Brain activity: beta wave analysis of 2D and 3D serious games using EEG

Hosťovecký, Marián; Babušiak, Branko

doi:10.1515/jamsi-2017-0008

Cited by 30 publications

(20 citation statements)

References 13 publications

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“…In the present study, datasets of EEG records related to 2D_3D and 3D_2D transitions were obtained with the predetermined scenario. Reviewing the studies on 2D and 3D technology, no detailed quantitative research was found using the channels representing the five lobes of the brain and all EEG bands [47], [8], [9]. The effect of watching 2D and 3D TV on brain signals has been the focus of some studies in terms of qualitative [12], [48].…”

Section: Discussionmentioning

confidence: 99%

Detection of 2D and 3D Video Transitions Based on EEG Power<strong> </strong>

Manshouri

Melek

Kayıkçıoğlu

2020

Preprint

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Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Regarding these transitions, the EEG signal analysis method was used for deep and comprehensive analysis of 2D and 3D brain signals. In this study, a single-stream anaglyph video of random 2D and 3D segments was prepared. After watching this single video, the obtained EEG recordings were considered for two different analyses: the part involving the critical transition (transition-state) and the state analysis of only the 2D versus 3D or 3D versus 2D parts (steady-state). The main objective of this study is to see the behavioral changes of brain signals in 2D and 3D transitions. To clarify the impacts of the human brain’s power spectral density (PSD) in 2D-to-3D (2D_3D) and 3D-to-2D (3D_2D) transitions of anaglyph video, 9 visual healthy individuals were prepared for testing in this pioneering study. Spectrogram graphs based on Short Time Fourier transform (STFT) were considered to evaluate the power spectrum analysis in each EEG channel of transition or steady-state. Thus, in 2D and 3D transition scenarios, important channels representing EEG frequency bands and brain lobes will be identified. To classify the 2D and 3D transitions, the dominant bands and time intervals representing the maximum difference of PSD were selected. Afterward, effective features were selected by applying statistical methods such as standard deviation (SD), maximum (max), and Hjorth parameters to epochs indicating transition intervals. Ultimately, k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The frontal, temporal, and partially parietal lobes show 2D_3D and 3D_2D transitions with a good classification success rate. Overall, it was found that Hjorth parameters and LDA algorithms have 71.11% and 77.78% classification success rates for transition and steady-state, respectively.

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Section: Discussionmentioning

confidence: 99%

Detection of 2D and 3D Video Transitions Based on EEG Power<strong> </strong>

Manshouri

Melek

Kayıkçıoğlu

2020

Preprint

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“…In a video consisting of single-stream 2D_3D and 3D_2D random transitions, the behavior of all brain lobes in the EEG frequency bands was studied in detail. In the literature review in the field of 2D and 3D technology, the analysis was started based on some basic information, e.g., by taking into account only the effectiveness of some brain lobes or frequency bands [41], [17], [9]. However, in the proposed study, the dominant EEG band and brain lobes [9] were selected by analyzing all the criteria.…”

Section: Discussionmentioning

confidence: 99%

The Efficacy of Frontal and Temporal Lobes in Detecting 2D&3D Video Transition Using EEG Power

Manshouri

Melek

Kayıkçıoğlu

2020

Sakarya University Journal of Computer and Information Sciences

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Due to the three-dimensional (3D) structure of the human eye, 3D technology was used in this research. Transition to 2D and 3D analysis is important, claiming that binocular vision will lose dimension during fatigue. Thus, a single-stream video consisting of random 2D&3D parts was watched by nine volunteers. The dynamic behavior and power spectral density (PSD) of the volunteers' brain signals were considered for a comprehensive quantitative analysis. The dominant EEG bands and time intervals were selected in 2D to 3D (2D_3D) and 3D to 2D (3D_2D) transitions by analyzing power differences based on short-time Fourier transformation (STFT). Taking into account this information, applying effective feature extraction and classification techniques, the behavioral patterns of channels representing the brain lobes of the volunteers were analyzed. Hjorth parameters and statistical methods were used as feature extraction methods. The k-nearest neighbors (k-NN) and linear discriminant analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The results revealed that, thanks to the comprehensive classification analysis of the 2D_3D and 3D_2D transitions, the change in the activity power of the brain cortex can be represented. The dominance of the temporal and frontal lobes between the channels representing these transitions cannot be excluded.

