Measurement of Brain Activity Rate Based on Electroensefalographical Signals on Smokers

Indonesia as a country with 255,182,144 people (Central Bureau of Statistics, 2015) with the number of smokers reached 46.16 percent in the third rank with the largest number of smokers in the world after China and India, there are some elements in cigarettes one of them is nicotine, it will be one of the addictive additives. It\u27s why smokers want to continue smoking cigarettes on a regular basis. It bounds to the brain receptors and in other organs.  Increased activity in the orbitofrontal area of the cortex occurs when a smoker wants a cigarette, while in the prefrontal cortex experiences an increase in activity when smokers smoke cigarettes. Increased activity in the area will produce electrically along the scalp that can be measured using Electroencephalography (EEG). The voltage difference of the ion current will give the addiction level information to the cigarette so it can be classified. Classification is done in 4 classes namely low dependence, Moderate dependence and high dependence. From the results of the study we found that the level of brain activity of the frontal cortex increased. 52% of the 40 sampled data showed the highest increase in activity by reaching 53.17% in the highly dependent addiction category

Search Process as Transitions Between Neural States

Search is one of the most performed activities on the World Wide Web. Various conceptual models postulate that the search process can be broken down into distinct emotional and cognitive states of searchers while they engage in a search process. These models significantly contribute to our understanding of the search process. However, they are typically based on self-report measures, such as surveys, questionnaire, etc. and therefore, only indirectly monitor the brain activity that supports such a process. With this work, we take one step further and directly measure the brain activity involved in a search process. To do so, we break down a search process into five time periods: a realisation of Information Need, Query Formulation, Query Submission, Relevance Judgment and Satisfaction Judgment. We then investigate the brain activity between these time periods. Using functional Magnetic Resonance Imaging (fMRI), we monitored the brain activity of twenty-four participants during a search process that involved answering questions carefully selected from the TREC-8 and TREC 2001 Q/A Tracks. This novel analysis that focuses on transitions rather than states reveals the contrasting brain activity between time periods – which enables the identification of the distinct parts of the search process as the user moves through them. This work, therefore, provides an important first step in representing the search process based on the transitions between neural states. Discovering more precisely how brain activity relates to different parts of the search process will enable the development of brain-computer interactions that better support search and search interactions, which we believe our study and conclusions advance

Brain activity during different throwing games: EEG exploratory study

Producción CientíficaThe purpose of this study is to explore the differences in brain activity in various types of throwing games by making encephalographic records. Three conditions of throwing games were compared looking for significant differences (simple throwing, throwing to a goal, and simultaneous throwing with another player). After signal processing, power spectral densities were compared through variance analysis (p ≤ 0.001). Significant differences were found especially in high-beta oscillations (22–30 Hz). “Goal” and “Simultaneous” throwing conditions show significantly higher values than those shown for throws without opponent. This can be explained by the higher demand for motor control and the higher arousal in competition situations. On the other hand, the high-beta records of the “Goal” condition are significantly higher than those of the “Simultaneous” throwing, which could be understood from the association of the beta waves with decision-making processes. These results support the difference in brain activity during similar games. This has several implications: opening up a path to study the effects of each specific game on brain activity and calling into question the transfer of research findings on animal play to all types of human play

Structure and stimulus familiarity: A study of memory in chess-players with functional magnetic resonance imaging.

A grandmaster and an international chess master were compared with a group of novices in a memory task with chess and non-chess stimuli, varying the structure and familiarity of the stimuli, while functional magnetic resonance images were acquired. The pattern of brain activity in the masters was different from that of the novices. Masters showed no differences in brain activity when different degrees of structure and familiarity where compared; however, novices did show differences in brain activity in such contrasts. The most important differences were found in the contrast of stimulus familiarity with chess positions. In this contrast, there was an extended brain activity in bilateral frontal areas such as the anterior cingulate and the superior, middle, and inferior frontal gyri; furthermore, posterior areas, such as posterior cingulate and cerebellum, showed great bilateral activation. These results strengthen the hypothesis that when performing a domain-specific task, experts activate different brain systems from that of novices. The use of the expertsversus- novices paradigm in brain imaging contributes towards the search for brain systems involved in cognitive processes

Event segmentation and biological motion perception in watching dance

We used a combination of behavioral, computational vision and fMRI methods to examine human brain activity while viewing a 386 s video of a solo Bharatanatyam dance. A computational analysis provided us with a Motion Index (MI) quantifying the silhouette motion of the dancer throughout the dance. A behavioral analysis using 30 naïve observers provided us with the time points where observers were most likely to report event boundaries where one movement segment ended and another began. These behavioral and computational data were used to interpret the brain activity of a different set of 11 naïve observers who viewed the dance video while brain activity was measured using fMRI. Results showed that the Motion Index related to brain activity in a single cluster in the right Inferior Temporal Gyrus (ITG) in the vicinity of the Extrastriate Body Area (EBA). Perception of event boundaries in the video was related to the BA44 region of right Inferior Frontal Gyrus as well as extensive clusters of bilateral activity in the Inferior Occipital Gyrus which extended in the right hemisphere towards the posterior Superior Temporal Sulcus (pSTS)

What Neuroimaging of the Psychedelic State Tells Us about the Mind-Body Problem

Recent neuroimaging studies of the psychedelic state, which have commanded great media attention, are reviewed. They show that psychedelic trances are consistently accompanied by broad reductions in brain activity, despite their experiential richness. This result is at least counterintuitive from the perspective of mainstream physicalism, according to which subjective experience is entirely constituted by brain activity. In this brief analysis, the generic implications of physicalism regarding the relationship between the richness of experience and brain activity levels are rigorously examined from an informational perspective, and then made explicit and unambiguous. These implications are then found to be non-trivial to reconcile with the results of said neuroimaging studies, which highlights the significance of such studies for the mind-body problem and philosophy of mind in general

Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. Therefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly re- lies on the expert experience. In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Specifically, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. Then, we adopt an Autoencoder layer to automatically refine the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the effect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. The experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.Comment: 10 page