Analyzing brain activity in understanding cultural and language interaction for depression and anxiety

Human brain has always been considered as a black box and is the source of all emotions. Analyzing cultural and language role through human emotion by looking at the brain activity can thus help us understand depression and stress better. This paper focuses on understanding and analyzing undergraduate students’ emotions with different background and culture after completing their semester final examination. Brain wave signals were captured using EEG device and analyzed through proposing an affective computation model. EEG signal was collected from 8 healthy subjects from different states of Malaysia with different dialects where each subject was emotionally induced with audio and video emotion stimuli using the International Affective Pictures and System (IAPS). Features were extracted from the captured EEG signals using Kernel Density Estimation (KDE), which was then categorized into four basic emotions of happy, calm, sad and fear using the Multi-layer Perceptron (MLP). Results of the study show potential of using such analysis in understanding stress, anxiety and depression

Exposure to diesel exhaust induces changes in EEG in human volunteers

Background: Ambient particulate matter and nanoparticles have been shown to translocate to the brain, and potentially influence the central nervous system. No data are available whether this may lead to functional changes in the brain. Methods: We exposed 10 human volunteers to dilute diesel exhaust (DE, 300 μg/m3) as a model for ambient PM exposure and filtered air for one hour using a double blind randomized crossover design. Brain activity was monitored during and for one hour following each exposure using quantitative electroencephalography (QEEG) at 8 different sites on the scalp. The frequency spectrum of the EEG signals was used to calculate the median power frequency (MPF) and specific frequency bands of the QEEG. Results: Our data demonstrate a significant increase in MPF in response to DE in the frontal cortex within 30 min into exposure. The increase in MPF is primarily caused by an increase in fast wave activity (β2) and continues to rise during the 1 hour post-exposure interval. Conclusion: This study is the first to show a functional effect of DE exposure in the human brain, indicating a general cortical stress response. Further studies are required to determine whether this effect is mediated by the nanoparticles in DE and to define the precise pathways involved

Nanoparticles and Neurotoxicity

Humans are exposed to nanoparticles (NPs; diameter < 100 nm) from ambient air and certain workplaces. There are two main types of NPs; combustion-derived NPs (e.g., particulate matters, diesel exhaust particles, welding fumes) and manufactured or engineered NPs (e.g., titanium dioxide, carbon black, carbon nanotubes, silver, zinc oxide, copper oxide). Recently, there have been increasing reports indicating that inhaled NPs can reach the brain and may be associated with neurodegeneration. It is necessary to evaluate the potential toxic effects of NPs on brain because most of the neurobehavioral disorders may be of environmental origin. This review highlights studies on both combustion-derived NP- and manufactured or engineered NP-induced neuroinflammation, oxidative stress, and gene expression, as well as the possible mechanism of these effects in animal models and in humans

The Causal Inference of Cortical Neural Networks during Music Improvisations

In this paper, we present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and "let-go" mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional mutual information from mixed embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain neural networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and "let-go" mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found musicians responding differently to listeners when playing music in different conditions.Comment: 22 pages, 9 figures. The version was a revised in accordance with referee's comments. The language was also improve

Degree centrality contrasts between strict mode and “let-go” mode in the second experiment.

<p>In this figure, the red stems and the blue stems indicate the in and out degree centrality contrasts between strict mode and “let-go” mode, respectively. The horizontal axis has 9 channels represent the 8 electrodes: P4, T8, C4, F4, F3, C3, T7, P3 and the overall average over the 8 electrodes, while the vertical axis gives the magnitudes of the degree centrality contrasts between strict mode and “let-go” mode.</p

Cross-brain networks for the two music improvisation experiments.

<p>The left graph is for the first experiment, while the right graph is for the second experiment. The red links represent the direction of cross-brain information flow, while the thickness of the links is proportional to the strength of the cross-brain weights (i.e. the average cross-brain causalities).</p

Degree centrality contrasts, or difference, between composed music and improvisation in the second experiment.

<p>In this figure, the red stems and the blue stems indicate the in and out degree centrality contrasts between composed music and improvisation, respectively. The horizontal axis has 9 channels represent the 8 electrodes: P4, T8, C4, F4, F3, C3, T7, P3 and the overall average over the 8 electrodes, while the vertical axis gives the magnitudes of the degree centrality contrasts between composed music and improvisation.</p

The cross-brain weights between flutist and listener in the second experiment.

<p>This figure plots the cross-brain causalities between flutist and listener against time windows for piece A: Ibert, strict mode. The red curve indicates flutistlistener, the blue curve represents listenerflutist, while the black curve is the significance threshold.</p

Listener's intra-brain neural networks for the first experiment.

<p>The two panels show the listener's intra-brain neural networks separately for composed music (left) and improvisation (right). The large brain regions are labeled by the 8 electrodes: F3, F4, C3, C4, T7, T8, P3, P4. The red links indicate the direction of neural information flow between large brain regions, where the thickness of the links represents the magnitudes of the causalities.</p