The SSVEP-Based BCI Text Input System Using Entropy Encoding Algorithm

Y-J. Chen, S-C. Chen, I.A.E. Zaeni, C-M. Wu, A.J. Tickle, P-J. Chen

    Research output: Contribution to journalArticle

    4 Citations (Scopus)
    22 Downloads (Pure)

    Abstract

    The so-called amyotrophic lateral sclerosis (ALS) or motor neuron disease (MND) is a neurodegenerative disease with various causes. It is characterized by muscle spasticity, rapidly progressive weakness due to muscle atrophy, and difficulty in speaking, swallowing, and breathing. The severe disabled always have a common problem that is about communication except physical malfunctions. The steady-state visually evoked potential based brain computer interfaces (BCI), which apply visual stimulus, are very suitable to play the role of communication interface for patients with neuromuscular impairments. In this study, the entropy encoding algorithm is proposed to encode the letters of multilevel selection interface for BCI text input systems. According to the appearance frequency of each letter, the entropy encoding algorithm is proposed to construct a variable-length tree for the letter arrangement of multilevel selection interface. Then, the Gaussian mixture models are applied to recognize electrical activity of the brain. According to the recognition results, the multilevel selection interface guides the subject to spell and type the words. The experimental results showed that the proposed approach outperforms the baseline system, which does not consider the appearance frequency of each letter. Hence, the proposed approach is able to ease text input interface for patients with neuromuscular impairments.
    Original languageEnglish
    Article number234260
    JournalMathematical Problems in Engineering
    Volume2015
    DOIs
    Publication statusPublished - 2015

    Bibliographical note

    Copyright © 2015 Yeou-Jiunn Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

    Keywords

    • Algorithms
    • Bioelectric potentials
    • Brain
    • Encoding (symbols)
    • Entropy
    • Interface states
    • Interfaces (computer)
    • Medical computing
    • Muscle
    • Neurodegenerative diseases
    • Neurons
    • Trees (mathematics)

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