Volume 19, Issue 3 (12-2022)
JSDP 2022, 19(3): 163-174 |
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Dorvashi M, Behzadfar N, Shahgholian G. Detection of Fatigue from Electroencephalogram Signal During Neurofeedback Training. JSDP 2022; 19 (3) : 11
URL: http://jsdp.rcisp.ac.ir/article-1-1140-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1140-en.html
Islamic Azad University
Abstract: (1172 Views)
Timely diagnosis of fatigue helps to improve the quality and effectiveness of neurofeedback training. Neurofeedback training (NFT) is a method that can change brain activity by altering brain signal fluctuations and teaches individuals to produce or reproduce their brain activity patterns in order to improve performance. Neurofeedback training has been widely utilized over the recent years owing to its considerable effect on the cognitive processes. Fatigue during NFT is one of factors affecting the functioning and achievement of NFT which results in decreased learning ability. Timely diagnosis of fatigue during NFT preserves quality of NFT.
Decreased learning ability reduces individuals' motivation for learning during NFT. In this paper, 12 participants` electroencephalogram signals were investigated to detect fatigue during NFT. Two training protocols named protocol 1 and 2 have been designed to improve working memory. Each protocol includes 6 participants and 10 training sessions that each session takes three 10-minute training intervals. Training features in protocol 1 are increased in power of lower2 alpha frequency band in OZ channel and permutation entropy reduction in FZ channel, while protocol 2`s training feature is increased in power of lower2 alpha frequency band in OZ channel. Occurrence of fatigue during NFT changes trend of training features. Changing of training features slope will decrease or become opposite to the goal of NFT. Therefore, examining trend of training features slope is a novel approach in detection of fatigue during NFT. During the occurrence of fatigue, in protocol 1, trend of power of lower2 alpha frequency band`s slope in the OZ channel is decreasing and the trend of entropy` s slope in the FZ channel is increasing. Consequently, the trend of score`s slope is also decreasing. Also in the protocol 2, the trend of power of lower 2 alpha frequency band`s slope in the OZ channel and score is decreasing. This shows that training features do not change in line with the neurofeedback`s goal. Fatigue was detected for 3 subjects in the protocol 1 and 1 subject in the protocol 2. Occurrence of fatigue was less in protocol 1 compared with protocol 2 since Protocol 1 `s training features are combination of frequency and non-frequency features, while the Protocol 2 `s training feature is only frequency feature. Detection of fatigue during NFT is an essential issue which contributes to increase in the effect of training and participants` performance.
Decreased learning ability reduces individuals' motivation for learning during NFT. In this paper, 12 participants` electroencephalogram signals were investigated to detect fatigue during NFT. Two training protocols named protocol 1 and 2 have been designed to improve working memory. Each protocol includes 6 participants and 10 training sessions that each session takes three 10-minute training intervals. Training features in protocol 1 are increased in power of lower2 alpha frequency band in OZ channel and permutation entropy reduction in FZ channel, while protocol 2`s training feature is increased in power of lower2 alpha frequency band in OZ channel. Occurrence of fatigue during NFT changes trend of training features. Changing of training features slope will decrease or become opposite to the goal of NFT. Therefore, examining trend of training features slope is a novel approach in detection of fatigue during NFT. During the occurrence of fatigue, in protocol 1, trend of power of lower2 alpha frequency band`s slope in the OZ channel is decreasing and the trend of entropy` s slope in the FZ channel is increasing. Consequently, the trend of score`s slope is also decreasing. Also in the protocol 2, the trend of power of lower 2 alpha frequency band`s slope in the OZ channel and score is decreasing. This shows that training features do not change in line with the neurofeedback`s goal. Fatigue was detected for 3 subjects in the protocol 1 and 1 subject in the protocol 2. Occurrence of fatigue was less in protocol 1 compared with protocol 2 since Protocol 1 `s training features are combination of frequency and non-frequency features, while the Protocol 2 `s training feature is only frequency feature. Detection of fatigue during NFT is an essential issue which contributes to increase in the effect of training and participants` performance.
Article number: 11
Type of Study: Research |
Subject:
Paper
Received: 2020/05/4 | Accepted: 2021/09/6 | Published: 2022/12/25 | ePublished: 2022/12/25
Received: 2020/05/4 | Accepted: 2021/09/6 | Published: 2022/12/25 | ePublished: 2022/12/25
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