Single Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes

BACKGROUND: Brain computer interfaces (BCI) based on electro-encephalography (EEG) have been shown to detect mental states accurately and non-invasively, but the equipment required so far is cumbersome and the resulting signal is difficult to analyze. BCI requires accurate classification of small amplitude brain signal components in single trials from recordings which can be compromised by currents induced by muscle activity. METHODOLOGY/PRINCIPAL FINDINGS: A novel EEG cap based on dry electrodes was developed which does not need time-consuming gel application and uses far fewer electrodes than on a standard EEG cap set-up. After optimizing the placement of the 6 dry electrodes through off-line analysis of standard cap experiments, dry cap performance was tested in the context of a well established BCI cursor control paradigm in 5 healthy subjects using analysis methods which do not necessitate user training. The resulting information transfer rate was on average about 30% slower than the standard cap. The potential contribution of involuntary muscle activity artifact to the BCI control signal was found to be inconsequential, while the detected signal was consistent with brain activity originating near the motor cortex. CONCLUSIONS/SIGNIFICANCE: Our study shows that a surprisingly simple and convenient method of brain activity imaging is possible, and that simple and robust analysis techniques exist which discriminate among mental states in single trials. Within 15 minutes the dry BCI device is set-up, calibrated and ready to use. Peak performance matched reported EEG BCI state of the art in one subject. The results promise a practical non-invasive BCI solution for severely paralyzed patients, without the bottleneck of setup effort and limited recording duration that hampers current EEG recording technique. The presented recording method itself, BCI not considered, could significantly widen the use of EEG for emerging applications requiring long-term brain activity and mental state monitoring

Towards Zero Training for Brain-Computer Interfacing

Electroencephalogram (EEG) signals are highly subject-specific and vary considerably even between recording sessions of the same user within the same experimental paradigm. This challenges a stable operation of Brain-Computer Interface (BCI) systems. The classical approach is to train users by neurofeedback to produce fixed stereotypical patterns of brain activity. In the machine learning approach, a widely adapted method for dealing with those variances is to record a so called calibration measurement on the beginning of each session in order to optimize spatial filters and classifiers specifically for each subject and each day. This adaptation of the system to the individual brain signature of each user relieves from the need of extensive user training. In this paper we suggest a new method that overcomes the requirement of these time-consuming calibration recordings for long-term BCI users. The method takes advantage of knowledge collected in previous sessions: By a novel technique, prototypical spatial filters are determined which have better generalization properties compared to single-session filters. In particular, they can be used in follow-up sessions without the need to recalibrate the system. This way the calibration periods can be dramatically shortened or even completely omitted for these ‘experienced’ BCI users. The feasibility of our novel approach is demonstrated with a series of online BCI experiments. Although performed without any calibration measurement at all, no loss of classification performance was observed

Locomotor recovery following contusive spinal cord injury does not require oligodendrocyte remyelination

The contribution of oligodendrocytes to remyelination in functional recovery after spinal cord injury is not fully understood. Here, the authors show that oligodendrocyte progenitor cell differentiation is not required for functional recovery after spinal cord injury in mice

Regeneration of myelin sheaths of normal length and thickness in the zebrafish CNS correlates with growth of axons in caliber

Demyelination is observed in numerous diseases of the central nervous system, including multiple sclerosis (MS). However, the endogenous regenerative process of remyelination can replace myelin lost in disease, and in various animal models. Unfortunately, the process of remyelination often fails, particularly with ageing. Even when remyelination occurs, it is characterised by the regeneration of myelin sheaths that are abnormally thin and short. This imperfect remyelination is likely to have implications for the restoration of normal circuit function and possibly the optimal metabolic support of axons. Here we describe a larval zebrafish model of demyelination and remyelination. We employ a drug-inducible cell ablation system with which we can consistently ablate 2/3rds of oligodendrocytes in the larval zebrafish spinal cord. This leads to a concomitant demyelination of 2/3rds of axons in the spinal cord, and an innate immune response over the same time period. We find restoration of the normal number of oligodendrocytes and robust remyelination approximately two weeks after induction of cell ablation, whereby myelinated axon number is restored to control levels. Remarkably, we find that myelin sheaths of normal length and thickness are regenerated during this time. Interestingly, we find that axons grow significantly in caliber during this period of remyelination. This suggests the possibility that the active growth of axons may stimulate the regeneration of myelin sheaths of normal dimensions

A common spatial pattern approach for classification of mental counting and motor execution EEG

© Springer Nature Switzerland AG 2018. A Brain Computer Interface (BCI) as a medium of communication is convenient for people with severe motor disabilities. Although there are a number of different BCIs, the focus of this paper is on Electroencephalography (EEG) as a means of human computer interaction. Motor imagery and mental arithmetic are the most popular techniques used to modulate brain waves that can be used to control devices. We show that it is possible to define different mental states using real fist rotation and imagined reverse counting. While people have already investigated left fist rotation and right fist rotation for dual state BCI, we intend to define a new state using mental reverse counting. We use Common Spatial Pattern (CSP) approach for feature extraction to distinguish between these states. CSP has been prominently used in the context of motor imagery task, we define its applicability for the distinction between motor execution and mental counting. CSP features are evaluated using classifiers like GMM, SVM, and GMM-UBM. GMM-UBM using data filtered through the beta band (13–30 Hz) gives the best performance

Across-subject offline decoding of motor imagery from MEG and EEG

Long calibration time hinders the feasibility of brain-computer interfaces (BCI). If other subjects' data were used for training the classifier, BCI-based neurofeedback practice could start without the initial calibration. Here, we compare methods for inter-subject decoding of left- vs. right-hand motor imagery (MI) from MEG and EEG. Six methods were tested on data involving MEG and EEG measurements of healthy participants. Inter-subject decoders were trained on subjects showing good within-subject accuracy, and tested on all subjects, including poor performers. Three methods were based on Common Spatial Patterns (CSP), and three others on logistic regression with l1 - or l2,1 -norm regularization. The decoding accuracy was evaluated using (1) MI and (2) passive movements (PM) for training, separately for MEG and EEG. With MI training, the best accuracies across subjects (mean 70.6% for MEG, 67.7% for EEG) were obtained using multi-task learning (MTL) with logistic regression and l2,1-norm regularization. MEGyielded slightly better average accuracies than EEG. With PM training, none of the inter-subject methods yielded above chance level (58.7%) accuracy. In conclusion, MTL and training with other subject's MI is efficient for inter-subject decoding of MI. Passive movements of other subjects are likely suboptimal for training the MI classifiers.Peer reviewe