Circular and non-flat data distribution are prevalent across diverse domains of data science, yet their specific geometric structures often remain underutilized in machine learning frameworks. A principled approach to accounting for the underlying geometry of such data is pivotal, particularly when extending statistical models, like the pervasive Gaussian distribution. In this work, we tackle those issue by focusing on the manifold of symmetric positive definite matrices, a key focus in information geometry. We introduced a non-isotropic wrapped Gaussian by leveraging the exponential map, we derive theoretical properties of this distribution and propose a maximum likelihood framework for parameter estimation. Furthermore, we reinterpret established classifiers on SPD through a probabilistic lens and introduce new classifiers based on the wrapped Gaussian model. Experiments on synthetic and real-world datasets demonstrate the robustness and flexibility of this geometry-aware distribution, underscoring its potential to advance manifold-based data analysis. This work lays the groundwork for extending classical machine learning and statistical methods to more complex and structured data.

The development of Brain-Computer Interfaces (BCIs) has been constrained by a predominant focus on signal classification.This paper rather emphasizes the integration of neurophysiological principles, BCI paradigm selection, and rigorous experimental design.By addressing common pitfalls in Machine Learning implementation, we provide researchers with a tutorial and robust framework for BCI development, promoting reproducibility and rigor.Furthermore, by tackling challenges at the intersection of BCI and Machine Learning, this work contributes to the advancement of practical, real-time BCI applications.

In this paper, we propose and compare multiple criteria for selecting ElectroEncephaloGraphic (EEG) channels over the Riemannian manifold, for EEG classification in Brain- Computer Interfaces (BCI). These criteria aim to promote EEG covariance matrix classifiers to generalize well by considering EEG data non-stationarity. Our approach consists of both increasing the discriminative information between classes over the manifold and reducing the dispersion within classes. We also reduce the influence of outliers in both discriminative and dispersion measures. Using the proposed criteria, channel selection is done automatically in a backward elimination process. The criteria are evaluated on EEG signals recorded from a tetraplegic subject and dataset IVa from BCI competition III. Experimental evidences confirm that considering the dispersion within each class as a measure for quantifying the effects of non-stationarity and removing the most affected channels can improve the performance of BCI by 5% on the tetraplegic subject and by 12 % on dataset IVa.

Understanding the neurophysiological mechanisms underlying curiosity and therefore being able to identify the curiosity level of a person, would provide useful information for researchers and designers in numerous fields such as neuroscience, psychology, and computer science. A first step to uncovering the neural correlates of curiosity is to collect neurophysiological signals during states of curiosity, in order to develop signal processing and machine learning (ML) tools to recognize the curious states from the non-curious ones. Thus, we ran an experiment in which we used electroencephalography (EEG) to measure the brain activity of participants as they were induced into states of curiosity, using trivia question and answer chains. We used two ML algorithms, i.e. Filter Bank Common Spatial Pattern (FBCSP) coupled with a Linear Discriminant Algorithm (LDA), as well as a Filter Bank Tangent Space Classifier (FBTSC), to classify the curious EEG signals from the non-curious ones. Global results indicate that both algorithms obtained better performances in the 3-to-5s time windows, suggesting an optimal time window length of 4 seconds (63.09% classification accuracy for the FBTSC, 60.93% classification accuracy for the FBCSP+LDA) to go towards curiosity states estimation based on EEG signals.

Motor Imagery-based Brain-Computer Interfaces (MI-BCI) allow users to control a computer for various applications using their brain activity alone, which is usually recorded by an electroencephalogram (EEG). Although BCI applications are numerous, their use outside laboratories is still scarce due to their poor accuracy. Some users cannot use BCIs, a phenomenon sometimes called "BCI illiteracy", which impacts around 10% to 30% of BCI users, who cannot produce discriminable EEG patterns. By performing neurophysiological analyses, and notably by identifying neurophysiological predictors of BCI performance, we may understand this phenomenon and its causes better. In turn, this may also help us to better understand and thus possibly improve, BCI user training. Therefore, this paper presents statistical models dedicated to the prediction of MI-BCI user performance, based on neurophysiological users' features extracted from a two minute EEG recording of a "relax with eyes open" condition. We consider data from 56 subjects that were recorded in a `relax with eyes open' condition before performing a MI-BCI experiment. We used machine learning regression algorithm with leave-one-subject-out cross-validation to build our model of prediction. We also computed different correlations between those features (neurophysiological predictors) and users' MI-BCI performances. Our results suggest such models could predict user performances significantly better than chance (p ≤ 0.01) but with a relatively high mean absolute error of 12.43%. We also found significant correlations between a few of our features and the performance, including the previously explored μ-band predictor, as well as a new one proposed here: the μ-peak location variability. These results are thus encouraging to better understand and predict BCI illiteracy. However, they also require further improvements in order to obtain more reliable predictions.

