Decoding the coupling between the brain and skin reactions in auditory stimulation by information-based analysis of EEG and GSR signals

BACKGROUND: The analysis of brain activity in different conditions is an important research area in neuroscience. OBJECTIVE: This paper analyzed the correlation between the brain and skin activities in rest and stimulations by information-based analysis of electroencephalogram (EEG) and galvanic skin resistance (GSR) signals. METHODS: We recorded EEG and GSR signals of eleven subjects during rest and auditory stimulations using three pieces of music that were differentiated based on their complexity. Then, we calculated the Shannon entropy of these signals to quantify their information contents. RESULTS: The results showed that music with greater complexity has a more significant effect on altering the information contents of EEG and GSR signals. We also found a strong correlation (r= 0.9682) among the variations of the information contents of EEG and GSR signals. Therefore, the activities of the skin and brain are correlated in different conditions. CONCLUSION: This analysis technique can be utilized to evaluate the correlation among the activities of various organs versus brain activity in different conditions.

Beyond body maps: information content of specific body parts is distributed across the somatosensory Homunculus

The somatosensory homunculus in primary somatosensory cortex (S1) is topographically organised, with relatively high selectivity to each body part in its primary area. This dominant feature may eclipse other organising principles in S1. Recent multivariate methodologies allow us to identify representational features beyond selectivity, e.g., information content, providing new opportunities to characterise the homunculus. Using Representational Similarity Analysis, we asked whether body part information content can be identified in S1 beyond the primary area of a given body part. Representational dissimilarities in fMRI activity patterns were compared between different body parts (face, hand and feet) and subparts (e.g., fingers), and between different actions performed with the same body part. Throughout the S1 homunculus, we identified significant dissimilarities between non-primary body parts (e.g., between the hand and the lips in the foot area). We also observed significant dissimilarities between body subparts in distant non-primary areas (e.g., different face parts in the foot area). Finally, we could significantly dissociate between two movements performed by one body part (e.g., the hand) well beyond its primary area (e.g., in the foot and face areas), even when focusing the analysis along the most topographically organised sub-region of S1, Brodmann area 3b. Altogether, our results demonstrate that body part and action-related information content is more distributed across S1 homunculus than previously considered. While this finding does not revoke the general topographic organising principle of S1, it reveals yet unexplored underlying information contents that could be harnessed for rehabilitation, as well as novel brain-machine interfaces.

A machine learning based exploration of COVID-19 mortality risk

Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invasive laboratory and noninvasive clinical and demographic data from patients’ day of admission. Three Support Vector Machine (SVM) models were developed and compared using invasive, non-invasive, and both groups. The results suggested that non-invasive features could provide mortality predictions that are similar to the invasive and roughly on par with the joint model. Feature inspection results from SVM-RFE and sparsity analysis displayed that, compared with the invasive model, the non-invasive model can provide better performances with a fewer number of features, pointing to the presence of high predictive information contents in several non-invasive features, including SPO2, age, and cardiovascular disorders. Furthermore, while the invasive model was able to provide better mortality predictions for the imminent future, non-invasive features displayed better performance for more distant expiration intervals. Early mortality prediction using non-invasive models can give us insights as to where and with whom to intervene. Combined with novel technologies, such as wireless wearable devices, these models can create powerful frameworks for various medical assignments and patient triage.

Categorical Exploratory Data Analysis: From Multiclass Classification and Response Manifold Analytics Perspectives of Baseball Pitching Dynamics

All features of any data type are universally equipped with categorical nature revealed through histograms. A contingency table framed by two histograms affords directional and mutual associations based on rescaled conditional Shannon entropies for any feature-pair. The heatmap of the mutual association matrix of all features becomes a roadmap showing which features are highly associative with which features. We develop our data analysis paradigm called categorical exploratory data analysis (CEDA) with this heatmap as a foundation. CEDA is demonstrated to provide new resolutions for two topics: multiclass classification (MCC) with one single categorical response variable and response manifold analytics (RMA) with multiple response variables. We compute visible and explainable information contents with multiscale and heterogeneous deterministic and stochastic structures in both topics. MCC involves all feature-group specific mixing geometries of labeled high-dimensional point-clouds. Upon each identified feature-group, we devise an indirect distance measure, a robust label embedding tree (LET), and a series of tree-based binary competitions to discover and present asymmetric mixing geometries. Then, a chain of complementary feature-groups offers a collection of mixing geometric pattern-categories with multiple perspective views. RMA studies a system’s regulating principles via multiple dimensional manifolds jointly constituted by targeted multiple response features and selected major covariate features. This manifold is marked with categorical localities reflecting major effects. Diverse minor effects are checked and identified across all localities for heterogeneity. Both MCC and RMA information contents are computed for data’s information content with predictive inferences as by-products. We illustrate CEDA developments via Iris data and demonstrate its applications on data taken from the PITCHf/x database.

