RSVP keyboard: An EEG based typing interface

Orhan, Umut; Hild, Kenneth E.; Erdoğmuş, Deni̇z; Roark, Brian; Oken, Barry; Fried-Oken, Melanie

doi:10.1109/icassp.2012.6287966

Cited by 98 publications

(93 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…6(a), many ERP-based BCI applications are designed based on a rapid serial visual presentation paradigm (RSVP), such as image search [47] and auto typing [48]. However, due to the nonstationarity of brain activity, an accurate estimation of the ERP requires the system to average over a large number of trials.…”

Section: B Radial Basis Function Network For Tracking Evoked Potentialsmentioning

confidence: 99%

EEG-Based Brain-Computer Interfaces: A Novel Neurotechnology and Computational Intelligence Method

Lin

Liu

et al. 2017

IEEE Syst. Man Cybern. Mag.

View full text Add to dashboard Cite

Abstract-Significant advances in neuroscience, sensor technologies, and efficient signal processing algorithms have greatly facilitated the transition from laboratory-oriented neuroscience research to practical applications. Brain-computer interfaces (BCIs) represent major strides in translating brain signals into actionable decisions and primarily consist of hardware and software that guide the communications between users and systems. This article presents several current neurotechnologies and computational intelligence methods applied to EEG-based BCIs. In the hardware aspect, novel portable EEG devices featuring dry electrodes are introduced as substitutes for traditional BCIs with wet electrodes and its bulky size. With these advantages, these novel EEG devices can acquire real-time EEG signals for operational workplaces without requiring conductive gel/paste or scalp preparations. As for the software aspect, blind source separation, artificial neural networks, effective connectivity measurements and information fusion techniques are introduced to address the technical issues of artifact removal, rapid event-related potential detection, complex brain network description, and decision fusion, respectively. For instance, information fusion technique has been utilized to attack the individual differences problem of motor imagery applications in the real-world environment. With continuous improvements in the development of a convenient approach to record brain signals and extract knowledge regarding intentions, BCI techniques are envisioned to lead to a wide range of real-life applications in the near future.

show abstract

Section: B Radial Basis Function Network For Tracking Evoked Potentialsmentioning

confidence: 99%

EEG-Based Brain-Computer Interfaces: A Novel Neurotechnology and Computational Intelligence Method

Lin

Liu

et al. 2017

IEEE Syst. Man Cybern. Mag.

View full text Add to dashboard Cite

show abstract

“…The works of Orhan et al, (2012) and Prabhu and Prasad (2011) used alphabets to represent natural language. Orhan et al (2012) used a simple interface that sequentially displays the letters of the alphabet, one by one, until the user identifies the intended letter and choose it.…”

Section: Primary Componentsmentioning

confidence: 99%

“…There are many types of programs ranging from virtual keyboards Fu and Ho, 2009;Orhan et al, 2012) to complex communication spreadsheets Mason and Chinn, 2010).…”

Section: Introductionmentioning

confidence: 99%

A concept-environment for computer-based augmentative and alternative communication founded on a systematic review

et al. 2015

View full text Add to dashboard Cite

Introduction: Locked-In Syndrome is admittedly the worst case of motor and speech impairment, it seriously damages the ability of oral and gestural communication of patients. In recent years, alternative and augmentative communication technology has provided resources to restore these patients' ability to communicate. Methods: In order to relate and classify the main methods with that purpose, this work conducted a systematic review on several journal databases. Results: We found 203 related papers and 55 of them were selected to compose the study. After that, we classified them into three major groups and we identified the main difficulties when using each approach. Conclusion: In order to overcome these difficulties, we propose a new system concept to develop an adaptive, robust and low cost communication environment. The proposed system is composed of five modules: data entry, communication, aid to the caregiver and external interaction.

show abstract

“…Mora-Cortes et al (2014) describe different types of EEG responses common when employing a typing-based BCI, and in the review of Speier et al (2016), event-related potentials (ERP) seem to be the prevailing choice in particular. In the RSVP-Keyboard (Orhan et al, 2012), an ERP signal is elicited in response to a rapid display of letters. Other systems such as the P300 (Farwell and Donchin, 1988) present a user with a grid of letters and the ERP signal is retrieved in response to a flash of each row or column.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the Second Workshop on Subword/Character LEvel Models

2018

View full text Add to dashboard Cite

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.

show abstract

RSVP keyboard: An EEG based typing interface

Cited by 98 publications

References 12 publications

EEG-Based Brain-Computer Interfaces: A Novel Neurotechnology and Computational Intelligence Method

EEG-Based Brain-Computer Interfaces: A Novel Neurotechnology and Computational Intelligence Method

A concept-environment for computer-based augmentative and alternative communication founded on a systematic review

Proceedings of the Second Workshop on Subword/Character LEvel Models

Contact Info

Product

Resources

About