A robust screen-free brain-computer interface for robotic object selection

Contains fulltext : 222466.pdf (publisher's version ) (Open Access)Brain signals represent a communication modality that can allow users of assistive robots to specify high-level goals, such as the object to fetch and deliver. In this paper, we consider a screen-free Brain-Computer Interface (BCI), where the robot highlights candidate objects in the environment using a laser pointer, and the user goal is decoded from the evoked responses in the electroencephalogram (EEG). Having the robot present stimuli in the environment allows for more direct commands than traditional BCIs that require the use of graphical user interfaces. Yet bypassing a screen entails less control over stimulus appearances. In realistic environments, this leads to heterogeneous brain responses for dissimilar objects-posing a challenge for reliable EEG classification. We model object instances as subclasses to train specialized classifiers in the Riemannian tangent space, each of which is regularized by incorporating data from other objects. In multiple experiments with a total of 19 healthy participants, we show that our approach not only increases classification performance but is also robust to both heterogeneous and homogeneous objects. While especially useful in the case of a screen-free BCI, our approach can naturally be applied to other experimental paradigms with potential subclass structure.14 p

Improving covariance matrices derived from tiny training datasets for the classification of event-related potentials with linear discriminant analysis

Electroencephalogram data used in the domain of brain-computer interfaces typically has subpar signal-to-noise ratio and data acquisition is expensive. An effective and commonly used classifier to discriminate event-related potentials is the linear discriminant analysis which, however, requires an estimate of the feature distribution. While this information is provided by the feature covariance matrix its large number of free parameters calls for regularization approaches like Ledoit-Wolf shrinkage. Assuming that the noise of event-related potential recordings is not time-locked, we propose to decouple the time component from the covariance matrix of event-related potential data in order to further improve the estimates of the covariance matrix for linear discriminant analysis. We compare three regularized variants thereof and a feature representation based on Riemannian geometry against our proposed novel linear discriminant analysis with time-decoupled covariance estimates. Extensive evaluations on 14 electroencephalogram datasets reveal, that the novel approach increases the classification performance by up to four percentage points for small training datasets, and gracefully converges to the performance of standard shrinkage-regularized LDA for large training datasets. Given these results, practitioners in this field should consider using our proposed time-decoupled covariance estimation when they apply linear discriminant analysis to classify event-related potentials, especially when few training data points are available

Brain-computer interface research: A state-of-the-art summary 9

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Brain-computer interface research: A state-of-the-art summary 9 [Introduction]

Brain-computer interface (BCI) systems can provide communication and control without any physical movement. The BCI Research Awards are annual events to select the best BCI projects that year. Groups from around the world submit projects that are scored by a jury of international experts that selects twelve nominees and three winners. We also produce books like this one that review that year’s nominees, awards ceremony, and winners. This introductory chapter briefly reviews BCIs and the 2019 awards process, including the jury, selection criteria, and nominees. We mention many chapters that might engage readers with different interests, including chapters with project descriptions or interviews with nominees. Many of the chapters here describe new approaches to BCIs that could be useful to patients and/or mainstream users. The final chapter of this book reviews the Awards Ceremony, announces the winners, and presents concluding comments

