CT-Net: an interpretable CNN-Transformer fusion network for fNIRS classification.

Functional near-infrared spectroscopy (fNIRS), an optical neuroimaging technique, has been widely used in the field of brain activity recognition and brain-computer interface. Existing works have proposed deep learning-based algorithms for the fNIRS classification problem. In this paper, a novel approach based on convolutional neural network and Transformer, named CT-Net, is established to guide the deep modeling for the classification of mental arithmetic (MA) tasks. We explore the effect of data representations, and design a temporal-level combination of two raw chromophore signals to improve the data utilization and enrich the feature learning of the model. We evaluate our model on two open-access datasets and achieve the classification accuracy of 98.05% and 77.61%, respectively. Moreover, we explain our model by the gradient-weighted class activation mapping, which presents a high consistent between the contributing value of features learned by the model and the mapping of brain activity in the MA task. The results suggest the feasibility and interpretability of CT-Net for decoding MA tasks.

Information transmission in action video gaming experts: Inferences from the lateralized readiness potential.

Research showed that action real-time strategy gaming (ARSG) experience is related to cognitive and neural plasticity, including visual selective attention and working memory, executive control, and information processing. This study explored the relationship between ARSG experience and information transmission in the auditory channel. Using an auditory, two-choice, go/no-go task and lateralized readiness potential (LRP) as the index to partial information transmission, this study examined information transmission patterns in ARSG experts and amateurs. Results showed that experts had a higher accuracy rate than amateurs. More importantly, experts had a smaller stimulus-locked LRP component (250 - 450 ms) than amateurs on no-go trials, while the response-locked LRP component (0 - 300 ms) on go trials did not differ between groups. Thus, whereas amateurs used an asynchronous information transmission pattern, experts used a reduced asynchronous information transmission pattern or a synchronous pattern where most of processing occurred prior to response execution - an information transmission pattern that supports rapid, error-free performance. Thus, experts and amateurs may use different information transmission patterns in auditory processing. In addition, the information transmission pattern used by experts is typically observed only after long-term auditory training according to past research. This study supports the relationship between ARSG experience and the development of information processing patterns.

Enhancement of Event-Related Desynchronization in Motor Imagery Based on Transcranial Electrical Stimulation.

Due to the individual differences controlling brain-computer interfaces (BCIs), the applicability and accuracy of BCIs based on motor imagery (MI-BCIs) are limited. To improve the performance of BCIs, this article examined the effect of transcranial electrical stimulation (tES) on brain activity during MI. This article designed an experimental paradigm that combines tES and MI and examined the effects of tES based on the measurements of electroencephalogram (EEG) features in MI processing, including the power spectral density (PSD) and dynamic event-related desynchronization (ERD). Finally, we investigated the effect of tES on the accuracy of MI classification using linear discriminant analysis (LDA). The results showed that the ERD of the µ and ß rhythms in the left-hand MI task was enhanced after electrical stimulation with a significant effect in the tDCS group. The average classification accuracy of the transcranial alternating current stimulation (tACS) group and transcranial direct current stimulation (tDCS) group (88.19% and 89.93% respectively) were improved significantly compared to the pre-and pseudo stimulation groups. These findings indicated that tES can improve the performance and applicability of BCI and that tDCS was a potential approach in regulating brain activity and enhancing valid features during noninvasive MI-BCI processing.

[Potential risks for transmission of schistosomiasis caused by mobile population in Shanghai].

OBJECTIVE: To understand the potential risk for schistosomiasis transmission caused by introduction of infection source from mobile population in Shanghai. METHODS: Field investigation was conducted in the suburb of Shanghai City by screening the mobile population living in Shanghai for more than 1 month and over 1 years old in a procedure of interviewing, serum indirect hemagglutination (IHA) test, and then fecal examination to detect the eggs with nylon sedimentation approach for those IHA positives. RESULTS: Among 2,931 mobile people investigated, 1,575 were male (53.74%) and 1356 were female(46.26%); 138 out of 2931 were positive in IHA test (4.71%). 1938 (66.12%) out of 2 931 came from Schistosoma japonicum-endemic provinces and its positive rate in mobile population (5.99%) was significantly higher than those from the transmission-interrupted provinces (2.6%) (chi2=10.28, P<0.01), and those from non-endemic provinces (1.68%) (chi2=12.86, P<0.01). The 138 IHA positives all showed negative in fecal examination. In accordance with the serum positive rate and egg-infection rate in the national reporting system in 2004, it was estimated that there would be about 13 356 and 1 699 potential serum positive cases respectively from endemic area and transmission controlled area, and about 2,168 and 255 egg-positive cases from the two kind areas respectively, majority of the cases were from Anhui Province. CONCLUSION: Schistosomiasis transmission risks potentially exist in Shanghai suburb due to the introduction of infected mobile people from other endemic provinces, and a surveillance system and quick response are needed for the possible re-emergence of the disease.

[Chemical constituents in volatile oil from the flos of Dendranthema indicum var. aromaticum var. nov].

OBJECTIVE: To analyse chemical constituents of volatile oil from Dendranthema indicum var. aromaticum var. nov. by GC-MS. METHOD: The volatile oil was extracted from Dendranthema indicum var. aromaticum var. nov. through steam distillation and was analysed with different kinds of capillary columns to find out the optimal conditions. The content of compositions of volatile oil was determined with normalization method, and the constituents were identified by GC-MS. RESULT: 44 Components were separated and identified, which accounted for over 43% of total volatile oil. CONCLUSION: The main constituents in the essential oils from Dendranthema indicum var. aromaticum var. nov. are Verberol, (-)zingiberene, beta-sesquiphellandrene, farnesene, transchrysanthenyl acetate, and caryophyllene.