Robust common spatial filters with a maxmin approach

Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison

Brain computer interfaces (BCIs) have been attracting a great interest in recent years. The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally proposed from a heuristic viewpoint, it can be also built on very strong foundations using information theory. This paper reviews the relationship between CSP and several information-theoretic approaches, including the Kullback-Leibler divergence, the Beta divergence and the Alpha-Beta log-det (AB-LD)divergence. We also revise other approaches based on the idea of selecting those features that are maximally informative about the class labels. The performance of all the methods will be also compared via experiments.

Robust Common Spatial Filters with a Maxmin Approach

Electroencephalographic signals are known to be nonstationary and easily affected by artifacts; therefore, their analysis requires methods that can deal with noise. In this work, we present a way to robustify the popular common spatial patterns (CSP) algorithm under a maxmin approach. In contrast to standard CSP that maximizes the variance ratio between two conditions based on a single estimate of the class covariance matrices, we propose to robustly compute spatial filters by maximizing the minimum variance ratio within a prefixed set of covariance matrices called the tolerance set. We show that this kind of maxmin optimization makes CSP robust to outliers and reduces its tendency to overfit. We also present a data-driven approach to construct a tolerance set that captures the variability of the covariance matrices over time and shows its ability to reduce the nonstationarity of the extracted features and significantly improve classification accuracy. We test the spatial filters derived with this approach and compare them to standard CSP and a state-of-the-art method on a real-world brain-computer interface (BCI) data set in which we expect substantial fluctuations caused by environmental differences. Finally we investigate the advantages and limitations of the maxmin approach with simulations.

Investigating brief motor imagery for an ERD/ERS based BCI

This study establishes the effectiveness of event related synchronisation (ERS) features for a system paced brain computer interface (BCI). In particular, the relationship between the duration of motor imagery (MI) and the quality of the features extracted from the ERS is investigated. To this end, two groups of users performed brief (2s) or sustained (4s) MI, and offline single trial BCIs were validated on each group based on features extracted from the EEG before, during and after MI. The BCIs were designed to recognise two intentional control tasks and a no-control state. Cross-validated results indicate that brief MI leads to more informative ERS features than sustained MI.

ERD-based online brain-machine interfaces (BMI) in the context of neurorehabilitation: optimizing BMI learning and performance

Event-related desynchronization (ERD) of sensori-motor rhythms (SMR) can be used for online brain-machine interface (BMI) control, but yields challenges related to the stability of ERD and feedback strategy to optimize BMI learning.Here, we compared two approaches to this challenge in 20 right-handed healthy subjects (HS, five sessions each, S1-S5) and four stroke patients (SP, 15 sessions each, S1-S15). ERD was recorded from a 275-sensor MEG system. During daily training,motor imagery-induced ERD led to visual and proprioceptive feedback delivered through an orthotic device attached to the subjects' hand and fingers. Group A trained with a heterogeneous reference value (RV) for ERD detection with binary feedback and Group B with a homogenous RV and graded feedback (10 HS and 2 SP in each group). HS in Group B showed better BMI performance than Group A (p < 0.001) and improved BMI control from S1 to S5 (p = 0.012) while Group A did not. In spite of the small n, SP in Group B showed a trend for a higher BMI performance (p = 0.06) and learning was significantly better (p < 0.05). Using a homogeneous RV and graded feedback led to improved modulation of ipsilesional activity resulting in superior BMI learning relative to use of a heterogeneous RV and binary feedback.

Automatic classification of artifactual ICA-components for artifact removal in EEG signals

BACKGROUND

Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts.

METHODS

We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects.

RESULTS

Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components.

CONCLUSIONS

We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.

Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges

In recent years, new research has brought the field of electroencephalogram (EEG)-based brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely, “Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user–machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human–computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices.