OBJECTIVE: In this research, electroencephalography (EEG) as the feature of brain activity and voice signals were simultaneously analyzed.
METHOD: For this purpose, we changed the activity of the human brain by applying different odours and simultaneously recorded their voices and EEG signals while they read a text. For the analysis, we used the fractal theory that deals with the complexity of objects. The fractal dimension of EEG signal versus voice signal in different levels of brain activity were computed and analyzed.
RESULTS: The results indicate that the activity of human voice is related to brain activity, where the variations of the complexity of EEG signal are linked to the variations of the complexity of voice signal. In addition, the EEG and voice signal complexities are related to the molecular complexity of applied odours.
CONCLUSION: The employed method of analysis in this research can be widely applied to other physiological signals in order to relate the activities of different organs of human such as the heart to the activity of his brain.
OBJECTIVE: The aim of this study was to analyze the effect of a virtual reality condition on students' learning ability and physiological state.
METHODS: Students were shown 6 sets of videos (3 videos in a two-dimensional condition and 3 videos in a three-dimensional condition), and their learning ability was analyzed based on a subsequent questionnaire. In addition, we analyzed the reaction of the brain and facial muscles of the students during both the two-dimensional and three-dimensional viewing conditions and used fractal theory to investigate their attention to the videos.
RESULTS: The learning ability of students was increased in the three-dimensional condition compared to that in the two-dimensional condition. In addition, analysis of physiological signals showed that students paid more attention to the three-dimensional videos.
CONCLUSIONS: A virtual reality condition has a greater effect on enhancing the learning ability of students. The analytical approach of this study can be further extended to evaluate other physiological signals of subjects in a virtual reality condition.
OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis.
METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18-22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked.
RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566).
CONCLUSION: We conclude that heart and brain activities are related.
OBJECTIVE: In this research, we investigated the correction between heart activation and the variations of walking paths.
METHOD: We employed Shannon entropy to analyze how the information content of walking paths affects the information content of HRV. Eight healthy students walked on three designed walking paths with different information contents while we recorded their ECG signals. We computed and analyzed the Shannon entropy of the R-R interval time series (as an indicator of HRV) versus the Shannon entropy of different walking paths and accordingly evaluated their relation.
RESULTS: According to the obtained results, walking on the path that contains more information leads to less information in the R-R time series.
CONCLUSION: The analysis method employed in this research can be extended to analyze the relation between other physiological signals (such as brain or muscle reactions) and the walking path.
METHOD: For this purpose, we employ fractal theory and analyze the variations of fractal dimension of GSR and EEG signals when subjects are exposed to different olfactory stimuli in the form of pleasant odors.
RESULTS: Based on the obtained results, the complexity of GSR signal changes with the complexity of EEG signal in case of different stimuli, where by increasing the molecular complexity of olfactory stimuli, the complexity of EEG and GSR signals increases. The results of statistical analysis showed the significant effect of stimulation on variations of complexity of GSR signal. In addition, based on effect size analysis, fourth odor with greatest molecular complexity had the greatest effect on variations of complexity of EEG and GSR signals.
CONCLUSION: Therefore, it can be said that human skin reaction changes with the variations in the activity of human brain. The result of analysis in this research can be further used to make a model between the activities of human skin and brain that will enable us to predict skin reaction to different stimuli.