In electroencephalography (EEG)-based emotion recognition systems, the distribution between the training samples and the testing samples may be mismatched if they are sampled from different experimental sessions or subjects because of user fatigue, different electrode placements, varying impedances, etc. Therefore, it is difficult to directly classify the EEG patterns with a conventional classifier. The domain adaptation method, which is aimed at obtaining a common representation across training and test domains, is an effective method for reducing the distribution discrepancy. However, the existing domain adaptation strategies either employ a linear transformation or learn the nonlinearity mapping without a consistency constraint; they are not sufficiently powerful to obtain a similar distribution from highly non-stationary EEG signals. To address this problem, in this paper, a novel component, called the subspace alignment auto-encoder (SAAE), is proposed. Taking advantage of both nonlinear transformation and a consistency constraint, we combine an auto-encoder network and a subspace alignment solution in a unified framework. As a result, the source domain can be aligned with the target domain together with its class label, and any supervised method can be applied to the new source domain to train a classifier for classification in the target domain, as the aligned source domain follows a distribution similar to that of the target domain. We compared our SAAE method with six typical approaches using a public EEG dataset containing three affective states: positive, neutral, and negative. Subject-to-subject and session-to-session evaluations were performed. The subject-to-subject experimental results demonstrate that our component achieves a mean accuracy of 77.88% in comparison with a state-of-the-art method, TCA, which achieves 73.82% on average. In addition, the average classification accuracy of SAAE in the session-to-session evaluation for all the 15 subjects in a dataset is 81.81%, an improvement of up to 1.62% on average as compared to the best baseline TCA. The experimental results show the effectiveness of the proposed method relative to state-of-the-art methods. It can be concluded that SAAE is a useful and effective tool for decreasing domain discrepancy and reducing performance degradation across subjects and sessions in the EEG-based emotion recognition field.

Electroencephalography (EEG)-based emotion recognition is an important element in psychiatric health diagnosis for patients. However, the underlying EEG sensor signals are always non-stationary if they are sampled from different experimental sessions or subjects. This results in the deterioration of the classification performance. Domain adaptation methods offer an effective way to reduce the discrepancy of marginal distribution. However, for EEG sensor signals, both marginal and conditional distributions may be mismatched. In addition, the existing domain adaptation strategies always require a high level of additional computation. To address this problem, a novel strategy named adaptive subspace feature matching (ASFM) is proposed in this paper in order to integrate both the marginal and conditional distributions within a unified framework (without any labeled samples from target subjects). Specifically, we develop a linear transformation function which matches the marginal distributions of the source and target subspaces without a regularization term. This significantly decreases the time complexity of our domain adaptation procedure. As a result, both marginal and conditional distribution discrepancies between the source domain and unlabeled target domain can be reduced, and logistic regression (LR) can be applied to the new source domain in order to train a classifier for use in the target domain, since the aligned source domain follows a distribution which is similar to that of the target domain. We compare our ASFM method with six typical approaches using a public EEG dataset with three affective states: positive, neutral, and negative. Both offline and online evaluations were performed. The subject-to-subject offline experimental results demonstrate that our component achieves a mean accuracy and standard deviation of 80.46% and 6.84%, respectively, as compared with a state-of-the-art method, the subspace alignment auto-encoder (SAAE), which achieves values of 77.88% and 7.33% on average, respectively. For the online analysis, the average classification accuracy and standard deviation of ASFM in the subject-to-subject evaluation for all the 15 subjects in a dataset was 75.11% and 7.65%, respectively, gaining a significant performance improvement compared to the best baseline LR which achieves 56.38% and 7.48%, respectively. The experimental results confirm the effectiveness of the proposed method relative to state-of-the-art methods. Moreover, computational efficiency of the proposed ASFM method is much better than standard domain adaptation; if the numbers of training samples and test samples are controlled within certain range, it is suitable for real-time classification. It can be concluded that ASFM is a useful and effective tool for decreasing domain discrepancy and reducing performance degradation across subjects and sessions in the field of EEG-based emotion recognition.

