Electrophysiologic features of SYT2 mutations causing a treatable neuromuscular syndrome.

OBJECTIVES: To describe the clinical and electrophysiologic features of synaptotagmin II (SYT2) mutations, a novel neuromuscular syndrome characterized by foot deformities and fatigable ocular and lower limb weakness, and the response to modulators of acetylcholine release. METHODS: We performed detailed clinical and neurophysiologic assessment in 2 multigenerational families with dominant SYT2 mutations (c.920T>G [p.Asp307Ala] and c.923G>A [p.Pro308Leu]). Serial clinical and electrophysiologic assessments were performed in members of one family treated first with pyridostigmine and then with 3,4-diaminopyridine. RESULTS: Electrophysiologic testing revealed features indicative of a presynaptic deficit in neurotransmitter release with posttetanic potentiation lasting up to 60 minutes. Treatment with 3,4-diaminopyridine produced both a clinical benefit and an improvement in neuromuscular transmission. CONCLUSION: SYT2 mutations cause a novel and potentially treatable complex presynaptic congenital myasthenic syndrome characterized by motor neuropathy causing lower limb wasting and foot deformities, with reflex potentiation following exercise and a uniquely prolonged period of posttetanic potentiation.

Hangman BCI: an unsupervised adaptive self-paced Brain-Computer Interface for playing games.

This paper presents a novel user interface suitable for adaptive Brain Computer Interface (BCI) system. A customized self-paced BCI architecture is introduced where the system combines onset detection system along with an adaptive classifier working in parallel. An unsupervised adaptive method based on sequential expectation maximization for Gaussian mixture model is employed with new timing scheme and an additional averaging step to avoid over-fitting. Sigmoid function based post-processing approach is proposed to enhance the classifiers' output. The adaptive system is compared to a non-adaptive one and tested on five subjects who used the BCI to play the hangman game. The results show significant improvement of the True-False difference for all the classes and a reduction in the number of steps required to solve the problem.

Conditional random fields as classifiers for three-class motor-imagery brain-computer interfaces.

Conditional random fields (CRFs) are demonstrated to be a discriminative model able to exploit the temporal properties of EEG data obtained during synchronous three-class motor-imagery-based brain-computer interface experiments. The advantages of CRFs over the hidden Markov model (HMM) are both theoretical and practical. Theoretically, CRFs focus on modeling latent variables (labels) rather than both observation and latent variables. Furthermore, CRFs' loss function is convex, guaranteeing convergence to the global optimum. Practically, CRFs are much less prone to singularity problems. This property allows for the use of both time- and frequency-based features, such as band power. The HMM, on the other hand, requires temporal features such as autoregressive coefficients. A CRF-based classifier is tested on 13 subjects. Significant improvement is found when applying CRFs over HMM- and LDA-based classifiers.

Temporal modeling of EEG during self-paced hand movement and its application in onset detection.

The temporal behavior of electroencephalography (EEG) recorded during self-paced hand movement is investigated for the purpose of improving EEG classification in general and onset detection in particular. Four temporal models based on conditional random fields are developed and applied to classify EEG data into the movement or idle class. They are further used for building an onset detection system and tested on self-paced EEG signals recorded from five subjects. True-false rates ranging from 74% to 98% have been achieved on different subjects, with significant improvement over non-temporal methods. The effectiveness of the proposed methods suggests their potential use in self-paced brain-computer interfaces.