Incorporating advanced language models into the P300 speller using particle filtering

Speier, William; Arnold, Corey; Deshpande, Aniket; Knall, J; Pouratian, Nader

doi:10.1088/1741-2560/12/4/046018

Cited by 18 publications

(27 citation statements)

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“…Sampling methods are necessary for estimating the probability distribution over such models so that high probability sequences can still be tracked without losing the ability to run analysis in real time. Speier et al [66] applied sequential importance resampling, a standard particle filtering (PF) method to handle more complicated language models. In this system, a probabilistic automaton was used to represent word frequency in English text.…”

Section: Resultsmentioning

confidence: 99%

“…Several subsequent methods have since incorporated similar methods and it is quickly becoming the standard in P300 classification [48,54,55,60,66]. Dynamic classification was implemented by setting a threshold probability, p Thresh , to determine when a decision should be made.…”

Section: Resultsmentioning

confidence: 99%

“…Process model approaches such as those presented by Speier et al [60,66] optimize over the entire output string and are able to update mistyped characters as more selections are made. Characters are changed if subsequent characters create strings that do not agree with the language model.…”

Section: Resultsmentioning

confidence: 99%

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Integrating language models into classifiers for BCI communication: a review

2016

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The present review systematically examines the integration of language models to improve classifier performance in brain-computer interface (BCI) communication systems. The domain of natural language has been studied extensively in linguistics and has been used in the natural language processing (NLP) 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 (AAC) 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. Although 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. Each 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 ALS population.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Integrating language models into classifiers for BCI communication: a review

2016

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“…To the best of our knowledge, it has rarely been explored in the area of BCI, with a notable exception being the work of Speier et al (2015), who later found it to be intractable due to complexity issues (Speier et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the Second Workshop on Subword/Character LEvel Models

2018

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ii Introduction Traditional NLP starts with a hand-engineered layer of representation, the level of tokens or words. A tokenization component first breaks up the text into units using manually designed rules. Tokens are then processed by components such as word segmentation, morphological analysis and multiword recognition. The heterogeneity of these components makes it hard to create integrated models of both structure within tokens (e.g., morphology) and structure across multiple tokens (e.g., multi-word expressions). This approach can perform poorly (i) for morphologically rich languages, (ii) for noisy text, (iii) for languages in which the recognition of words is difficult and (iv) for adaptation to new domains; and (v) it can impede the optimization of preprocessing in end-to-end learning.The workshop provides a forum for discussing recent advances as well as future directions on sub-word and character-level natural language processing and representation learning that address these problems. Topics of Interest:• tokenization-free models• character-level machine translation• character-ngram information retrieval• transfer learning for character-level models• models of within-token and cross-token structure• NL generation (of words not seen in training etc)• out of vocabulary words• morphology and segmentation• relationship b/w morphology and character-level models• stemming and lemmatization • inflection generation• orthographic productivity• form-meaning representations• true end-to-end learning AbstractNeural machine translation has achieved impressive results in the last few years, but its success has been limited to settings with large amounts of parallel data. One way to improve NMT for lower-resource settings is to initialize a word-based NMT model with pretrained word embeddings. However, rare words still suffer from lower quality word embeddings when trained with standard word-level objectives. We introduce word embeddings that utilize morphological resources, and compare to purely unsupervised alternatives. We work with Arabic, a morphologically rich language with available linguistic resources, and perform Ar-to-En MT experiments on a small corpus of TED subtitles. We find that word embeddings utilizing subword information consistently outperform standard word embeddings on a word similarity task and as initialization of the source word embeddings in a low-resource NMT system.

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“…[17,18] However, recently a particle filter (PF) algorithm made possible the use of more complicated language models by eliminating the requirement for sampling over entire state spaces, which was shown to have superior results, yielding a 56% average increase in bit rate over traditional methods in offline trials. [19] This method approximates distributions by projecting samples through a state-space language model based on the observed EEG signals. The system then determines the most likely output by finding the state that attracts the highest number of samples.…”

Section: Introductionmentioning

confidence: 99%

Online BCI typing using language model classifiers by ALS patients in their homes

Speier

Chandravadia

Roberts

et al. 2016

Brain-Computer Interfaces

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The P300 speller is a common brain-computer interface system that can provide a means of communication for patients with amyotrophic lateral sclerosis (ALS). Recent studies have shown that incorporating language information in signal classification can improve system performance, but they have largely been tested on healthy volunteers in a laboratory setting. The goal of this study was to demonstrate the functionality of the P300 speller system with language models when used by ALS patients in their homes. Six ALS patients with functional ratings ranging from two to 28 participated in this study. All subjects had improved offline performance when using a language model and five subjects were able to type at least six characters per minute with over 84% accuracy in online sessions. The results of this study indicate that the improvements in performance using language models in the P300 speller translate into the ALS population, which could help to make it a viable assistive device.

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Incorporating advanced language models into the P300 speller using particle filtering

Cited by 18 publications

References 27 publications

Integrating language models into classifiers for BCI communication: a review

Integrating language models into classifiers for BCI communication: a review

Proceedings of the Second Workshop on Subword/Character LEvel Models

Online BCI typing using language model classifiers by ALS patients in their homes

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