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ObjectiveThe present review systematically examines the integration of language models to improve classifier performance in brain-computer interface (BCI) communication systems.ApproachThe domain of natural language has been studied extensively in linguistics and has been used in the natural language processing field in applications including information extraction, machine translation, and speech recognition. While these methods have been used for years in traditional augmentative and assistive communication devices, information about the output domain has largely been ignored in BCI communication systems. Over the last few years, BCI communication systems have started to leverage this information through the inclusion of language models.Main resultsAlthough this movement began only recently, studies have already shown the potential of language integration in BCI communication and it has become a growing field in BCI research. BCI communication systems using language models in their classifiers have progressed down several parallel paths, including: word completion; signal classification; integration of process models; dynamic stopping; unsupervised learning; error correction; and evaluation.SignificanceEach of these methods have shown significant progress, but have largely been addressed separately. Combining these methods could use the full potential of language model, yielding further performance improvements. This integration should be a priority as the field works to create a BCI system that meets the needs of the amyotrophic lateral sclerosis population.

ObjectiveThe P300 speller is a common brain-computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject's electroencephalogram signal. Information about the structure of natural language can be valuable for BCI communication, but attempts to use this information have thus far been limited to rudimentary n-gram models. While more sophisticated language models are prevalent in natural language processing literature, current BCI analysis methods based on dynamic programming cannot handle their complexity.ApproachSampling methods can overcome this complexity by estimating the posterior distribution without searching the entire state space of the model. In this study, we implement sequential importance resampling, a commonly used particle filtering (PF) algorithm, to integrate a probabilistic automaton language model.Main resultThis method was first evaluated offline on a dataset of 15 healthy subjects, which showed significant increases in speed and accuracy when compared to standard classification methods as well as a recently published approach using a hidden Markov model (HMM). An online pilot study verified these results as the average speed and accuracy achieved using the PF method was significantly higher than that using the HMM method.SignificanceThese findings strongly support the integration of domain-specific knowledge into BCI classification to improve system performance.

Brain-computer interfaces (BCI) such as the P300 speller have the potential to restore communication to advanced-stage neuromuscular disease patients. Research has improved typing speed and accuracy through innovations including the use of language models. While significant advances have been made, implementations have largely been restricted to a single language, primarily English. It is unclear whether these improvements would extend to other languages that present potential technical hurdles due to different alphabets and grammatical structures. Here, we adapt a language model-based classifier designed for English to two other languages, Spanish and Greek, to demonstrate the generalizability of these methods. Online experimental trials with 30 healthy native English, Spanish, and Greek speakers showed no significant difference between performances using the different versions of the system (66.20 vs. 61.97 vs. 60.89 bits/minute). Extending these methods across languages allows for expanding access to BCI systems to other populations, particularly in the developing world. [ABSTRACT FROM AUTHOR]

Background: Since 1996, 6 new drugs have been introduced for the treatment of metastatic colorectal cancer. Although they are promising, these drugs frequently are given in the palliative and are much more expensive than older treatments. The objective of the current study was to measure the cost implications of treatment with sequential regimens that include chemotherapy and/or monoclonal antibodies.Methods: A Markov model was used to evaluate a hypothetical cohort of 1000 patients with newly diagnosed, metastatic colorectal cancer. Patients supposedly received up to 3 lines of treatment before supportive care and subsequent death. Data were obtained from published, multicenter phase 2 and randomized phase 3 clinical trials. Sensitivity analyses were conducted on the efficacy, toxicity, and cost.Results: Using drug costs alone, treatment that included new chemotherapeutic agents increased survival at an incremental cost-effectiveness ratio (ICER) of $100,000 per discounted life-year (DLY). The addition of monoclonal antibodies improved survival at an ICER of >$170,000 per DLY. The results were most sensitive to changes in the initial regimen. Even with significant improvements in clinical characteristics (efficacy and toxicity), treatment with the most effective regimens still had very high ICERs.Conclusions: Treatment of metastatic colorectal cancer with the most effective regimens came at very high incremental costs. The authors concluded that cost-effectiveness analyses should be a routine component of the drug-development process, so that physicians and patients are informed appropriately regarding the value of new innovations. [ABSTRACT FROM AUTHOR]

The combination of x-ray diffraction analysis with surface sensitive techniques reveals a chemical inhomogeneity in the surface region of monocrystals of SrTiO[sub 3] prepared under low and high partial pressure of oxygen at elevated temperatures. A solid state reaction leads to the formation of a multilayer-type structure. For oxidized crystals we observe AO-rich Ruddlesden-Popper phases at the surface and Magnelli phases of Ti in deeper layers of the surface region. The order of the layered structure is reversed for the reduced crystals with Ti-oxides of different oxidation levels at the surface and Ruddlesden-Popper phases in lower parts of the surface region. It is argued that this restructuring influences the electrical properties of SrTiO[sub 3] . [ABSTRACT FROM AUTHOR]