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

Manshouri, Negin; Kayıkçıoğlu, Temel

doi:10.1093/comjnl/bxz043

Cited by 9 publications

(9 citation statements)

References 24 publications

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“…In [12], the researchers claimed that the behavior of brain signals did not change during 2D/3D TV watching. With the opposite result of this study, brain dynamics have been shown to exhibit different behaviors in the brain lobes and EEG frequency bands [49], [48], [5], [10]. Analysis of brain signals by watching 2D and 3D videos individually was done extensively in our previous studies [5], [10].…”

Section: Discussionmentioning

confidence: 96%

“…The configuration pattern of the electrodes is shown in Fig 1. Individuals were asked to sit on a comfortable chair about 85 cm from the TV (LG 32 inch) stand. The features of this television are detailed in [5]. Each EEG recording lasted approximately 135 s. Each test was repeated 5 times.…”

Section: Eeg Recording and Datasetmentioning

confidence: 99%

“…Stereo vision is a normal human vision with some amazing features [5]. The relationship between such vision and brain function has been the subject of many studies [6], [7], [8], [9].…”

mentioning

confidence: 99%

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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: 96%

Section: Eeg Recording and Datasetmentioning

confidence: 99%

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

Manshouri

Melek

Kayıkçıoğlu

2020

Preprint

Self Cite

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“…One of the reasons why binocular vision is so important is that it allows to perceive depth and relationships between objects. EEG analysis of 2D and 3D technology can generally be classified as eye fatigue detection [17], game analysis [18], [19], detection of power spectrum differences in brain waves [20], [8], and investigation of the effects of stereoscopic disparity on event-related potentials [21]. Few of these studies have focused on a brief look analysis of brain signals in 2D and 3D video watching instead of comprehensive analysis, and none of them has examined the 2D and 3D transitions of video watching modes.…”

Section: Introductionmentioning

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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“…Acquiring and analyzing EEG signals are challenging. Various algorithms have been developed to efficiently process the EEG data, such as frequency analysis [3], wavelet transform [4], filter banks [5], hidden Markov models [6], support vector machines [7], and artificial neural networks [8].…”

Section: Introductionmentioning

confidence: 99%

EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks

Taherisadr

Joneidi

Rahnavard

2019

2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)

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A new deep learning-based electroencephalography (EEG) signal analysis framework is proposed. While deep neural networks, specifically convolutional neural networks (CNNs), have gained remarkable attention recently, they still suffer from high dimensionality of the training data. Two-dimensional input images of CNNs are more vulnerable to be redundant versus one-dimensional input time-series of conventional neural networks. In this study, we propose a new dimensionality reduction framework for reducing the dimension of CNN inputs based on the tensor decomposition of the time-frequency representation of EEG signals. The proposed tensor decomposition-based dimensionality reduction algorithm transforms a large set of slices of the input tensor to a concise set of slices which are called super-slices. Employing super-slices not only handles the artifacts and redundancies of the EEG data but also reduces the dimension of the CNNs training inputs. We also consider different timefrequency representation methods for EEG image generation and provide a comprehensive comparison among them. We test our proposed framework on HCB-MIT data and as results show our approach outperforms other previous studies.

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

Cited by 9 publications

References 24 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

EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks

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