EEG topographical maps analysis for 2D and 3D video game play

Melek

Sakarya University Journal of Computer and Information Sciences

2020

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.

Section: Introductionmentioning

confidence: 99%

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

Melek

Sakarya University Journal of Computer and Information Sciences

2020

“…The complexity level has increased in the eye opened and 3D mode. In [16] by considering the power of neuronal oscillations and coherence, an increase in the power of the lower frequency at the frontal and occipital regions in 3D and the higher power of the upper frequency at temporal lobes for 2D were explored. Electric power of the brain waves as the result of watching the 2D, 2.5D and 3D motion pictures and also the comparison of these modes for alpha and gamma bands were done in [17].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Analysis of 2D&3D Video Watching of EEG Signals by Increasing PLSR and SVM Classification Results

2019

The Computer Journal

Despite the development of two- and three-dimensional (2D&3D) technology, it has attracted the attention of researchers in recent years. This research is done to reveal the detailed effects of 2D in comparison with 3D technology on the human brain waves. The impact of 2D&3D video watching using electroencephalography (EEG) brain signals is studied. A group of eight healthy volunteers with the average age of 31 ± 3.06 years old participated in this three-stage test. EEG signal recording consisted of three stages: After a bit of relaxation (a), a 2D video was displayed (b), the recording of the signal continued for a short period of time as rest (c), and finally the trial ended. Exactly the same steps were repeated for the 3D video. Power spectrum density (PSD) based on short time Fourier transform (STFT) was used to analyze the brain signals of 2D&3D video viewers. After testing all the EEG frequency bands, delta and theta were extracted as the features. Partial least squares regression (PLSR) and Support vector machine (SVM) classification algorithms were considered in order to classify EEG signals obtained as the result of 2D&3D video watching. Successful classification results were obtained by selecting the correct combinations of effective channels representing the brain regions.

“…Multidimensional technologies have come a long way from 2D to 3D technologies. Although the 2D technology has remained on the market for a long time, technological advances have improved the 3D technology to the extent that it is used in multiple applications such as the 3D printing industry, entertainment [11], healthcare [12], [13], defense [14], aerospace [15], industry, manufacturing, and architecture [16]. Entertainment has been the most dominant practice in the market and is expected to continue to steer the market within the stipulated period.…”

mentioning

confidence: 99%

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

Melek

2020

Preprint

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.