Advancing the detection of steady-state visual evoked potentials in brain-computer interfaces

© 2016 IOP Publishing Ltd. Objective. Spatial filtering has proved to be a powerful pre-processing step in detection of steady-state visual evoked potentials and boosted typical detection rates both in offline analysis and online SSVEP-based brain-computer interface applications. State-of-the-art detection methods and the spatial filters used thereby share many common foundations as they all build upon the second order statistics of the acquired Electroencephalographic (EEG) data, that is, its spatial autocovariance and cross-covariance with what is assumed to be a pure SSVEP response. The present study aims at highlighting the similarities and differences between these methods. Approach. We consider the canonical correlation analysis (CCA) method as a basis for the theoretical and empirical (with real EEG data) analysis of the state-of-the-art detection methods and the spatial filters used thereby. We build upon the findings of this analysis and prior research and propose a new detection method (CVARS) that combines the power of the canonical variates and that of the autoregressive spectral analysis in estimating the signal and noise power levels. Main results. We found that the multivariate synchronization index method and the maximum contrast combination method are variations of the CCA method. All three methods were found to provide relatively unreliable detections in low signal-to-noise ratio (SNR) regimes. CVARS and the minimum energy combination methods were found to provide better estimates for different SNR levels. Significance. Our theoretical and empirical results demonstrate that the proposed CVARS method outperforms other state-of-the-art detection methods when used in an unsupervised fashion. Furthermore, when used in a supervised fashion, a linear classifier learned from a short training session is able to estimate the hidden user intention, including the idle state (when the user is not attending to any stimulus), rapidly, accurately and reliably

Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems

© 2017 IOP Publishing Ltd. Objective. This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry application, and a way to enable fluent and intuitive interaction in embodiment systems. The main focus is laid upon inferring the hidden target goals of users while navigating in a remote environment as a basis for possible adaptations. Approach. To reason about possible user goals, a general user-agnostic Bayesian update rule is devised to be recursively applied upon the arrival of evidences, i.e. user input and user gaze. Experiments were conducted with healthy subjects within robotic embodiment settings to evaluate the proposed method. These experiments varied along three factors: the type of the robot/environment (simulated and physical), the type of the interface (keyboard or BCI), and the way goal recognition (GR) is used to guide a simple shared control (SC) driving scheme. Main results. Our results show that the proposed GR algorithm is able to track and infer the hidden user goals with relatively high precision and recall. Further, the realized SC driving scheme benefits from the output of the GR system and is able to reduce the user effort needed to accomplish the assigned tasks. Despite the fact that the BCI requires higher effort compared to the keyboard conditions, most subjects were able to complete the assigned tasks, and the proposed GR system is additionally shown able to handle the uncertainty in user input during SSVEP-based interaction. The SC application of the belief vector indicates that the benefits of the GR module are more pronounced for BCIs, compared to the keyboard interface. Significance. Being based on intuitive heuristics that model the behavior of the general population during the execution of navigation tasks, the proposed GR method can be used without prior tuning for the individual users. The proposed methods can be easily integrated in devising more advanced SC schemes and/or strategies for automatic BCI self-adaptations

Invariance and variability in interaction error-related potentials and their consequences for classification

© 2017 IOP Publishing Ltd. Objective. This paper discusses the invariance and variability in interaction error-related potentials (ErrPs), where a special focus is laid upon the factors of (1) the human mental processing required to assess interface actions (2) time (3) subjects. Approach. Three different experiments were designed as to vary primarily with respect to the mental processes that are necessary to assess whether an interface error has occurred or not. The three experiments were carried out with 11 subjects in a repeated-measures experimental design. To study the effect of time, a subset of the recruited subjects additionally performed the same experiments on different days. Main results. The ErrP variability across the different experiments for the same subjects was found largely attributable to the different mental processing required to assess interface actions. Nonetheless, we found that interaction ErrPs are empirically invariant over time (for the same subject and same interface) and to a lesser extent across subjects (for the same interface). Significance. The obtained results may be used to explain across-study variability of ErrPs, as well as to define guidelines for approaches to the ErrP classifier transferability problem

Local and Remote Cooperation with Virtual and Robotic Agents: a P300 BCI Study in Healthy and People Living with Spinal Cord Injury

The development of technological applications that allow people to control and embody external devices within social interaction settings represents a major goal for current and future brain-computer interface (BCI) systems