Analysis of the Correlation between Static Visual Stimuli, Eye Movements, and Brain Signals

Analysis of the correlation among the activities of the eyes and brain is an important research area in physiological science. In this paper, we analyzed the correlation between the reactions of eyes and the brain during rest and while watching different visual stimuli. Since every external stimulus transfers information to the human brain, and on the other hand, eye movements and EEG signals contain information, we utilized Shannon entropy to evaluate the coupling between them. In the experiment, 10 subjects looked at 4 images with different information contents while we recorded their EEG signals and eye movements simultaneously. According to the results, the information contents of eye fluctuations, EEG signals, and visual stimuli are coupled, which reflect the coupling between the brain and eye activities. Similar analyses could be performed to evaluate the correlation among the activities of other organs versus the brain.

Genetic Diversity and Population Structures in Chinese Miniature Pigs Revealed by SINE Retrotransposon Insertion Polymorphisms, a New Type of Genetic Markers

RIPs have been developed as effective genetic markers and popularly applied for genetic analysis in plants, but few reports are available for domestic animals. Here, we established 30 new molecular markers based on the SINE RIPs, and applied them for population genetic analysis in seven Chinese miniature pigs. The data revealed that the closed herd (BM-clo), inbreeding herd (BM-inb) of Bama miniature pigs were distinctly different from the BM-cov herds in the conservation farm, and other miniature pigs (Wuzhishan, Congjiang Xiang, Tibetan, and Mingguang small ear). These later five miniature pig breeds can further be classified into two clades based on a phylogenetic tree: one included BM-cov and Wuzhishan, the other included Congjiang Xiang, Tibetan, and Mingguang small ear, which was well-supported by structure analysis. The polymorphic information contents estimated by using SINE RIPs are lower than the predictions based on microsatellites. Overall, the genetic distances and breed-relationships between these populations revealed by 30 SINE RIPs generally agree with their evolutions and geographic distributions. We demonstrated the potential of SINE RIPs as new genetic markers for genetic monitoring and population structure analysis in pigs, which can even be extended to other livestock animals.

Information-based Analysis of the Coupling between Dynamic Visual Stimuli, Eye Movements, and Brain Signals

Our eyes are always in search of exploring our surrounding environment. The brain controls our eyes’ activities through the nervous system. Hence, analyzing the correlation between the activities of the eyes and brain is an important area of research in vision science. This paper evaluates the coupling between the reactions of the eyes and the brain in response to different moving visual stimuli. Since both eye movements and EEG signals (as the indicator of brain activity) contain information, we employed Shannon entropy to decode the coupling between them. Ten subjects looked at four moving objects (dynamic visual stimuli) with different information contents while we recorded their EEG signals and eye movements. The results demonstrated that the changes in the information contents of eye movements and EEG signals are strongly correlated ([Formula: see text]), which indicates a strong correlation between brain and eye activities. This analysis could be extended to evaluate the correlation between the activities of other organs versus the brain.

Information-Based Analysis of the Coupling Between the Alterations of Heart and Brain Activities in Response to Auditory Stimuli

In this work, we investigated the coupling among the activities of the brain and heart versus the changes in auditory stimuli using information-based analysis. Three music were selected based on the difference in their complexity. We applied these auditory stimuli on 11 subjects, and accordingly, computed and compared the Shannon entropy of electroencephalography (EEG) signals and heart rate variability (R–R time series). The results demonstrated a correlation among the alterations of the information contents of EEG signals and R–R time series. This finding shows the coupling between the activities of the brain and heart. This analysis could be expanded to analyze the activities of other organs versus the brain’s reaction in various conditions.