EEG-based hyper-scanning refers to two or more subjects engaged in a task together or performing the same action together while neurophysiological signals are simultaneously recorded from them. This is one of the manners for investigating between-subject neural activities involved in social interactions. Emotion perception plays an important role in human social interactions. Interaction and emotional state influence each other. In this study, we aim to investigate how between-subject interaction modulates emotion perception based on event related potentials (ERPs), connectivity analysis and classification analysis. We found that there are distinct differences appearing between paired subjects who performed the task together, which are early ERP components (N250 and N400), late ERP components (P1500 and N1500), and the greater amplitude in N250 for the seconding responding subject compared to the first one. In the exploration of connectivity using phase locking value (PLV), we found that there are significant differences among different frequency bands for each subject under positive and negative stimuli and the significant difference of hyper-connectivity existed in the gamma frequency band between positive and negative stimulus trials. In the classification analysis, we compared the hyper-features for two individual subjects separately, the performance was improved when hyper-features of the PLV was employed compared to the features of power spectrum density.

Measuring brain activity with electroencephalography (EEG) is mature enough to assess mental states. Combined with existing methods, such tool can be used to strengthen the understanding of user experience. We contribute a set of methods to estimate continuously the user's mental workload, attention and recognition of interaction errors during different interaction tasks. We validate these measures on a controlled virtual environment and show how they can be used to compare different interaction techniques or devices, by comparing here a keyboard and a touch-based interface. Thanks to such a framework, EEG becomes a promising method to improve the overall usability of complex computer systems.

Although Mental Imagery based Brain-Computer Interfaces (MI-BCIs) seem to be very promising for many applications, they are still rarely used outside laboratories. This is partly due to suboptimal training protocols, which provide little help to users learning how to control the system. Indeed, they do not take into account recommendations from instructional design. However, it has been shown that MI-BCI performances are significantly correlated to certain aspects of the users' cognitive profile, such as their Spatial Abilities (SA). Thus, it remains to be elucidated whether training the SA of BCI users would also improve their BCI control performance. Therefore, we proposed and validated an SA training that aimed at being included in an MI-BCI training protocol. Our pre-studies indeed confirmed that such a training does increase people's SA abilities. We then conducted a pilot study with 3 participants, one with a standard MI-BCI training protocol, one with the proposed SA training integrated into a standard MI-BCI training, and another control integrating another training, here verbal comprehension tasks, into a standard MI-BCI training. While such a small population cannot lead to any strong result, our first results show that SA training can indeed be integrated into MI-BCI training and is thus worth being further investigated for BCI user training.

We propose a toolkit for creating Tangible Out-of-Body Experiences: exposing the inner states of users using physiological signals such as heart rate or brain activity. Tobe can take the form of a tangible avatar displaying live physiological readings to reflect on ourselves and others. Such a toolkit could be used by researchers and designers to create a multitude of potential tangible applications, including (but not limited to) educational tools about Science Technologies Engineering and Mathematics (STEM) and cognitive science, medical applications or entertainment and social experiences with one or several users or Tobes involved. Through a co-design approach, we investigated how everyday people picture their physiology and we validated the acceptability of Tobe in a scientific museum. We also give a practical example where two users relax together, with insights on how Tobe helped them to synchronize their signals and share a moment.

Despite their potential for many applications, Brain -- Computer Interfaces (BCI) are still rarely used due to their low reliability and long training. These limitations are partly due to inappropriate training protocols, which includes the feedback provided to the user. While feedback should theoretically be explanatory, motivating and meaningful, current BCI feedback is usually boring, corrective only and difficult to understand. In this study, different features of the electroencephalogram signals were explored to be used as a richer, explanatory BCI feedback. First, based on offline mental imagery BCI data, muscular relaxation was notably found to be negatively correlated to BCI performance. Second, this study reports on an online BCI evaluation using muscular relaxation as additional feedback. While this additional feedback did not lead to significant change in BCI performance, this study showed that multiple feedbacks can be used without deteriorating performance and provided interesting insights for explanatory BCI feedback design.