BackgroundMicrobial extracellular electron transfer (EET) is essential in driving the microbial interspecies interaction and redox reactions in bioelectrochemical systems (BESs). Magnetite (Fe3O4) and magnetic fields (MFs) were recently reported to promote microbial EET, but the mechanisms of MFs stimulation of EET and current generation in BESs are not known. This study investigates the behavior of current generation and EET in a state-of-the-art pulse electromagnetic field (PEMF)-assisted magnetic BES (PEMF-MBES), which was equipped with magnetic carbon particle (Fe3O4@N-mC)-coated electrodes. Illumina Miseq sequencing of 16S rRNA gene amplicons was also conducted to reveal the changes of microbial communities and interactions on the anode in response to magnetic field.ResultsPEMF had significant influences on current generation. When reactors were operated in microbial fuel cell (MFC) mode with pulse electromagnetic field (PEMF-MMFCs), power densities increased by 25.3–36.0% compared with no PEMF control MFCs (PEMF-OFF-MMFCs). More interestingly, when PEMF was removed, the power density dropped by 25.7%, while when PEMF was reintroduced, the value was restored to the previous level. Illumina sequencing of 16S rRNA gene amplicon and principal component analysis (PCA) based on operational taxonomic units (OTUs) indicate that PEMFs led to the shifts in microbial community and changes in species evenness that decreased biofilm microbial diversity. Geobacter spp. were found dominant in all anode biofilms, but the relative abundance in PEMF-MMFCs (86.1–90.0%) was higher than in PEMF-OFF-MMFCs (82.5–82.7%), indicating that the magnetic field enriched Geobacter on the anode. The current generation of Geobacter-inoculated microbial electrolysis cells (MECs) presented the same change regularity, the accordingly increase or decrease corresponding with switch of PEMF, which confirmed the reversible stimulation of PEMFs on microbial electron transfer.ConclusionThe pulse electromagnetic field (PEMF) showed significant influence on state-of-the-art pulse magnetic bioelectrochemical systems (PEMF-MBES) in terms of current generation and microbial ecology. EET was instantaneously and reversibly enhanced in MBESs inoculated with either mixed-culture or Geobacter. PEMF notably decreased bacterial and archaeal diversities of the anode biofilms in MMFCs via changing species evenness rather than species richness, and facilitated specific enrichment of exoelectrogenic bacteria (Geobacter) on the anode surface. This study demonstrates a new magnetic approach for understanding and facilitating microbial electrochemical activities. Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-017-0929-3) contains supplementary material, which is available to authorized users.

Respiratory diseases including apnea are often accompanied by abnormal respiratory depth, frequency, and rhythm. If different abnormal respiratory patterns can be detected and recorded, with their depth, frequency, and rhythm analyzed, the detection and diagnosis of respiratory diseases can be achieved. High-frequency millimeter-wave radar (76–81 GHz) has low environmental impact, high accuracy, and small volume, which is more suitable for respiratory signal detection and recognition compared with other contact equipment. In this paper, the experimental platform of frequency-modulated continuous wave (FMCW) radar was built at first, realizing the noncontact measurement of vital signs. Secondly, the energy intensity and threshold of respiration signal during each period were calculated by using the rectangular window, and the accurate judgment of apnea was realized via numerical comparison. Thirdly, the features of respiratory and heart rate signals, the number of peaks and valleys, the difference between peaks and valleys, the average and the standard deviation of normalized short-term energy, and the average and the standard deviation and the minimum of instantaneous frequency, were extracted and analyzed. Finally, support vector machine (SVM) and K-nearest neighbor (KNN) were used to classify the extracted features, and the accuracy was 98.25% and 88.75%, respectively. The classification and recognition of respiratory patterns have been successfully realized.