Search

Robotics research in helping children with autism has gained increased attention in recent years. Children with autism often struggle with neurodevelopmental disorders that affect their lives particularly in communication skills, social interaction and their repetitive stereotyped behavior. In this article, we highlight previous findings on human–robot interaction for children with autism. To date, it has been claimed that child–robot interaction is a beneficial approach in helping children with autism to improve their quality of life. Based on an extensive search of the literature, we identified three major research gaps: (1) diversity in research focus, (2) bias contribution in robotics research towards specific behavior impairments in autism and, (3) effectiveness of human–robot interaction after robot-based intervention program. Therefore, this review paper shall identify and thoroughly discuss published works that address the research gaps found in this areas. This article is therefore seen as a crucial step in bridging the gap between robotics research and children with autism. The results presented could be beneficial for researchers in determining future directions for robotics research in helping children with autism.

One of the fundamental challenges in brain-computer interfaces (BCIs) is to tune a brain signal decoder to reliably detect a user's intention. While information about the decoder can partially be transferred between subjects or sessions, optimal decoding performance can only be reached with novel data from the current session. Thus, it is preferable to learn from unlabeled data gained from the actual usage of the BCI application instead of conducting a calibration recording prior to BCI usage. We review such unsupervised machine learning methods for BCIs based on event-related potentials of the electroencephalogram. We present results of an online study with twelve healthy participants controlling a visual speller. Online performance is reported for three completely unsupervised learning methods: (1) learning from label proportions, (2) an expectation-maximization approach and (3) MIX, which combines the strengths of the two other methods. After a short ramp-up, we observed that the MIX method not only defeats its two unsupervised competitors but even performs on par with a state-of-the-art regularized linear discriminant analysis trained on the same number of data points and with full label access. With this online study, we deliver the best possible proof in BCI that an unsupervised decoding method can in practice render a supervised method unnecessary. This is possible despite skipping the calibration, without losing much performance and with the prospect of continuous improvement over a session. Thus, our findings pave the way for a transition from supervised to unsupervised learning methods in BCIs based on eventrelated potentials.

Objective To diagnose and lateralise temporal lobe epilepsy (TLE) by building a classification system that uses directed functional connectivity patterns estimated during EEG periods without visible pathological activity. Methods Resting-state high-density EEG recording data from 20 left TLE patients, 20 right TLE patients and 35 healthy controls was used. Epochs without interictal spikes were selected. The cortical source activity was obtained for 82 regions of interest and whole-brain directed functional connectivity was estimated in the theta, alpha and beta frequency bands. These connectivity values were then used to build a classification system based on two two-class Random Forests classifiers: TLE vs healthy controls and left vs right TLE. Feature selection and classifier training were done in a leave-one-out procedure to compute the mean classification accuracy. Results The diagnosis and lateralization classifiers achieved a high accuracy (90.7% and 90.0% respectively), sensitivity (95.0% and 90.0% respectively) and specificity (85.7% and 90.0% respectively). The most important features for diagnosis were the outflows from left and right medial temporal lobe, and for lateralization the right anterior cingulate cortex. The interaction between features was important to achieve correct classification. Significance This is the first study to automatically diagnose and lateralise TLE based on EEG. The high accuracy achieved demonstrates the potential of directed functional connectivity estimated from EEG periods without visible pathological activity for helping in the diagnosis and lateralization of TLE., Highlights • Both classifiers for TLE diagnosis and lateralization achieved high accuracy (90%). • Outflow from left and right hippocampus was the most important for diagnosis. • Outflow from the right ACC was the most important for lateralization. • The interaction between features is important for a correct classification.

