Few-Electrode EEG from the Wearable Devices Using Domain Adaptation for Depression Detection.
Few-Electrode EEG from the Wearable Devices Using Domain Adaptation for Depression Detection.
复制标题
使用域自适应检测抑郁症的可穿戴设备的少电极脑电。
DOI：
10.3390/bios12121087
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发表时间：
2022-11-28
期刊：
影响因子：
5.4
通讯作者：
Zhao, Xianghong
中科院分区：
文献类型：
--
作者：
Wu, Wei;Ma, Longhua;Lian, Bin;Cai, Weiming;Zhao, Xianghong
Nowadays, major depressive disorder (MDD) has become a crucial mental disease that endangers human health. Good results have been achieved by electroencephalogram (EEG) signals in the detection of depression. However, EEG signals are time-varying, and the distributions of the different subjects’ data are non-uniform, which poses a bad influence on depression detection. In this paper, the deep learning method with domain adaptation is applied to detect depression based on EEG signals. Firstly, the EEG signals are preprocessed and then transformed into pictures by two methods: the first one is to present the three channels of EEG separately in the same image, and the second one is the RGB synthesis of the three channels of EEG. Finally, the training and prediction are performed in the domain adaptation model. The results indicate that the domain adaptation model can effectively extract EEG features and obtain an average accuracy of 77.0 ± 9.7%. This paper proves that the domain adaptation method can effectively weaken the inherent differences of EEG signals, making the diagnosis of different users more accurate.
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DOI：
10.1109/tpami.2018.2814042
发表时间：
2019-04-01
影响因子：
23.6
作者：
Rozantsev, Artem;Salzmann, Mathieu;Fua, Pascal
通讯作者：
Fua, Pascal
影响因子：
2.8
作者：
Olbrich, Sebastian;Arns, Martijn
通讯作者：
Arns, Martijn
影响因子：
3.3
作者：
Loh, Hui Wen;Ooi, Chui Ping;Acharya, U. Rajendra
通讯作者：
Acharya, U. Rajendra
影响因子：
6.6
作者：
Daly, Michael;Sutin, Angelina R.;Robinson, Eric
通讯作者：
Robinson, Eric
DOI：
10.3390/e24020211
发表时间：
2022-01-28
期刊：
Entropy (Basel, Switzerland)
影响因子：
--
作者：
Avots E;Jermakovs K;Bachmann M;Päeske L;Ozcinar C;Anbarjafari G
通讯作者：
Anbarjafari G