Beta Wave Activity Analysis Of EEG During Mental Painting Reflects Influence Of Artistic Expertise

Soltani, Ariana; Vakili, Amir Mohammad; Sho’ouri, Nasrin

doi:10.1109/icbme49163.2019.9030399

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…The reason might be that the beta wave is associated with the normal waking state of consciousness when attention is directed toward cognitive tasks and the outside world [16]. Beta has "fast" activity, present when a person is alert, attentive, engaged in problem-solving, judgment, decision-making, or focused mental activity [17,18]. Furthermore, pattern recognition by the model in the case of combined data was quite difficult than in the case of when the data of each wave was taken individually.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning Approaches for Electroencephalography (EEG)-Based User Response Prediction

Sharma,

Pandey,

Chauhan

et al. 2023

Artificial Intelligence Evolution

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In deep learning, finding the best algorithms for time series data can be challenging due to its stochastic and nonlinear nature. This study endeavours to address the challenges posed by a 10-class classification and binary classification problem employing deep learning algorithms. We collected Electroencephalography (EEG) data from participants engaged in the Corsi Block Tapping Task, utilizing various combinations of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) models across multiple layers to achieve the highest accuracy across different frequency bands - namely, beta band (14-30 Hz), alpha band (8-13 Hz), theta band (4-8 Hz), and delta band (0.5-4.35 Hz). Our findings in the context of the 10-class classification problem highlight the superior performance of the 1 CNN + 4 linear layers model, boasting an accuracy of 64.47%. In the realm of binary classification, the 1 LSTM + 4 linear layers model emerged as the top performer, achieving an impressive accuracy of 93.30%. Notably, the beta wave exhibited enhanced predictive capabilities. These results hold promising implications for the design of brain-computer interface experiments, where specific brain regions can predict responses with heightened accuracy. Furthermore, future applications may encompass the development of cognitive systems where both time and accuracy play pivotal roles.

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Section: Resultsmentioning

confidence: 99%

Deep Learning Approaches for Electroencephalography (EEG)-Based User Response Prediction

Sharma,

Pandey,

Chauhan

et al. 2023

Artificial Intelligence Evolution

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show abstract

“…The diffusion of water vapor without EO did not alter the high alpha wave ( Figure 3 A). Further, we analyzed the high beta (20–30 Hz) wave, which is commonly associated with stressful situations [ 52 , 53 ]. The high beta wave was reduced while breathing both P. menziesii EO and L. angustifolia EO ( Figure 3 B), indicating a reduction in stress level.…”

Section: Resultsmentioning

confidence: 99%

Relaxing Effects of Breathing Pseudotsuga menziesii and Lavandula angustifolia Essential Oils on Psychophysiological Status in Older Adults

Chung

Chen

Lee

et al. 2022

IJERPH

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We evaluated the effects of breathing Pseudotsuga menziesii (P. menziesii) and Lavandula angustifolia (L. angustifolia) essential oils (EOs) during a horticultural activity on older adults. A total number of 92 older adult (71.2 ± 7.7 years old) participants were guided through a leaf printing procedure. In the meantime, water vapor and EOs were diffused in an orderly manner. The heart rate variability-related parameters as well as the brain waves were recorded. In addition, we also collected data for the State–Trait Anxiety Inventory-State (STAI-S) questionnaires before and after the whole indoor natural activity program. The physiological parameters including standard deviation of normal to normal intervals, normalized high frequency (nHF), and high alpha wave increased while the normalized low frequency (nLF), the ratio of LF-to-HF power, high beta wave, and gamma wave decreased following the breathing of P. menziesii and L. angustifolia EOs. These changes indicated a relaxing effect of breathing both EOs during a horticultural activity on older adults. Our results demonstrated a beneficial effect of P. menziesii EO which is as good as a well-known relaxant L. angustifolia EO. This notion was supported by the results of STAI-S. Here we developed an indoor natural activity program for older adults to promote physical and mental health.

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“…In addition, brain activity patterns in individuals skilled in a specific field are different from those in non-skilled and novice ones. 18–39 If people learn to change their brain activity to assimilate them to those in skilled people, acquiring a skill or enhancing a performance will be assumed quicker. Therefore, NF is used to enhance performance in individuals.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, NF is used to enhance performance in individuals. 7,8,36,[40][41][42][43][44][45][46][47][48][49][50][51][52][53] To design an NF training course; how subjects are evaluated and encouraged (scoring method and types of feedback) as well as training protocol need to be determined. All of these factors can also have a significant impact on the effectiveness of NF training.…”

Section: Introductionmentioning

confidence: 99%

Hard Boundary-Based Neurofeedback Training Procedure: A Modified Fixed Thresholding Method for More Accurate Guidance of Subjects Within Target Areas During Neurofeedback Training

Sho’ouri

2022

Clin EEG Neurosci

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In nearly all studies within the domain of neurofeedback, a threshold has been defined for each training feature in a way that subjects’ status can be evaluated during training according to the given value. In this study, a hard boundary-based neurofeedback training (HBNFT) method based on the determination of decision boundary using support vector machine (SVM) classifier was proposed in which subjects’ status were clarified considering a decision boundary and they could also be encouraged once entering a target area. In this method, a scoring index (SI) was similarly defined whose value was determined in accordance with subject performance during training. The results revealed that employing a classifier and determining a decision boundary instead of using a threshold could prove more successful in accurately guiding them towards a target area and also meet no needs to choose a basis for determining a threshold. Moreover, it was likely that the proposed method could be more efficient in controlling features and preventing extreme changes compared to those using variable thresholds.

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Beta Wave Activity Analysis Of EEG During Mental Painting Reflects Influence Of Artistic Expertise

Cited by 5 publications

References 31 publications

Deep Learning Approaches for Electroencephalography (EEG)-Based User Response Prediction

Deep Learning Approaches for Electroencephalography (EEG)-Based User Response Prediction

Relaxing Effects of Breathing Pseudotsuga menziesii and Lavandula angustifolia Essential Oils on Psychophysiological Status in Older Adults

Hard Boundary-Based Neurofeedback Training Procedure: A Modified Fixed Thresholding Method for More Accurate Guidance of Subjects Within Target Areas During Neurofeedback Training

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