Incorporation of a language model into a brain computer interface based speller

Brain computer interface (BCI) research deals with the problem of establishing direct communication pathways between the brain and external devices. The primary motivation is to enable patients with limited or no muscular control to use external devices by automatically interpreting their intent based on brain electrical activity, measured by, e.g., electroencephalography (EEG). The P300 speller is a widely practised BCI set up that involves having subjects type letters based on P300 signals generated by their brains in response to visual stimuli. Because of the low signal-to-noise ratio (SNR) and variability of EEG signals, existing typing systems use many repetitions of the visual stimuli in order to increase accuracy at the cost of speed. The main motivation for the work in this thesis comes from the observation that the prior information provided by both neighbouring and current letters within words in a particular language can assist letter estimation with the aim of developing a system that achieves higher accuracy and speed simultaneously. Based on this observation, in this thesis, we present an approach for incorporation of such information into a BCI-based speller through Hidden Markov Models (HMM) trained by a language model. We then describe filtering and smoothing algorithms in conjunction with n-gram language models for inference over such a model. We have designed data collection experiments for offline and online decision-making which demonstrate that incorporation of the language model in this manner results in significant improvements in letter estimation and typing speed

The first brain-computer interface utilizing a Turkish language model (Türkçe dil modeli kullanan ilk beyin-bilgisayar arayüzü)

One of the widely studied electroencephalography (EEG) based Brain-Computer Interface (BCI) set ups involves having subjects type letters based on so-called P300 signals generated by their brains in response to unpredictable stimuli. Due to the low signal-to-noise ratio (SNR) of EEG signals, current BCI typing systems need several stimulus repetitions to obtain acceptable accuracy, resulting in low typing speed. However, in the context of typing letters within words in a particular language, neighboring letters would provide information about the current letter as well. Based on this observation, we propose an approach for incorporation of such information into a BCI-based speller through a Hidden Markov Model (HMM) trained by a Turkish language model. We describe smoothing and Viterbi algorithms for inference over such a model. Experiments on real EEG data collected in our laboratory demonstrate that incorporation of the language model in this manner leads to significant improvements in classification accuracy and bit rate

Incorporation of a language model into a Brain Computer Interface based speller through HMMs

Brain computer interface (BCI) research deals with the problem of establishing direct communication pathways between the brain and external devices. The primary motivation is to enable patients with limited or no muscular control to use external devices by automatically interpreting their intent based on brain electrical activity, measured by, e.g., electroencephalography (EEG). A widely studied BCI set up involves having subjects type letters based on so-called P300 signals generated by their brains in response to visual stimuli. Due to the low signal-to-noise ratio (SNR) of EEG signals, brain signals generated for a single letter often have to be recorded many times to obtain acceptable accuracy, which reduces the typing speed of the system. Conventionally the measured signals for each letter are processed and classified separately. However, in the context of typing letters within words in a particular language, neighboring letters would provide information about the current letter as well. Based on this observation, we propose an approach for incorporation of such information into a BCI-based speller through hidden Markov models (HMM) trained by a language model. We then describe filtering and smoothing algorithms for inference over such a model. Experiments on real EEG data collected in our laboratory demonstrate that incorporation of the language model in this manner results in significant improvements in classification accuracy and bit rate

The investigation of the Cox-2 selective ınhibitor parecoxib effects in spinal cord injury in rat

Aim: Today, spinal cord injury (SCI) can be rehabilitated but cannot be treated adequately. This experimental study was conducted to investigate possible beneficial effects of methylprednisolone and parecoxib in treatment of SCI. Materials and methods: Forty-eight male Wistar albino rats were assigned into CONTROL, acute (MP-A, PX-A, and PXMP-A), and subacute (MP-S, PX-S, and PXMP-S) stage groups. Then, to induce SCI, a temporary aneurysm clip was applied to the spinal cord following T7-8 laminectomy, except in the CONTROL group. Four hours later parecoxib, methylprednisolone, or their combination was administered to rats intraperitoneally except CONTROL, SHAM-A, and SHAM-S groups. Rats in the acute stage group were sacrificed 72 h later, and whereas rats in the subacute stage were sacrificed 7 days later for histopathological and biochemical investigation and for gene-expression analyses. Results: Parecoxib and methylprednisolone and their combination could not improve histopathological grades in any stage. They also could not decrease malondialdehyde or caspase-3, myeloperoxidase levels in any stage. Parecoxib and methylprednisolone could decrease the TNF-? gene expression in subacute stage. Methylprednisolone could increase TGF-1ß gene-expression level in acute stage. Conclusion: Neither of the experimental drugs, either alone or in combination, did not show any beneficial effects in SCI model in rats

Effect of alpha-lipoic acid on small fibre neuropathy findings in patients with type 2 diabetes mellitus

Objective: Cutaneous silent period (CSP) is an inhibitory spinal reflex and the afferent arm of this response involves A-delta nerve fibers. The aim of this study was to investigate CSP parameters in patients with type 2 diabetes mellitus (T2DM) and to examine the effects of alpha-lipoic acid (ALA) treatment on CSP. To the best of our knowledge, this investigation has not yet been carried out until date. Methods: Seventeen patients with T2DM and 23 healthy volunteers were studied. CSP latency and duration in the upper and lower extremities of both the groups were examined. In T2DM patients, the variables were examined before and after ALA treatment. Results: CSP latency in T2DM patients was longer than that in the controls. In the patient group, CSP latency in the upper and lower extremities and CSP Latency Differences (LD) shortened in the third month after treatment compared with the pre-treatment values. Conclusions: The results suggest that ALA treatment may alleviate small-fiber neuropathy in T2DM patients and that CSP may be a useful supportive tool to evaluate ALA treatment effectiveness. © 2015, Ege University Press. All rights reserved