One of the major limitations of brain-computer interfaces (BCI) is their long calibration time, which limits their use in practice, both by patients and healthy users alike. Such long calibration times are due to the large between-user variability and thus to the need to collect numerous training electroencephalography (EEG) trials for the machine learning algorithms used in BCI design. In this paper, we first survey existing approaches to reduce or suppress calibration time, these approaches being notably based on regularization, user-to-user transfer, semi-supervised learning and a priori physiological information. We then propose new tools to reduce BCI calibration time. In particular, we propose to generate artificial EEG trials from the few EEG trials initially available, in order to augment the training set size. These artificial EEG trials are obtained by relevant combinations and distortions of the original trials available. We propose three different methods to do so. We also propose a new, fast and simple approach to perform user-to-user transfer for BCI. Finally, we study and compare offline different approaches, both old and new ones, on the data of 50 users from three different BCI data sets. This enables us to identify guidelines about how to reduce or suppress calibration time for BCI.

Brain-Computer Interfaces (BCI) aim at translating brain signals, typically ElectroEncephaloGraphy (EEG), into commands for external devices. Spatial filters are powerful tools for EEG classification, able to reduce spatial blurring effects. In particular, optimal spatial filters have been designed to classify EEG signals based on band power features. Unfortunately, there are other relevant EEG features for which no optimal spatial filter exists. This is the case for Phase Locking Value (PLV) features, which measure the synchronization between 2 EEG channels. Therefore, this paper proposes to create such a pair of optimal spatial filters for PLV-features. To do so, we optimized a functional measuring the discriminability of PLV-features based on a genetic algorithm. An evaluation of our algorithm on a motor imagery EEG data set showed that using optimized spatial filters led to higher classification performances, and that combining the resulting PLV features with traditional methods boosts the overall BCI performances.

We introduce Teegi, a Tangible ElectroEncephaloGraphy (EEG) Interface that enables novice users to get to know more about something as complex as brain signals, in an easy, engaging and informative way. To this end, we have designed a new system based on a unique combination of spatial augmented reality, tangible interaction and real-time neurotechnologies. With Teegi, a user can visualize and analyze his or her own brain activity in real-time, on a tangible character that can be easily manipulated, and with which it is possible to interact. An exploration study has shown that interacting with Teegi seems to be easy, motivating, reliable and informative. Overall, this suggests that Teegi is a promising and relevant training and mediation tool for the general public.

Imagine you are facing a mirror, seeing at the same time both your real body and a virtual display of your brain in activity and perfectly superimposed to your real image “inside your real skull”. In this paper, we introduce a novel augmented reality paradigm called “Mind-Mirror” which enables the experience of seeing “through your own head”, visualizing your brain “in action and in situ”. Our approach relies on the use of a semi-transparent mirror positioned in front of a computer screen. A virtual brain is displayed on screen and automatically follows the head movements using an optical face-tracking system. The brain activity is extracted and processed in real-time with the help of an electroencephalography cap (EEG) worn by the user. A rear view is also proposed thanks to an additional webcam recording the rear of the user's head. The use of EEG classification techniques enables to test a Neurofeedback scenario in which the user can train and progressively learn how to control different mental states, such as “concentrated” versus “relaxed”. The results of a user study comparing a standard visualization used in Neurofeedback to our approach showed that the Mind-Mirror could be successfully used and that the participants have particularly appreciated its innovation and originality. We believe that, in addition to applications in Neurofeedback and Brain-Computer Interfaces, the Mind-Mirror could also be used as a novel visualization tool for education, training or entertainment applications.

How can we connect two brains to a video game by means of a brain-computer interface (BCI), and what will happen when we do so? How will the two users behave, and how will they perceive this novel common experience? In this paper, we are concerned with the design and evaluation of multiuser BCI applications. We created a multiuser videogame called BrainArena in which two users can play a simple football game by means of two BCIs. They can score goals on the left or right side of the screen by simply imagining left or right hand movements. To add another interesting element, the gamers can play in a collaborative manner (their two mental activities are combined to score in the same goal), or in a competitive manner (the gamers must push the ball in opposite directions). Two experiments were conducted to evaluate the performance and subjective experience of users in the different conditions. In the first experiment, we compared a single-user situation with one multiuser situation: the collaborative task. Experiment 1 showed that multiuser conditions are significantly preferred, in terms of fun and motivation, compared to the single-user condition. The performance of some users was even significantly improved in the multiuser condition. A subset of well-performing subjects was involved in the second experiment, where we added the competitive task. Experiment 2 suggested that competitive and collaborative conditions may lead to similar performances and motivations. However, the corresponding gaming experiences can be perceived differently among the participants. Taken together our results suggest that multiuser BCI applications can be operational, effective, and more engaging for participants.

