The first Brain-Computer Interface utilizing a Turkish language model

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...

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Vydáno v:2013 21st Signal Processing and Communications Applications Conference (SIU) s. 1 - 4
Hlavní autoři: Ulas, C., Cetin, M.
Médium: Konferenční příspěvek
Jazyk:angličtina
turečtina
Vydáno: IEEE 01.04.2013
Témata:
Accuracy
Brain modeling
Brain-Computer Interface
Brain-computer interfaces
Electroencephalography
Forward-Backward algorithm
Hidden Markov Model
Hidden Markov models
language model
Markov processes
P300 speller
Viterbi algorithm
ISBN:9781467355629, 1467355623
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Abstract 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.
AbstractList 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.
Author Ulas, C.
Cetin, M.
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  surname: Cetin
  fullname: Cetin, M.
  email: mcetin@sabanciuniv.edu
  organization: Muhendislik ve Doga Bilimleri Fak., Sabanci Univ., İstanbul, Turkey
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Snippet One of the widely studied electroencephalography (EEG) based Brain-Computer Interface (BCI) set ups involves having subjects type letters based on so-called...
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SubjectTerms Accuracy
Brain modeling
Brain-Computer Interface
Brain-computer interfaces
Electroencephalography
Forward-Backward algorithm
Hidden Markov Model
Hidden Markov models
language model
Markov processes
P300 speller
Viterbi algorithm
Title The first Brain-Computer Interface utilizing a Turkish language model
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