Common spatial pattern (CSP) has been proved to be one of the most efficient feature-extracting methods for brain-computer interfaces (BCIs), 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. The experiments illustrate the effectiveness of our algorithm. When there are none labeled data for target subjects, our algorithm outperforms two state-of-the-art algorithms in semi-supervised learning field, and as the amount of unlabeled data for target subjects becomes 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,'' and 9.7% for ''ay'' on average, respectively. When M = 40, our method performs the best for ''al'' and ''av''. It averagely outperforms 8.0% for ''al'' and 15.3% for ''av,'' respectively. For GigaDataset, averagely our method outperforms 4.4% for ''sbj2,'' 5.0% for ''sbj4,'' and 7.1% for ''sbj5'' when M = 20, respectively. When M = 40, our algorithm performs the best only for ''sbj5.'' It averagely outperforms 8.6% compared with counterparts. Although the performances of our method are not the best for all the conditions, its performances are very robust and competitive.
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.
In this paper, a simple-structured and high-performance current-mode logic (CML) ternary D flip-flop based on BiCMOS is proposed. It combines both advantages of BiCMOS and CML circuits, which is with much more high-speed, strong-drive and anti-interference abilities. Utilizing TSMC 180 nm process, results of simulations carried out by HSPICE illustrate the proposed circuit not only has correct logic function, but also gains improvements of 95.6~98.4% in average D-Q delay and 16.2%~70.4 in PDP compared with advanced ternary D flip-flop. When compared at the same information transmission speed, proposed circuit is more competitive. Furthermore, it can perform up to high frequency of 15 GHz and drive heavier load. All the results prove that proposed circuit is high-performance and very suitable for high-speed and high-frequency applications.
Dual supply voltage scheme provides very effective solution to cut down power consumption in digital integrated circuits design, where level converting flip–flops (LCFF) are the key component circuits. In this paper, a new general structure and design method for dual-edge triggered LCFF based on BiCMOS is proposed, according to that PNP-PNP-DELCFF and NPN-NPN-DELCFF are designed. The experiments carried out by Hspice using TSMC 180 nm show proposed circuits have correct logic functions. Compared to counterparts, proposed PNP-PNP-DELCFF gains improvements of 6.7%, 96.0%, 86.0% and 28.5% in D-Q Delay, 50.0%, 16.0%, 12.6% and 10.8% in product of delay and power (PDP), respectively. NPN-NPN-DELCFF gains improvements of 5.1%, 93.0%, 83.2% and 26.5% in D-Q Delay, 39.7%, 7.9%, 5.0% and 3.4% in PDP, respectively. Furthermore, proposed circuits have better drive ability.
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