While, to date, there has been successful research into brain-computer game interaction (BCGI), the algorithms and techniques developed are limited in scope and may not utilize all available data in the appropriate contexts, e.g., optimizing for genre-specific games. This special issue was, therefore, solicited to gain insights into new biosignal processing algorithms, tested in gaming applications, which exploit BCI and neural signals to enhance gameplay experience and playermotivation, be the players ablebodied or physically impaired. A snapshot of the current trends in BCIcontrolled computer games is presented across 11 manuscripts. Each is briefly summarized in this editorial introduction.

The goal of this study is to explore new navigation methods in Virtual Reality (VR) and to understand the impact of motor activity on spatial cognition, and more precisely the question of the spatial learning transfer. We present a user study comparing two interfaces with different motor activities: the first one, a walking interface (a treadmill with rotation) gives the user a high level of sensorimotor activity (especially body-based and vestibular information). The second one, a brain computer interface (BCI), enables the user to navigate in a virtual environment (VE) without any motor activity, by using brain activity only. The task consisted in learning a path in a virtual city built from a 3D model of a real city with either one of these two interfaces (named treadmill condition and BCI condition), or in the real city directly (the real condition). Then, participants had to recall spatial knowledge, according to six different tasks assessing spatial memory and transfer. We also evaluated the ergonomics of these two interfaces and the presence felt by participants. Surprisingly, contrary to expectations, our results showed similar performances whatever the spatial restitution tasks or the interfaces used, very close to that of the real condition, which tends to indicate that motor activity is not essential to learn and transfer spatial knowledge. Even if BCI seems to be less natural to use than the treadmill, our study suggests that BCI is a promising interface for studying spatial cognition.

Brain-Computer Interfaces (BCI) are communication systems conveying messages through brain activity only. This paper elaborates on the suitability of BCI for 3D Video Games (VG). Thus, we first review some recent BCI-based 3D VG. We then discuss the limitations of current BCI technology, those being mainly related to usability and performances. Finally, we report on some areas in which BCI could be useful for 3D VG despite their limitations. More precisely, BCI could be useful as an additional control channel, to send commands that cannot be intuitively sent with other devices. BCI could also be used for mental state monitoring either 1) during the game, in order to make adaptive and dynamic video games or 2) during the game creation in order to maximizes some measures of game quality that could be derived from a tester's mental state.

A major limitation of Brain-Computer Interfaces (BCI) is their long calibration time, as much data from the user must be collected in order to tune the BCI for this target user. In this paper, we propose a new method to reduce this calibration time by using data from other subjects. More precisely, we propose an algorithm to regularize the Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) algorithms based on the data from a subset of automatically selected subjects. An evaluation of our approach showed that our method significantly outperformed the standard BCI design especially when the amount of data from the target user is small. Thus, our approach helps in reducing the amount of data needed to achieve a given performance level.

In this paper, we propose a new algorithm for Brain-Computer Interface (BCI): Spatially Regularized Common Spatial Patterns (SRCSP). SRCSP is an extension of the famous CSP algorithm which includes spatial a priori in the learning process, by adding a regularization term which penalizes spatially non smooth filters. We compared SRCSP and CSP algorithms on data of 14 subjects from BCI competitions. Results suggested that SRCSP can improve performances, around 10% more in classification accuracy, for subjects with poor CSP performances. They also suggested that SRCSP leads to more physiologically relevant filters than CSP.

In this paper, we propose a model to predict the performance of selection techniques using Brain-Computer Interfaces based on P300 signals. This model is based on Markov theory and can compute both the time required to select a target and the number of visual flashes needed. We illustrate how to use this model with three different interaction techniques to select a target. A first experimental evaluation with three healthy participants shows a good match between the model and the experimental data.

This paper deals with pattern rejection strategies for self-paced brain-computer interfaces (BCI). First, it introduces two pattern rejection strategies not used yet for self-paced BCI design: 1) the rejection class (RC) strategy and 2) thresholds on reliability functions (TRF) based on the automatic multiple-threshold learning algorithm. Second, it compares several rejection strategies using several classifiers, on motor imagery data, in order to identify their most desirable properties. Results showed that nonlinear classifiers led to the most efficient self-paced BCI. Concerning the reject option, RC outperformed a specialized reject classifier which outperformed TRF. Overall, the best results were obtained using the RC reject option and non-linear classifiers such as a Gaussian support vector machine, a fuzzy inference system or a radial basis function network.