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“…En el grupo experimental también existen aumento en el número de correlaciones en el 50% de los sujetos. Las ondas beta indican una alta velocidad de propagación asociados a estados de excitación y alerta (Tiago-Costa et al, 2016), además ciertos rangos de beta se relacionan con aumentos de energía, ansiedad, rendimiento cognitivo y concentración (Hos•ovecký & Babušiak, 2017). El aumento en el número de correlaciones entre los exponentes de Hurst se pueden relacionar con estados más sincronizados, con un trabajo global para sostener la concentración durante un estado de reposo con ojos cerrados.…”

Section: Discussionunclassified

Efectos de una sesión de ejercicio aeróbico sobre las correlaciones no lineales del cerebro en reposo (Effects of an aerobic exercise session on non-linear correlations of resting brain)

Cid

2020

Retos

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En las últimas décadas, se ha estudiado la señal del electroencefalograma como una serie de tiempo con dinámicas no lineales. El objetivo de la presente investigación fue determinar los efectos del ejercicio físico sobre el número de correlaciones de la actividad no lineal de las diversas ondas cerebrales en estado basal. La muestra estuvo constituida por 12 varones voluntarios (seis experimentales y seis controles). El registro de la actividad cerebral se realizó con un dispositivo cerebro-interfaz Emotiv Epoc® mientras los estudiantes permanecían dos minutos sentados, relajados y con los ojos cerrados. Los registros se realizaron antes y después de un trabajo aeróbico de 30 minutos. Los resultados muestran que el número de correlaciones entre los exponentes de Hurst presentan modificaciones similares entre el grupo experimental y control en las ondas delta, beta y gamma. En las ondas theta existe mayor número de modificaciones en el grupo experimental (100% v/s 83.3%), situación más acentuada en alfa (83.3% v/s 16.7%). En conclusión, la intervención con 30 minutos de ejercicio físico aeróbico muestra efectos sobre el número de correlaciones entre los exponentes de Hurst en estado de reposo con los ojos cerrados en ondas theta y alfa. Los efectos son menos notorios o no presentan un patrón discriminativo en las ondas delta, beta y gamma. Abstract. In recent decades, the electroencephalogram signal has been studied as a time series with non-linear dynamics. The aim of this research was to determine the effects of physical exercise on the number of correlations of non-linear activity of the various brain waves at baseline. The sample consisted of 12 male volunteers (six experimental and six controls). The recording of brain activity was performed through a brain-interface device Emotiv Epoc® while the students remain seated, relaxed and with their eyes closed for two minutes. The logs were performed before and after 30-minutes of aerobic exercise. The results show that the number of correlations between the Hurst exponents show similar modifications between the experimental group and control group in the delta, beta and gamma waves. In theta waves there are a greater number of modifications in the experimental group (100% v/s 83.3%), a more pronounced situation in alpha (83.3% v/s 16.7%). In conclusion, the intervention with 30-minutes of aerobic physical exercise shows effects on the number of correlations for theta and alpha waves between the Hurst exponents at rest. The effects are less noticeable or do not show a discriminatory pattern in delta, beta and gamma waves.

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Brain activity: beta wave analysis of 2D and 3D serious games using EEG

Cited by 30 publications

References 13 publications

Detection of 2D and 3D Video Transitions Based on EEG Power<strong> </strong>

Detection of 2D and 3D Video Transitions Based on EEG Power<strong> </strong>

The Efficacy of Frontal and Temporal Lobes in Detecting 2D&3D Video Transition Using EEG Power

Efectos de una sesión de ejercicio aeróbico sobre las correlaciones no lineales del cerebro en reposo (Effects of an aerobic exercise session on non-linear correlations of resting brain)

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