Motor imagery brain‐computer interfaces (BCIs) have demonstrated great potential and attract world‐spread attentions. Due to the nonstationary character of the motor imagery signals, costly and boring calibration sessions must be proceeded before use. This prevents them from going into our realistic life. In this paper, the source subject’s data are explored to perform calibration for target subjects. Model trained on source subjects is transferred to work for target subjects, in which the critical problem to handle is the distribution shift. It is found that the performance of classification would be bad when only the marginal distributions of source and target are made closer, since the discriminative directions of the source and target domains may still be much different. In order to solve the problem, our idea comes that joint distribution adaptation is indispensable. It makes the classifier trained in the source domain perform well in the target domain. Specifically, a measure for joint distribution discrepancy (JDD) between the source and target is proposed. Experiments demonstrate that it can align source and target data according to the class they belong to. It has a direct relationship with classification accuracy and works well for transferring. Secondly, a deep neural network with joint distribution matching for zero‐training motor imagery BCI is proposed. It explores both marginal and joint distribution adaptation to alleviate distribution discrepancy across subjects and obtain effective and generalized features in an aligned common space. Visualizations of intermediate layers illustrate how and why the network works well. Experiments on the two datasets prove the effectiveness and strength compared to outstanding counterparts.

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.

Common Spatial Pattern (CSP) has been proved to be one of the most efficient featureextracting methods for Brain Computer Interfaces (BCI), especially for motor imagery BCI. However, CSP is a supervised method and performs poorly when there are not enough labeled data. This paper aims to construct a minimum-training BCI, which means there are only a few labeled data, even none labeled data for target subjects. Under this condition, conventional CSP cannot work well. Therefore, source data (related labeled data from other subjects) are exploited and common filters across subjects are obtained using a clustering method. After that features are extracted and a semi-supervised support vector machine which transfers knowledge across subjects is proposed. Experiments illustrate the effectiveness of our algorithm. When there are none labeled data for target subjects, our algorithm outperforms two state of art algorithms in semi-supervised learning field, and as the amount of unlabeled data for target subjects become larger, the performance of our algorithm grows better and better, which is suitable for online use. When the amount of labeled data for target subjects (denoted as M in this paper) is small, our algorithm also shows its strength compared with corresponding outstanding algorithms. For dataset IVa of BCI competition III, our algorithm performs the best for all the subjects excluding ‘aw’ when M=20. Compared with counterparts, our method outperforms 6.6% for ‘aa’, 19.3% for ‘al’, 11.8% for ‘av’, 9.7% for ‘ay’ on average, respectively. When M=40, our method performs the best for ‘al’, ‘av’. It averagely outperforms 8.0% for ‘al’, 15.3% for ‘av’, respectively. For GigaDataset, averagely our method outperforms 4.4% for ‘sbj2’, 5.0% for ‘sbj4’, 7.1% for ‘sbj5’ when M=20, respectively. When M=40, our algorithm performs the best only for ‘sbj5’. It outperforms 8.6% averagely compared with counterparts. Although performances of our method are not the best for all the conditions, its performances are very robust and competitive.

Many IoT applications require users to share their devices’ location, and enhanced privacy-protection means sharing location anonymously, unlinkably and without relying on any administrators. But under such protection, it is difficult to trust shared location data, which may be from unregistered devices or from the same one’s multiple logins or from the cloned device ID, even be generated by an attacker without any devices! Such untrusted location sharing cheats system, misleads users, even attacks system. To the best of our knowledge, such problems have not been solved in a decentralized system. To solve them in one scheme, we put forward the first decentralized accumulator for device registration and construct the first practical decentralized anonymous authentication for device login. When logging in, the device provides a special knowledge proof, which integrates zero-knowledge (for privacy) with knowledge-leakage (for identifying abnormal behaviors) designing for blockchain (for decentralization). Therefore, in our system, only registered IoT devices can upload location data and their logins are anonymous and unlinkable, while login exceeding K times in a system period or cloning ID to login concurrently can be identified and tracked without any trusted centers. In addition, we provide the security proofs and the application examples of the proposed scheme. And the efficiency analysis and experimental data show that the performance of our scheme can meet the needs of real-world location sharing on IoT.