Decoding subjective emotional arousal from eeg during an immersive virtual reality experience

Immersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining experimental control, but dynamic and interactive stimuli pose methodological challenges. We here probed the link between emotional arousal, a fundamental property of affective experience, and parieto-occipital alpha power under naturalistic stimulation: 37 young healthy adults completed an immersive VR experience, which included rollercoaster rides, while their EEG was recorded. They then continuously rated their subjective emotional arousal while viewing a replay of their experience. The association between emotional arousal and parieto-occipital alpha power was tested and confirmed by (1) decomposing the continuous EEG signal while maximizing the comodulation between alpha power and arousal ratings and by (2) decoding periods of high and low arousal with discriminative common spatial patterns and a Long Short-Term Memory recurrent neural network. We successfully combine EEG and a naturalistic immersive VR experience to extend previous findings on the neurophysiology of emotional arousal towards real-world neuroscience.

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Decoding subjective emotional arousal from EEG during an immersive Virtual Reality experience

10.1101/2020.10.24.353722 ◽

2020 ◽

Cited By ~ 1

Author(s):

Simon M. Hofmann ◽

Felix Klotzsche ◽

Alberto Mariola ◽

Vadim V. Nikulin ◽

Arno Villringer ◽

...

Keyword(s):

Virtual Reality ◽

Short Term Memory ◽

Fundamental Property ◽

Emotional Arousal ◽

Alpha Power ◽

Immersive Virtual Reality ◽

Eeg Signal ◽

Short Term ◽

Continuous Eeg ◽

Long Short Term Memory

AbstractImmersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining experimental control, but dynamic and interactive stimuli pose methodological challenges. We here probed the link between emotional arousal, a fundamental property of affective experience, and parieto-occipital alpha power under naturalistic stimulation:37 young healthy adults completed an immersive VR experience, which included rollercoaster rides, while their EEG was recorded. They then continuously rated their subjective emotional arousal while viewing a replay of their experience. The association between emotional arousal and parieto-occipital alpha power was tested and confirmed by (1) decomposing the continuous EEG signal while maximizing the comodulation between alpha power and arousal ratings and by (2) decoding periods of high and low arousal with discriminative common spatial patterns and a Long Short-Term Memory recurrent neural network.We successfully combine EEG and a naturalistic immersive VR experience to extend previous findings on the neurophysiology of emotional arousal towards real-world neuroscience.

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BI - Directional long short-term memory for automatic detection of sleep apnea events based on single channel EEG signal

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2022.105211 ◽

2022 ◽

pp. 105211

Author(s):

Yao Wang ◽

Zhuangwen Xiao ◽

Shuaiwen Fang ◽

Weiming Li ◽

Jinhai Wang ◽

...

Keyword(s):

Sleep Apnea ◽

Short Term Memory ◽

Single Channel ◽

Automatic Detection ◽

Eeg Signal ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Deep long short term memory based minimum variance kernel random vector functional link network for epileptic EEG signal classification

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2021.104426 ◽

2021 ◽

Vol 105 ◽

pp. 104426

Author(s):

Sebamai Parija ◽

Ranjeeta Bisoi ◽

P.K. Dash ◽

Mrutyunjaya Sahani

Keyword(s):

Random Vector ◽

Short Term Memory ◽

Minimum Variance ◽

Signal Classification ◽

Eeg Signal ◽

Short Term ◽

Functional Link ◽

Term Memory ◽

Long Short Term Memory ◽

Eeg Signal Classification

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Prediction of Depression from EEG Signal Using Long Short Term Memory(LSTM)

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) ◽

10.1109/icoei.2019.8862560 ◽

2019 ◽

Cited By ~ 6

Author(s):

S.Dhananjay Kumar ◽

DP Subha

Keyword(s):

Short Term Memory ◽

Eeg Signal ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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