EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System.

PURPOSE: Anxiety disorder is one of the psychiatric disorders that involves extreme fear or worry, which can change the balance of chemicals in the brain. To the best of our knowledge, the evaluation of anxiety state is still based on some subjective questionnaires and there is no objective standard assessment yet. Unlike other methods, our approach focuses on study the neural changes to identify and classify the anxiety state using electroencephalography (EEG) signals. METHODS: We designed a closed neurofeedback experiment that contains three experimental stages to adjust subjects' mental state. The EEG resting state signal was recorded from thirty-four subjects in the first and third stages while EEG-based mindfulness recording was recorded in the second stage. At the end of each stage, the subjects were asked to fill a Visual Analogue Scale (VAS). According to their VAS score, the subjects were classified into three groups: non-anxiety, moderate or severe anxiety groups. RESULTS: After processing the EEG data of each group, support vector machine (SVM) classifiers were able to classify and identify two mental states (non-anxiety and anxiety) using the Power Spectral Density (PSD) as patterns. The highest classification accuracies using Gaussian kernel function and polynomial kernel function are 92.48 ±  1.20% and 88.60  ±  1.32%, respectively. The highest average of the classification accuracies for healthy subjects is 95.31 ±  1.97% and for anxiety subjects is 87.18 ±  3.51%. CONCLUSIONS: The results suggest that our proposed EEG neurofeedback-based classification approach is efficient for developing affective BCI system for detection and evaluation of anxiety disorder states.

Purification and identification of cell surface antigens using lamprey monoclonal antibodies.

Variable lymphocyte receptor (VLR) B antibodies of the evolutionary distant sea lamprey are structurally distinct from conventional mammalian antibodies. The different protein architecture and large evolutionary distance of jawless vertebrates suggest that VLR antibodies may represent promising tools for biomarker discovery. Here we report the generation of panels of monoclonal VLR antibodies from lamprey larvae immunized with human T cells and the use of a recombinant monoclonal VLR antibody for antigen purification and mass spectrometric identification. We demonstrate that despite predicted low affinity of individual VLR antigen binding units to the antigen, the high avidity resulting from decameric assembly of secreted VLR antibodies allows for efficient antigen capture and subsequent identification by mass spectometry. We show that VLR antibodies detect their antigens with high specificity and can be used in various standard laboratory application techniques. The lamprey antibodies are novel reagents that can complement conventional monoclonal antibodies in multiple scientific research disciplines.