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

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...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 21; H. 15; S. 5019
Hauptverfasser: Chen, Yeou-Jiunn, Chen, Pei-Chung, Chen, Shih-Chung, Wu, Chung-Min
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 23.07.2021
MDPI
Schlagworte:
Amyotrophic lateral sclerosis
Back propagation
brain computer interface
Communication
deep neural network
denoising autoencoder
Electroencephalography
Interfaces
Neural networks
Noise
noise suppression
Performance evaluation
steady state visually evoked potential
ISSN:1424-8220, 1424-8220
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Zusammenfassung: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s21155019