In the past, the decoding quality of brain-computer interface (BCI) systems was often enhanced by independently improving either the machine learning algorithms or the BCI paradigms. We propose to take a novel perspective instead by optimizing the whole system, paradigm and decoder, jointly. To exemplify this holistic idea, we introduce learning from label proportions (LLP) as a new classification approach and prove its value for visual event-related potential (ERP) signals of the EEG. LLP utilizes the existence of subgroups with different label proportions in the data. This leads to a conceptually simple BCI system which combines previously unseen capabilities: (1) it does not require calibration and learns from unlabeled data, (2) under i.i.d. conditions, LLP is guaranteed to obtain the optimal decoder for online data, (3) under violation of stationarity assumptions, LLP can continuously adapt to the changing data, and (4) it can, in practice, replace a traditional supervised decoder when combined with an expectation-maximization algorithm.

Machine learning has become a core component in brain-computer interfaces (BCIs). Unfortunately, the use of machine learning typically requires the collection of subject specific labelled data. This process is time-consuming and not productive from a user's point of view, as during calibration the user has to follow given instructions and cannot make own decisions. Only after calibration, the user is able to use the BCI freely. In this chapter, we describe how the supervised calibration process can be circumvented by unsupervised learning in which the decoder is trained while the user is utilising the system. We discuss three variations. First, expectation-maximisation (EM)-based training, which works wells empirically but can sometimes be unstable. Learning from label proportions (LLP)-based training, which is guaranteed to converge to the optimal solution, but learns more slowly. Third, a hybrid approach combining the stability of LLP with the speed of learning of EM in a highly efficient and effective approach that can readily replace supervised decoders for event-related potential BCI.

Objective: Key issues in the epilepsy seizure prediction research are (1) the reproducibility of results (2) the inability to compare multiple approaches directly. To overcome these problems, the seizure prediction challenge was organized on Kaggle.com. It aimed at establishing benchmarks on a dataset with predefined train, validation, and test sets. Our main objective is to analyze the competition format, and to propose improvements, which would facilitate a better comparison of algorithms. The second objective is to present a novel deep learning approach to seizure prediction and compare it to other commonly used methods using patient centered metrics. Methods: We used the competition's datasets to illustrate the effects of data contamination. Having better data partitions, we compared three types of models in terms of different objectives. Results: We found that correct selection of test samples is crucial when evaluating the performance of seizure forecasting models. Moreover, we showed that models, which achieve state-of-the-art performance with respect to commonly used AUC, sensitivity, and specificity metrics, may not yet be suitable for practical usage because of low precision scores. Conclusion: Correlation between validation and test datasets used in the competition limited its scientific value. Significance: Our findings provide guidelines which allow for a more objective evaluation of seizure prediction models.

Objective. Brain-computer interfaces (BCI) based on event-related potentials (ERP) incorporate a decoder to classify recorded brain signals and subsequently select a control signal that drives a computer application. Standard supervised BCI decoders require a tedious calibration procedure prior to every session. Several unsupervised classification methods have been proposed that tune the decoder during actual use and as such omit this calibration. Each of these methods has its own strengths and weaknesses. Our aim is to improve overall accuracy of ERP-based BCIs without calibration. Approach. We consider two approaches for unsupervised classification of ERP signals. Learning from label proportions (LLP) was recently shown to be guaranteed to converge to a supervised decoder when enough data is available. In contrast, the formerly proposed expectation maximization (EM) based decoding for ERP-BCI does not have this guarantee. However, while this decoder has high variance due to random initialization of its parameters, it obtains a higher accuracy faster than LLP when the initialization is good. We introduce a method to optimally combine these two unsupervised decoding methods, letting one method's strengths compensate for the weaknesses of the other and vice versa. The new method is compared to the aforementioned methods in a resimulation of an experiment with a visual speller. Main Results. Analysis of the experimental results shows that the new method exceeds the performance of the previous unsupervised classification approaches in terms of ERP classification accuracy and symbol selection accuracy during the spelling experiment. Furthermore, the method shows less dependency on random initialization of model parameters and is consequently more reliable. Significance. Improving the accuracy and subsequent reliability of calibrationless BCIs makes these systems more appealing for frequent use.

