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| Research Advancements of Deep Learning on EEG Decoding |
| Liu Zheng1, He Feng1#, Tang Jiabei1, Wan Baikun1, Ming Dong1,2#* |
1(College of Precision Instruments ＆ Optoelectronics Engineering, Tianjin University, Tianjin 300072, China)
2(Academy of Medical Engineering and Translational Medicine, Tianjin University,Tianjin 300072,China) |
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Abstract Electroencephalography (EEG) has millisecond time resolution, which can acquire real-time neurophysiology information of brain activity. EEG has been widely used in cerebral imaging and has become an important tool in neuroscience and neuroengineering in 21st century. However, the original signal-to-noise ratio (SNR) and spatial resolution are poor and the decoding is seriously hindered due to the volume condution effect. With the great development of Deep Learning (DL) in this century, researchers have been trying to combine the two to explore the application of deep learning in EEG data processing, and leading to some phase achievements. Nevertheless, there are still challenges in applying the current DNNs to EEG data processing. Combining with recent studies on DNN-based EEG data processing, this paper introduced the implementation of deep EEG decoding and discussed existing problems and future directions.
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Received: 29 April 2019
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Corresponding Authors:
*E-mail: richardming@tju.edu.cn
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| About author:: #Member,Chinese Society of Biomedical Engineering |
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