The effect of beta/alpha neurofeedback training on imitating brain activity patterns in visual artists

Sho’ouri, Nasrin; Firoozabadi, Mohammad; Badie, Kambiz

doi:10.1016/j.bspc.2019.101661

Cited by 18 publications

(17 citation statements)

References 66 publications

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“…Whenever relative low beta in a subject was higher than the threshold, the image was changed, their points were added, and an auditory feedback was heard. 37,51,60…”

Section: Description Of Proposed Training Methodsmentioning

confidence: 99%

“…At each period of time; the points added to each subject for each success, intensity of auditory feedback, and dimensions of visual feedback frame were determined according to the SI. 51,60…”

Section: Description Of Proposed Training Methodsmentioning

confidence: 99%

“…As a result, this method cannot inform subjects regarding their success rate and it may also confuse them in their evaluations. 51,60–63…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Whenever relative low beta in a subject was higher than the threshold, the image was changed, their points were added, and an auditory feedback was heard. 37,51,60…”

Section: Description Of Proposed Training Methodsmentioning

confidence: 99%

Section: Description Of Proposed Training Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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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“…EEG is the most widely used technique in the neuroscientific study of meditation. EEG signals are decomposed into five frequency bands, including delta (1-4Hz), theta (5-8 Hz), alpha (8-12 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-70 Hz). Several meditation studies report the importance of theta and alpha waves for enhancing attention and are associated with several cognitive processes [5].…”

Section: Cognitive Relevancementioning

confidence: 99%

“…1. Previous studies on neurofeedback have shown promising results to enhance performance, including athletes (archers improve their shooting performance), musicians, professional dancers, and non-artists to attain skills resembling visual artists [27]. NeuroFeedback requires a timeefficient and high-performing computational technique in terms to classify the different stages of brain responses.…”

Section: Cognitive Relevancementioning

confidence: 99%

BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings

Pandey¹,

Miyapuram²

2021

Preprint

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Several Convolutional Deep Learning models have been proposed to classify the cognitive states utilizing several neuro-imaging domains. These models have achieved significant results, but they are heavily designed with millions of parameters, which increases train and test time, making the model complex and less suitable for real-time analysis. This paper proposes a simple, lightweight CNN model to classify cognitive states from Electroencephalograph (EEG) recordings. We develop a novel pipeline to learn distinct cognitive representation consisting of two stages. The first stage is to generate the 2D spectral images from neural time series signals in a particular frequency band. Images are generated to preserve the relationship between the neighboring electrodes and the spectral property of the cognitive events. The second is to develop a time-efficient, computationally less loaded, and high-performing model. We design a network containing 4 blocks and major components include standard and depth-wise convolution for increasing the performance and followed by separable convolution to decrease the number of parameters which maintains the tradeoff between time and performance. We experiment on open access EEG meditation dataset comprising expert, nonexpert meditative, and control states. We compare performance with six commonly used machine learning classifiers and four state of the art deep learning models. We attain comparable performance utilizing less than 4% of the parameters of other models. This model can be employed in a real-time computation environment such as neurofeedback.

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BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings

Pandey

Miyapuram

2021

Medical Image Understanding and Analysis

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The effect of beta/alpha neurofeedback training on imitating brain activity patterns in visual artists

Cited by 18 publications

References 66 publications

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

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

BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings

BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings

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