Objective. State of the art brain-computer interface (BCI) research focuses on improving individual components such as the application or the decoder that converts the user's brain activity to control signals. In this study, we investigate the interaction between these components in the P300 speller, a BCI for communication. We introduce a synergistic approach in which the stimulus presentation sequence is modified to enhance the machine learning decoding. In this way we aim for an improved overall BCI performance. Approach. First, a new stimulus presentation paradigm is introduced which provides us flexibility in tuning the sequence of visual stimuli presented to the user. Next, an experimental setup in which this paradigm is compared to other paradigms uncovers the underlying mechanism of the interdependence between the application and the performance of the decoder. Main results. Extensive analysis of the experimental results reveals the changing requirements of the decoder concerning the data recorded during the spelling session. When few data is recorded, the balance in the number of target and non-target stimuli shown to the user is more important than the signal-to-noise rate (SNR) of the recorded response signals. Only when more data has been collected, the SNR becomes the dominant factor. Significance. For BCIs in general, knowing the dominant factor that affects the decoder performance and being able to respond to it is of utmost importance to improve system performance. For the P300 speller, the proposed tunable paradigm offers the possibility to tune the application to the decoder's needs at any time and, as such, fully exploit this application-decoder interaction.

Objective To diagnose and lateralise Temporal Lobe Epilepsy (TLE) by building a classification system that uses directed functional connectivity patterns estimated during EEG periods without visible pathological activity. Methods Resting-state high-density EEG recording data from 20 left TLE patients, 20 right TLE patients and 35 healthy controls was used. Epochs without interictal spikes were selected. The cortical source activity was obtained for 82 regions of interest and whole-brain directed functional connectivity was estimated in the theta, alpha and beta frequency bands. These connectivity values were then used to build a classification system based on two two-class Random Forests classifiers: TLE vs healthy controls and left vs right TLE. Feature selection and classifier training were done in a leave-one-out procedure to compute the mean classification accuracy. Results The diagnosis and lateralization classifiers achieved a high accuracy (90.7% and 90.0% respectively), sensitivity (95.0% and 90.0% respectively) and specificity (85.7% and 90.0% respectively). The most important features for diagnosis were the outflows from left and right medial temporal lobe, and for lateralization the right anterior cingulate cortex. The interaction between features was important to achieve correct classification. Conclusions This is the first study to automatically diagnose and lateralise TLE based on EEG. The high accuracy achieved demonstrates the potential of directed functional connectivity estimated from EEG periods without visible pathological activity for helping in the diagnosis and lateralization of TLE.

Objective: Using traditional approaches, a Brain-Computer Interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g.~by transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch and adapt over time. While such heuristics work well in practice, none of them can provide theoretical guarantees. Our objective is to modify an event-related potential (ERP) paradigm to work in unison with the machine learning decoder to achieve a reliable calibration-less decoding with a guarantee to recover the true class means. Method: We introduce learning from label proportions (LLP) to the BCI community as a new unsupervised, and easy-to-implement classification approach for ERP-based BCIs. The LLP estimates the mean target and non-target responses based on known proportions of these two classes in different groups of the data. We modified a visual ERP speller to meet the requirements of the LLP. For evaluation, we ran simulations on artificially created data sets and conducted an online BCI study with N=13 subjects performing a copy-spelling task. Results: Theoretical considerations show that LLP is guaranteed to minimize the loss function similarly to a corresponding supervised classifier. It performed well in simulations and in the online application, where 84.5% of characters were spelled correctly on average without prior calibration. Significance: The continuously adapting LLP classifier is the first unsupervised decoder for ERP BCIs guaranteed to find the true class means. This makes it an ideal solution to avoid a tedious calibration and to tackle non-stationarities in the data. Additionally, LLP works on complementary principles compared to existing unsupervised methods, allowing for their further enhancement when combined with LLP., Comment: The EEG data of 13 subjects is freely available online at: http://doi.org/10.5281/zenodo.192684