VR sickness measurement with EEG using DNN algorithm

Jeong, Dae Kyo; Yoo, Seong-Joon; Jang, Yun

doi:10.1145/3281505.3283387

Cited by 13 publications

(18 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The final list of four papers [26][27][28][29] included in this review were closely scrutinized and encoded to capture a variety of attributes in relation to experimental procedures, data preprocessing steps, and DL architecture choices. Table 1 provides a detailed summary of these papers.…”

Section: Resultsmentioning

confidence: 99%

“…Lastly, one paper was a review paper reporting no empirical data. This screening resulted in a final list of four papers that meet the eligibility criteria [26][27][28][29]. Therefore, the current review focused on these four papers.…”

Section: Screeningmentioning

confidence: 99%

“…[26] used 44 short videos depicting an assortment of urban and astrospace scenes, all of which authors noted included visual motion. Similarly, the videos used by [27], [28] and [29] also included visual motion. In line with prior cybersickness research, roller coaster scenes were a popular choice in these previous studies [2][3][4].…”

Section: ) Vr Environmentsmentioning

confidence: 99%

“…Given that such an attempt requires automatic detection of cybersickness on the basis of patterns observed in EEG signals, traditional machine learning algorithms (e.g., Support Vector Machines, Naïve Bayes, and k-Nearest Neighbors) have been extensively applied to the analysis of EEG data for automatic cybersickness detection [20][21][22][23][24][25]. Moreover, recent advances in deep learning (DL) algorithms, which are aimed at learning representations from data in successive layers of processing in neural networks, and increasing availability of computational resources for DL have paved the way for the application of DL frameworks to EEG-based detection of cybersickness [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Review of Deep Learning Approaches to EEG-Based Classification of Cybersickness in Virtual Reality

Yıldırım

2020

2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

View full text Add to dashboard Cite

Cybersickness is an unpleasant side effect of exposure to a virtual reality (VR) experience and refers to such physiological repercussions as nausea and dizziness triggered in response to VR exposure. Given the debilitating effect of cybersickness on the user experience in VR, academic interest in the automatic detection of cybersickness from physiological measurements has crested in recent years. Electroencephalography (EEG) has been extensively used to capture changes in electrical activity in the brain and to automatically classify cybersickness from brainwaves using a variety of machine learning algorithms. Recent advances in deep learning (DL) algorithms and increasing availability of computational resources for DL have paved the way for a new area of research into the application of DL frameworks to EEGbased detection of cybersickness. Accordingly, this review involved a systematic review of the peer-reviewed papers concerned with the application of DL frameworks to the classification of cybersickness from EEG signals. The relevant literature was identified through exhaustive database searches, and the papers were scrutinized with respect to experimental protocols for data collection, data preprocessing, and DL architectures. The review revealed a limited number of studies in this nascent area of research and showed that the DL frameworks reported in these studies (i.e., DNN, CNN, and RNN) could classify cybersickness with an average accuracy rate of 93%. This review provides a summary of the trends and issues in the application of DL frameworks to the EEG-based detection of cybersickness, with some guidelines for future research.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Screeningmentioning

confidence: 99%

Section: ) Vr Environmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Deep Learning Approaches to EEG-Based Classification of Cybersickness in Virtual Reality

Yıldırım

2020

2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

View full text Add to dashboard Cite

show abstract

“…The development of artificial intelligence technology has paved the way for varied improvements to virtual agents in virtual environments through active research [1]. Particularly, virtual agents are controlled in games by following predefined rules [2,3].…”

Section: Introductionmentioning

confidence: 99%

Enhanced DQN Framework for Selecting Actions and Updating Replay Memory Considering Massive Non-Executable Actions

Sung

2021

Applied Sciences

View full text Add to dashboard Cite

A Deep-Q-Network (DQN) controls a virtual agent as the level of a player using only screenshots as inputs. Replay memory selects a limited number of experience replays according to an arbitrary batch size and updates them using the associated Q-function. Hence, relatively fewer experience replays of different states are utilized when the number of states is fixed and the state of the randomly selected transitions becomes identical or similar. The DQN may not be applicable in some environments where it is necessary to perform the learning process using more experience replays than is required by the limited batch size. In addition, because it is unknown whether each action can be executed, a problem of an increasing amount of repetitive learning occurs as more non-executable actions are selected. In this study, an enhanced DQN framework is proposed to resolve the batch size problem and reduce the learning time of a DQN in an environment with numerous non-executable actions. In the proposed framework, non-executable actions are filtered to reduce the number of selectable actions to identify the optimal action for the current state. The proposed method was validated in Gomoku, a strategy board game, in which the application of a traditional DQN would be difficult.

show abstract

SACA Net: Cybersickness Assessment of Individual Viewers for VR Content via Graph-Based Symptom Relation Embedding

Lee

Kim

et al. 2020

Computer Vision – ECCV 2020

View full text Add to dashboard Cite

VR sickness measurement with EEG using DNN algorithm

Cited by 13 publications

References 3 publications

A Review of Deep Learning Approaches to EEG-Based Classification of Cybersickness in Virtual Reality

A Review of Deep Learning Approaches to EEG-Based Classification of Cybersickness in Virtual Reality

Enhanced DQN Framework for Selecting Actions and Updating Replay Memory Considering Massive Non-Executable Actions

SACA Net: Cybersickness Assessment of Individual Viewers for VR Content via Graph-Based Symptom Relation Embedding

Contact Info

Product

Resources

About