Embedding neurophysiological signals

Neurophysiological time-series recordings of brain activity like the electroencephalogram (EEG) or local field potentials can be decoded by machine learning models in order to either control an application, e.g., for communication or rehabilitation after stroke, or to passively monitor the ongoing brain state of the subject, e.g., in a demanding work environment. A typical decoding challenge faced by a brain-computer interface (BCI) is the small dataset size compared to other domains of machine learning like computer vision or natural language processing. The possibilities to tackle classification or regression problems in BCI are to either train a regular model on the available small training data sets or through transfer learning, which utilizes data from other sessions, subjects, or even datasets to train a model. Transfer learning is non-trivial because of the non-stationary of EEG signals between subjects but also within subjects. This variability calls for explicit calibration phases at the start of every session, before BCI applications can be used online. In this study, we present arguments to BCI researchers to encourage the use of embeddings for EEG decoding. In particular, we introduce a simple domain adaptation technique involving both deep learning (when learning the embeddings from the source data) and classical machine learning (for fast calibration on the target data). This technique allows us to learn embeddings across subjects, which deliver a generalized data representation. These can then be fed into subject-specific classifiers in order to minimize their need for calibration data. We conducted offline experiments on the 14 subjects of the High Gamma EEG-BCI Dataset [1]. Embedding functions were obtained by training EEGNet [2] using a leave-one-subject-out (LOSO) protocol, and the embedding vectors were classified by the logistic regression algorithm. Our pipeline was compared to two baseline approaches: EEGNet without subject-specific calibration and the standard FBCSP pipeline in a within-subject training. We observed that the representations learned by the embedding functions were indeed non-stationary across subjects, justifying the need for an additional subject-specific calibration. We also observed that the subject-specific calibration indeed improved the score. Finally, our data suggest, that building upon embeddings requires fewer individual calibration data than the FBCSP baseline to reach satisfactory scores

Unsupervised learning in a BCI chess application using label proportions and expectation-maximization

Item does not contain fulltextThe online usage of brain-computer interfaces (BCI) generates unlabeled data. This data in combination with the rich structure contained in BCI applications based on event-related potentials allow to design novel unsupervised classification approaches like learning from label proportions (LLP) or its combination with expectation-maximization (EM) into a mixed model. In this work, we explore the feasibility of unsupervised classification in a BCI chess application. We propose an LLP extension based on weighted least squares regression. It requires randomization of timing parameters but overcomes the dependency on additional symbols. Simulations on electroencephalogram data obtained from six subjects playing BCI-controlled chess show that a combination of unsupervised LLP with EM (despite not using any labels) by constant adaptation quickly reaches and on the long run outperforms the average performance level of non-adaptive supervised classifiers. With our contribution, we increase the scope for which unsupervised learning methods can successfully be applied in BCI.14 p

Recent advances in brain-computer interface research: A summary of the 2019 BCI Award and online BCI research activities

The introduction chapter of this book described the BCI Research Awards, selection criteria, nominees, and jury. Developing a good submission for a BCI Research Award is a formidable goal, and being nominated is even more demanding. This book has presented thirteen chapters by the authors of projects nominated for a BCI Research Award in 2019. Some of these chapters detailed the projects that were nominated, while other chapters comprised interviews with nominees. In this chapter, we review the 2019 BCI Research Awards Ceremony and present the winners. We also discuss emerging directions such as online BCI-related activities that have become much more prominent during 2020 due to COVID concerns

Identifying controllable cortical neural markers with machine learning for adaptive deep brain stimulation in Parkinson's disease

Contains fulltext : 222474.pdf (publisher's version ) (Open Access)The identification of oscillatory neural markers of Parkinson's disease (PD) can contribute not only to the understanding of functional mechanisms of the disorder, but may also serve in adaptive deep brain stimulation (DBS) systems. These systems seek online adaptation of stimulation parameters in closed-loop as a function of neural markers, aiming at improving treatment's efficacy and reducing side effects. Typically, the identification of PD neural markers is based on group-level studies. Due to the heterogeneity of symptoms across patients, however, such group-level neural markers, like the beta band power of the subthalamic nucleus, are not present in every patient or not informative about every patient's motor state. Instead, individual neural markers may be preferable for providing a personalized solution for the adaptation of stimulation parameters. Fortunately, data-driven bottom-up approaches based on machine learning may be utilized. These approaches have been developed and applied successfully in the field of brain-computer interfaces with the goal of providing individuals with means of communication and control. In our contribution, we present results obtained with a novel supervised data-driven identification of neural markers of hand motor performance based on a supervised machine learning model. Data of 16 experimental sessions obtained from seven PD patients undergoing DBS therapy show that the supervised patient-specific neural markers provide improved decoding accuracy of hand motor performance, compared to group-level neural markers reported in the literature. We observed that the individual markers are sensitive to DBS therapy and thus, may represent controllable variables in an adaptive DBS system.15 p