-density 126-channel array centered on the extrinsic flexors of the fingers and 8 further electrodes placed on the extrinsic extensor muscles. A data-glove was used to record 18 angles from the joints of the wrist and fingers. The correct synchronisation of the data-glove and the EMG was ascertained and the resulting data were further validated by implementing a simple classification of the movements. These data can be used to test experimental hypotheses regarding EMG and hand kinematics. Our database allows for the extraction of the neural drive as well as performing electrode selection from the high-density EMG signals. Moreover, the hand kinematic signals allow the development of proportional methods of control of the hand in addition to the more traditional movement classification approaches