Feature learning from incomplete EEG with denoising autoencoder
An alternative pathway for the human brain to communicate with the outside world is by means of a brain computer interface (BCI). A BCI can decode electroencephalogram (EEG) signals of brain activities, and then send a command or an intent to an external interactive device, such as a wheelchair. The...
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| Vydáno v: | Neurocomputing (Amsterdam) Ročník 165; s. 23 - 31 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.10.2015
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | An alternative pathway for the human brain to communicate with the outside world is by means of a brain computer interface (BCI). A BCI can decode electroencephalogram (EEG) signals of brain activities, and then send a command or an intent to an external interactive device, such as a wheelchair. The effectiveness of the BCI depends on the performance in decoding the EEG. Usually, the EEG is contaminated by different kinds of artefacts (e.g., electromyogram (EMG), background activity), which leads to a low decoding performance. A number of filtering methods can be utilized to remove or weaken the effects of artefacts, but they generally fail when the EEG contains extreme artefacts. In such cases, the most common approach is to discard the whole data segment containing extreme artefacts. This causes the fatal drawback that the BCI cannot output decoding results during that time. In order to solve this problem, we employ the Lomb–Scargle periodogram to estimate the spectral power from incomplete EEG (after removing only parts contaminated by artefacts), and Denoising Autoencoder (DAE) for learning. The proposed method is evaluated with motor imagery EEG data. The results show that our method can successfully decode incomplete EEG to good effect. |
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| AbstractList | An alternative pathway for the human brain to communicate with the outside world is by means of a brain computer interface (BCI). A BCI can decode electroencephalogram (EEG) signals of brain activities, and then send a command or an intent to an external interactive device, such as a wheelchair. The effectiveness of the BCI depends on the performance in decoding the EEG. Usually, the EEG is contaminated by different kinds of artefacts (e.g., electromyogram (EMG), background activity), which leads to a low decoding performance. A number of filtering methods can be utilized to remove or weaken the effects of artefacts, but they generally fail when the EEG contains extreme artefacts. In such cases, the most common approach is to discard the whole data segment containing extreme artefacts. This causes the fatal drawback that the BCI cannot output decoding results during that time. In order to solve this problem, we employ the Lomb–Scargle periodogram to estimate the spectral power from incomplete EEG (after removing only parts contaminated by artefacts), and Denoising Autoencoder (DAE) for learning. The proposed method is evaluated with motor imagery EEG data. The results show that our method can successfully decode incomplete EEG to good effect. |
| Author | Zhang, Liqing Cichocki, Andrzej Li, Junhua Struzik, Zbigniew |
| Author_xml | – sequence: 1 givenname: Junhua surname: Li fullname: Li, Junhua email: juhalee.bcmi@gmail.com organization: Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Saitama 351-0198, Japan – sequence: 2 givenname: Zbigniew surname: Struzik fullname: Struzik, Zbigniew organization: Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Saitama 351-0198, Japan – sequence: 3 givenname: Liqing surname: Zhang fullname: Zhang, Liqing organization: Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China – sequence: 4 givenname: Andrzej surname: Cichocki fullname: Cichocki, Andrzej organization: Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Saitama 351-0198, Japan |
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| Keywords | Spectral power estimation Denoising autoencoder Brain computer interface Motor imagery Incomplete EEG |
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