Inhaltsangabe
(EEG) brain signals are electrical activity measurements of the brain. Because they cannot be taken remotely or obtained later, they are "secret" by nature and resistant to spoof attempts. EEG can tell the difference between the truth and a lie based on brain waves. uses electroencephalogram (EEG) signals as a set of statistical features to build a lie detection method. Two central problems are addressed; firstly, the number of electrodes and features tested to make the system applicable; secondly, to build a fast and accurate method. Two approaches are adopted; the first is to extract features from a single channel, and the second is to use the least number of features extracted. The EEG-based automatic lie detection method applies four main stages (i.e., preprocessing, feature extraction, feature selection, and classification phase) to the input EEG. The preprocessing includes two steps, normalization, and framing. Feature extraction contains two sets of features; spectral features and statistical moments. The feed-forward neural network classifier is used for the classification task. For evaluation purposes, the publicly available datasets are considered.
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Über die Autorin bzw. den Autor
Israa Jalal holds a bachelor's degree in Computer Science and IT from Wasit University and holds an MSC from the Informatics Institute for Postgraduate Studies / the Iraqi Commission for Computers and Informatics. I am affiliated with the Ministry of Higher Education and Scientific Research. I wish everyone a useful and enjoyable read.
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Lie Detection-Based EEG Signal using Frequency and Time Features
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -(EEG) brain signals are electrical activity measurements of the brain. Because they cannot be taken remotely or obtained later, they are 'secret' by nature and resistant to spoof attempts. EEG can tell the difference between the truth and a lie based on brain waves. uses electroencephalogram (EEG) signals as a set of statistical features to build a lie detection method. Two central problems are addressed; firstly, the number of electrodes and features tested to make the system applicable; secondly, to build a fast and accurate method. Two approaches are adopted; the first is to extract features from a single channel, and the second is to use the least number of features extracted. The EEG-based automatic lie detection method applies four main stages (i.e., preprocessing, feature extraction, feature selection, and classification phase) to the input EEG. The preprocessing includes two steps, normalization, and framing. Feature extraction contains two sets of features; spectral features and statistical moments. The feed-forward neural network classifier is used for the classification task. For evaluation purposes, the publicly available datasets are considered. 112 pp. Englisch. Bestandsnummer des Verkäufers 9786206769033
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. (EEG) brain signals are electrical activity measurements of the brain. Because they cannot be taken remotely or obtained later, they are secret by nature and resistant to spoof attempts. EEG can tell the difference between the truth and a lie based on bra. Bestandsnummer des Verkäufers 1154223289
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -(EEG) brain signals are electrical activity measurements of the brain. Because they cannot be taken remotely or obtained later, they are 'secret' by nature and resistant to spoof attempts. EEG can tell the difference between the truth and a lie based on brain waves. uses electroencephalogram (EEG) signals as a set of statistical features to build a lie detection method. Two central problems are addressed; firstly, the number of electrodes and features tested to make the system applicable; secondly, to build a fast and accurate method. Two approaches are adopted; the first is to extract features from a single channel, and the second is to use the least number of features extracted. The EEG-based automatic lie detection method applies four main stages (i.e., preprocessing, feature extraction, feature selection, and classification phase) to the input EEG. The preprocessing includes two steps, normalization, and framing. Feature extraction contains two sets of features; spectral features and statistical moments. The feed-forward neural network classifier is used for the classification task. For evaluation purposes, the publicly available datasets are considered.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 112 pp. Englisch. Bestandsnummer des Verkäufers 9786206769033
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Taschenbuch. Zustand: Neu. Lie Detection-Based EEG Signal using Frequency and Time Features | Israa Jalal (u. a.) | Taschenbuch | Englisch | 2023 | Scholars' Press | EAN 9786206769033 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 127788703
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - (EEG) brain signals are electrical activity measurements of the brain. Because they cannot be taken remotely or obtained later, they are 'secret' by nature and resistant to spoof attempts. EEG can tell the difference between the truth and a lie based on brain waves. uses electroencephalogram (EEG) signals as a set of statistical features to build a lie detection method. Two central problems are addressed; firstly, the number of electrodes and features tested to make the system applicable; secondly, to build a fast and accurate method. Two approaches are adopted; the first is to extract features from a single channel, and the second is to use the least number of features extracted. The EEG-based automatic lie detection method applies four main stages (i.e., preprocessing, feature extraction, feature selection, and classification phase) to the input EEG. The preprocessing includes two steps, normalization, and framing. Feature extraction contains two sets of features; spectral features and statistical moments. The feed-forward neural network classifier is used for the classification task. For evaluation purposes, the publicly available datasets are considered. Bestandsnummer des Verkäufers 9786206769033
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