Independent Components of EEG Activity Correlating with Emotional State

Maruyama, Yasuhisa; Ogata, Yuji; Martinez-Tejada, Laura Alejandra; Koike, Yasuharu; Yoshimura, Natsue

doi:10.3390/brainsci10100669

Cited by 6 publications

(5 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We imported the basic 3D model into Twinmotion to produce the 360° 3D images to be implemented on the VR device. Twinmotion is a real-time immersive 3D architecture visualization solution for producing high-quality images [ 67 ]. We set up the daylight coming through the window at 13 o’clock.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…The duration of the exposure of the EEG stimuli varies across authors. Yasuhisa et al (2020) exposed each image stimulus for 6 seconds [ 67 ], whereas Wei-Yin et al (2019) presented 60 photos at random for 20 seconds [ 68 ]. Agnieszka et al (2018) presented 36 images at random for 10 seconds [ 41 ].…”

Section: Experiments and Analysismentioning

confidence: 99%

“…on the VR device. Twinmotion is a real-time immersive 3D architecture visualization solution for producing high-quality images [67]. We set up the daylight coming through the window at 13 o'clock.…”

Section: Production Of Visual Stimuli In Vr Spacementioning

confidence: 99%

See 2 more Smart Citations

Effects of Changes to Architectural Elements on Human Relaxation-Arousal Responses: Based on VR and EEG

Kim

Park

Choo

2021

IJERPH

View full text Add to dashboard Cite

This study combines electroencephalogram (EEG) with virtual reality (VR) technologies to measure the EEG responses of users experiencing changes to architectural elements. We analyze the ratio of alpha to beta waves (RAB) indicators to determine the pre- and poststimulation changes. In our methodology, thirty-three females experience using private rooms in a postpartum care center participated in the experiment. Their brain waves are measured while they are experiencing the VR space of a private room in a postpartum care center. Three architectural elements (i.e., aspect ratio of space, ceiling height, and window ratio) are varied in the VR space. In addition, a self-report questionnaire is administered to examine whether the responses are consistent with the results of the EEG response analysis. As a result, statistically significant differences (p < 0.05) are observed in the changes in the RAB indicator values of the pre- and poststimulation EEG while the subjects are experiencing the VR space where the architectural elements are varied. That is, the effects of the changes to architectural elements on users’ relaxation-arousal responses are statistically verified. Notably, in all the RAB indicator values where significant differences are observed, the poststimulation RAB decreases in comparison to the prestimulus ratios, which is indicative of the arousal response. However, the arousal levels vary across the architectural elements, which implies it would be possible to find out the elements that could induce less arousal response using the proposed method. Moreover, following the experience in the VR space, certain lobes of the brain (F4 and P3 EEG channels) show statistically significant differences in the relaxation-arousal responses. Unlike previous studies, which measured users’ physiological responses to abstract and primordial spatial elements, this study extends the boundaries of the literature by applying the architectural elements applicable to design in practice.

show abstract

Section: Experiments and Analysismentioning

confidence: 99%

Section: Experiments and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Effects of Changes to Architectural Elements on Human Relaxation-Arousal Responses: Based on VR and EEG

Kim

Park

Choo

2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…There are also many variants of CSP, such as Common Space Spectral Pattern (CSSP), Filter Bank CSP (FBCSP), etc. (Park et al, 2018;Maruyama et al, 2020;Kumar et al, 2021) (Al-Fahoum and Al-Fraihat, 2014). Wavelet Packet Decomposition (WPD), a modification of WT, further decomposes the high-frequency sub-bands and provides better frequency resolution.…”

Section: Feature Extraction By Conventional Algorithmsmentioning

confidence: 99%

“…There are also many variants of CSP, such as Common Space Spectral Pattern (CSSP), Filter Bank CSP (FBCSP), etc. (Park et al, 2018 ; Maruyama et al, 2020 ; Kumar et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Survey on the research direction of EEG-based signal processing

Sun

Mou

2023

Front. Neurosci.

View full text Add to dashboard Cite

Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI) systems due to its portability and simplicity. In this paper, we provide a comprehensive review of research on EEG signal processing techniques since 2021, with a focus on preprocessing, feature extraction, and classification methods. We analyzed 61 research articles retrieved from academic search engines, including CNKI, PubMed, Nature, IEEE Xplore, and Science Direct. For preprocessing, we focus on innovatively proposed preprocessing methods, channel selection, and data augmentation. Data augmentation is classified into conventional methods (sliding windows, segmentation and recombination, and noise injection) and deep learning methods [Generative Adversarial Networks (GAN) and Variation AutoEncoder (VAE)]. We also pay attention to the application of deep learning, and multi-method fusion approaches, including both conventional algorithm fusion and fusion between conventional algorithms and deep learning. Our analysis identifies 35 (57.4%), 18 (29.5%), and 37 (60.7%) studies in the directions of preprocessing, feature extraction, and classification, respectively. We find that preprocessing methods have become widely used in EEG classification (96.7% of reviewed papers) and comparative experiments have been conducted in some studies to validate preprocessing. We also discussed the adoption of channel selection and data augmentation and concluded several mentionable matters about data augmentation. Furthermore, deep learning methods have shown great promise in EEG classification, with Convolutional Neural Networks (CNNs) being the main structure of deep neural networks (92.3% of deep learning papers). We summarize and analyze several innovative neural networks, including CNNs and multi-structure fusion. However, we also identified several problems and limitations of current deep learning techniques in EEG classification, including inappropriate input, low cross-subject accuracy, unbalanced between parameters and time costs, and a lack of interpretability. Finally, we highlight the emerging trend of multi-method fusion approaches (49.2% of reviewed papers) and analyze the data and some examples. We also provide insights into some challenges of multi-method fusion. Our review lays a foundation for future studies to improve EEG classification performance.

show abstract