<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.7//EN" "https://dtd.nlm.nih.gov/ncbi/pubmed/in/PubMed.dtd">
<ArticleSet>
<Article>
<Journal>
				<PublisherName></PublisherName>
				<JournalTitle>Transactions on Machine Intelligence</JournalTitle>
				<Issn>2821-1693</Issn>
				<Volume>6</Volume>
				<Issue>1</Issue>
				<PubDate PubStatus="epublish">
					<Year>2023</Year>
					<Month>03</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Studying The Influences of Visual Neurofeedback Below the Range Of Δ Frequency Band</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1</FirstPage>
			<LastPage>9</LastPage>
			<ELocationID EIdType="pii">181342</ELocationID>
			
<ELocationID EIdType="doi">10.47176/TMI.2023.1</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>R.</FirstName>
					<LastName>Yaghoobi Karimi</LastName>
<Affiliation>Department of Biomedical Engineering, Semnan University, Semnan, Iran</Affiliation>

</Author>
<Author>
					<FirstName>S.</FirstName>
					<LastName>Azadi</LastName>
<Affiliation>Department of Biomedical Engineering, Semnan University, Semnan, Iran</Affiliation>

</Author>
<Author>
					<FirstName>O.</FirstName>
					<LastName>Rahmani Seryasat</LastName>
<Affiliation>Assistant Prof, Department of Electrical Engineering, Shams Higher Education Institute, Gorgan, Iran</Affiliation>
<Identifier Source="ORCID">0000-0002-9289-6128</Identifier>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2022</Year>
					<Month>12</Month>
					<Day>08</Day>
				</PubDate>
			</History>
		<Abstract>The treatment of conditions like attention deficit disorder through visual neurofeedback not only alleviates the side effects linked with medications but also empowers the brain to autonomously regulate its functions. Numerous research studies employ visual neurofeedback targeting standard EEG bands, especially the beta band, for addressing attention deficit issues. These studies argue that such neurofeedback protocols specifically modulate brain function, exerting the most pronounced influence on the 0.5 to 1.5 Hz EEG band. Consequently, our study delves into the impact of visual neurofeedback on the 0.5 to 1.5 Hz band with the aim of enhancing the visual attention of normal adult subjects. Two distinct neurofeedback training protocols were implemented: the relative beta-I band power and fractal dimension. Subjects underwent 12 training sessions, each lasting 15 minutes. Visual attention assessment utilized the Test of Variables of Attention (TOVA). The results demonstrated significant improvements in the visual attention of subjects for both protocols (DRT = 37.3 ms and 19.6 ms for the beta-I protocol and fractal dimension protocol, respectively). Moreover, an analysis of the data indicated a noteworthy decrease in the relative band power of 0.5 to 1.5 Hz across all subjects throughout the 12 training sessions (DRP = 1.19±0.36 and 0.63±0.39 for the beta-I protocol and fractal dimension protocol, respectively). This implies that this specific band could be an effective approach for enhancing visual attention in visual neurofeedback or eye biofeedback, potentially mitigating eye movements.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Visual neurofeedback</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Reaction Time</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">The EEG band of 0.5 to 1.5 Hz</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Beta-I protocol</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Fractal dimension protocol</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Test of variables of attention (TOVA)</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://www.tmachineintelligence.ir/article_181342_f70b5e288b6d89eb469417283da7bd5c.pdf</ArchiveCopySource>
</Article>
</ArticleSet>
