Lip contour detection based AAC device using morse code

Anila, M; Radhika, P

doi:10.1109/wispnet.2017.8299950

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Subjects with ALS can rely on AAC to facilitate communication [3][4][5][6][7]. Hornero et al developed a communication board to help subjects with speech disabilities [3].…”

Section: Introductionmentioning

confidence: 99%

“…To help subjects with severe disabilities, Jafari et al proposed a tongue drive system, which uses voluntary tongue movements as the input interface, to help people with accessing their environment [4]. Anila and Radhika used the Morse code, which is detected from the lip contour, as a human communication interface [5]. Thus, the patients can easily communicate with others when they are familiar with the Morse code.…”

Section: Introductionmentioning

confidence: 99%

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Denoising Autoencoder-Based Feature Extraction to Robust SSVEP-Based BCIs

Chen

et al. 2021

Sensors

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For subjects with amyotrophic lateral sclerosis (ALS), the verbal and nonverbal communication is greatly impaired. Steady state visually evoked potential (SSVEP)-based brain computer interfaces (BCIs) is one of successful alternative augmentative communications to help subjects with ALS communicate with others or devices. For practical applications, the performance of SSVEP-based BCIs is severely reduced by the effects of noises. Therefore, developing robust SSVEP-based BCIs is very important to help subjects communicate with others or devices. In this study, a noise suppression-based feature extraction and deep neural network are proposed to develop a robust SSVEP-based BCI. To suppress the effects of noises, a denoising autoencoder is proposed to extract the denoising features. To obtain an acceptable recognition result for practical applications, the deep neural network is used to find the decision results of SSVEP-based BCIs. The experimental results showed that the proposed approaches can effectively suppress the effects of noises and the performance of SSVEP-based BCIs can be greatly improved. Besides, the deep neural network outperforms other approaches. Therefore, the proposed robust SSVEP-based BCI is very useful for practical applications.

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“…Subjects with ALS can rely on AAC to facilitate communication [3][4][5][6][7]. Hornero et al developed a communication board to help subjects with speech disabilities [3].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%