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

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...

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Vydané v:Mathematical problems in engineering Ročník 2015; číslo 2015; s. 1 - 8
Hlavní autori: Tickle, Andrew Jason, Wu, Chung-Min, Zaeni, Ilham A. E., Chen, Shih-Chung, Chen, Yeou-Jiunn, Chen, Pei-Jarn
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015
John Wiley & Sons, Inc
Predmet:
Activity recognition
Algorithms
Atrophy
Coding
Data compression
Efficiency
Electroencephalography
Encoding
Entropy
Fourier transforms
Human-computer interface
Impairment
Multilevel
Muscles
Patients
Probabilistic models
Texts
Visual stimuli
Taiwan
ISSN:1024-123X, 1563-5147
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Shrnutí: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.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2015/234260