Auditory Source Localization by Time Frequency Analysis and Classification of Electroencephalogram Signals

The temporal lobe or auditory cortex in the brain is involved in processing auditory stimuli. The auditory data processing capability in the brain changes as a person ages. In this paper, we use the hrtf method to produce sound in different directions as auditory stimulus. Experiments are conducted with auditory stimulation of human subjects. Electroencephalogram (EEG) recording from the subjects are made during the exposure to the sound. A set of time frequency analysis operators consisting of the cyclic short time Fourier transform and the continuous wavelet transform is applied to the pre-processed EEG signal and a classifier is trained with time-frequency power from training data. The support vector machine classifier is then used for source localization of the sound. The paper also presents results with respect to neuronal regions involved in processing multi source sound information

Graph Convolutional Network Using Adaptive Neighborhood Laplacian Matrix for Hyperspectral Images with Application to Rice Seed Image Classification

Graph convolutional neural network architectures combine feature extraction and convolutional layers for hyperspectral image classification. An adaptive neighborhood aggregation method based on statistical variance integrating the spatial information along with the spectral signature of the pixels is proposed for improving graph convolutional network classification of hyperspectral images. The spatial-spectral information is integrated into the adjacency matrix and processed by a single-layer graph convolutional network. The algorithm employs an adaptive neighborhood selection criteria conditioned by the class it belongs to. Compared to fixed window-based feature extraction, this method proves effective in capturing the spectral and spatial features with variable pixel neighborhood sizes. The experimental results from the Indian Pines, Houston University, and Botswana Hyperion hyperspectral image datasets show that the proposed AN-GCN can significantly improve classification accuracy. For example, the overall accuracy for Houston University data increases from 81.71% (MiniGCN) to 97.88% (AN-GCN). Furthermore, the AN-GCN can classify hyperspectral images of rice seeds exposed to high day and night temperatures, proving its efficacy in discriminating the seeds under increased ambient temperature treatments

BIODIVERSITY ASSESSMENT USING HIERARCHICAL CLUSTERING OVER HYPERSPECTRAL IMAGES

ABSTRACT Hyperspectral images represent an important source of information to assess ecosystem biodiversity. In particular, plant species richness is a primary indicator of biodiversity. This paper aims to use spectral variance to predict vegetation richness, known as Spectral Variation Hypothesis. A hierarchical clustering method based on minimum spanning tree computations retrieve clusters whose Shannon entropy reflects the species richness on a given zone. These entropies correlate well with the ones calculated directly from field data

Detection of Target Genes for Drug Repurposing to Treat Skeletal Muscle Atrophy in Mice Flown in Spaceflight

Skeletal muscle atrophy is a common condition in aging, diabetes, and in long duration spaceflights due to microgravity. This article investigates multi-modal gene disease and disease drug networks via link prediction algorithms to select drugs for repurposing to treat skeletal muscle atrophy. Key target genes that cause muscle atrophy in the left and right extensor digitorum longus muscle tissue, gastrocnemius, quadriceps, and the left and right soleus muscles are detected using graph theoretic network analysis, by mining the transcriptomic datasets collected from mice flown in spaceflight made available by GeneLab. We identified the top muscle atrophy gene regulators by the Pearson correlation and Bayesian Markov blanket method. The gene disease knowledge graph was constructed using the scalable precision medicine knowledge engine. We computed node embeddings, random walk measures from the networks. Graph convolutional networks, graph neural networks, random forest, and gradient boosting methods were trained using the embeddings, network features for predicting links and ranking top gene-disease associations for skeletal muscle atrophy. Drugs were selected and a disease drug knowledge graph was constructed. Link prediction methods were applied to the disease drug networks to identify top ranked drugs for therapeutic treatment of skeletal muscle atrophy. The graph convolution network performs best in link prediction based on receiver operating characteristic curves and prediction accuracies. The key genes involved in skeletal muscle atrophy are associated with metabolic and neurodegenerative diseases. The drugs selected for repurposing using the graph convolution network method were nutrients, corticosteroids, anti-inflammatory medications, and others related to insulin

Assessment of Cognitive Aging Using an SSVEP-Based Brain–Computer Interface System

Cognitive deterioration caused by illness or aging often occurs before symptoms arise, and its timely diagnosis is crucial to reducing its medical, personal, and societal impacts. Brain−computer interfaces (BCIs) stimulate and analyze key cerebral rhythms, enabling reliable cognitive assessment that can accelerate diagnosis. The BCI system presented analyzes steady-state visually evoked potentials (SSVEPs) elicited in subjects of varying age to detect cognitive aging, predict its magnitude, and identify its relationship with SSVEP features (band power and frequency detection accuracy), which were hypothesized to indicate cognitive decline due to aging. The BCI system was tested with subjects of varying age to assess its ability to detect aging-induced cognitive deterioration. Rectangular stimuli flickering at theta, alpha, and beta frequencies were presented to subjects, and frontal and occipital Electroencephalographic (EEG) responses were recorded. These were processed to calculate detection accuracy for each subject and calculate SSVEP band power. A neural network was trained using the features to predict cognitive age. The results showed potential cognitive deterioration through age-related variations in SSVEP features. Frequency detection accuracy declined after age group 20−40, and band power declined throughout all age groups. SSVEPs generated at theta and alpha frequencies, especially 7.5 Hz, were the best indicators of cognitive deterioration. Here, frequency detection accuracy consistently declined after age group 20−40 from an average of 96.64% to 69.23%. The presented system can be used as an effective diagnosis tool for age-related cognitive decline

A Deep Neural Network for Working Memory Load Prediction from EEG Ensemble Empirical Mode Decomposition

Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) are frequently associated with working memory (WM) dysfunction, which is also observed in various neural psychiatric disorders, including depression, schizophrenia, and ADHD. Early detection of WM dysfunction is essential to predict the onset of MCI and AD. Artificial Intelligence (AI)-based algorithms are increasingly used to identify biomarkers for detecting subtle changes in loaded WM. This paper presents an approach using electroencephalograms (EEG), time-frequency signal processing, and a Deep Neural Network (DNN) to predict WM load in normal and MCI-diagnosed subjects. EEG signals were recorded using an EEG cap during working memory tasks, including block tapping and N-back visuospatial interfaces. The data were bandpass-filtered, and independent components analysis was used to select the best electrode channels. The Ensemble Empirical Mode Decomposition (EEMD) algorithm was then applied to the EEG signals to obtain the time-frequency Intrinsic Mode Functions (IMFs). The EEMD and DNN methods perform better than traditional machine learning methods as well as Convolutional Neural Networks (CNN) for the prediction of WM load. Prediction accuracies were consistently higher for both normal and MCI subjects, averaging 97.62%. The average Kappa score for normal subjects was 94.98% and 92.49% for subjects with MCI. Subjects with MCI showed higher values for beta and alpha oscillations in the frontal region than normal subjects. The average power spectral density of the IMFs showed that the IMFs (p = 0.0469 for normal subjects and p = 0.0145 for subjects with MCI) are robust and reliable features for WM load prediction