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

Speier, William; Chandravadia, Nand; Roberts, Dustin G.; Pendekanti, Shrita; Pouratian, Nader

doi:10.1080/2326263x.2016.1252143

Cited by 20 publications

(10 citation statements)

References 36 publications

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“…Based on the EEG signal decoding, a few researches start to explore the non-invasive brain typing method. The approach in [33] enables ALS patients to type through BCI and achieves the typing rate of 6 characters per minute. The authors in [34] investigate three kinds of typing interfaces and illustrates that both matrix presentation and RSVP (rapid serial visual presentation) can work well.…”

Section: Related Workmentioning

confidence: 99%

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

Zhang

Yao

Sheng

et al. 2018

2018 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subject's active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a largescale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-theart methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.

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Section: Related Workmentioning

confidence: 99%

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

Zhang

Yao

Sheng

et al. 2018

2018 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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“…a personal pronoun ('he') in English is more likely to precede a finite verb ('reads') than vice versa). It has been shown that predictive methods from linguistics, which rely on probabilities of co-occurrence of language units, can enhance the speed of spelling in P300 interfaces in healthy [86] and in paralyzed [87] subjects. A recent development in ECoG-BCI studies is that probabilistic n-gram-based models have been applied in production [3] and perception [88] studies to constrain the number of meaningful choices for speech decoding.…”

Section: Developments In the Area Of Direct Speech Bcis From A Linguimentioning

confidence: 99%

Neurolinguistic and machine-learning perspectives on direct speech BCIs for restoration of naturalistic communication

Iljina

Derix

Schirrmeister

et al. 2017

Brain-Computer Interfaces

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a GrK 1624 'frequency effects in language' , university of freiburg, ABSTRACT The ultimate goal of brain-computer-interface (BCI) research on speech restoration is to develop devices which will be able to reconstruct spontaneous, naturally spoken language from the underlying neuronal signals. From this it follows that thorough understanding of brain activity and its functional dynamics during real-world speech will be required. Here, we review current developments in intracranial neurolinguistic and BCI research on speech production under increasingly naturalistic conditions. With an example of neurolinguistic data from our ongoing research, we illustrate the plausibility of neurolinguistic investigations in non-experimental, out-of-the-lab conditions of speech production. We argue that interdisciplinary endeavors at the interface of neuroscience and linguistics can provide valuable insight into the functional significance of speech-related neuronal data. Finally, we anticipate that work with neurolinguistic corpora composed of real-world language samples and simultaneous neuronal recordings, together with machine-learning methodology accounting for the specifics of the neurolinguistic material, will improve the functionality of speech BCIs.

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“…Likewise, Kubler et al showed that four individuals with ALS could learn to operate a BCI with EEG rhythms recorded over the sensorimotor cortex. However, most BCI studies in individuals with ALS have used the P300 . Of interest is the fact that most of these studies used relatively standard signal‐processing methods, in contrast to many of the studies examining archival data that tended to evaluate more novel and complex methods.…”

Section: Real‐time Applications Of Bcis With Disabled Usersmentioning

confidence: 99%

Brain‐computer interfaces for amyotrophic lateral sclerosis

McFarland

2020

Muscle and Nerve

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A brain-computer interface (BCI) is a device that detects signals from the brain and transforms them into useful commands. Researchers have developed BCIs that utilize different kinds of brain signals. These different BCI systems have differing characteristics, such as the amount of training required and the degree to which they are or are not invasive. Much of the research on BCIs to date has involved healthy individuals and evaluation of classification algorithms. Some BCIs have been shown to have potential benefit for users with minimal muscular function as a result of amyotrophic lateral sclerosis. However, there are still several challenges that need to be successfully addressed before BCIs can be clinically useful.amyotrophic lateral sclerosis, brain-computer interface, neuroprosthetics

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

Cited by 20 publications

References 36 publications

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

Neurolinguistic and machine-learning perspectives on direct speech BCIs for restoration of naturalistic communication

Brain‐computer interfaces for amyotrophic lateral sclerosis

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