Inversion de polarite de la variation contingente negative au cours d'etats d'endormissement

Timsit‐Berthier, M.; Gerono, A.; Mantanus, H.

doi:10.1016/s0370-4475(81)80037-8

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Thus in our paradigm, a negative deflection of the averaged waveform, called CNV, is obtained [13]. This attentional component has the property of decreasing in amplitude when the participant is less alert, either because he is distracted [14], is deprived of sleep [15] or is falling asleep [16]. This fundamental result is shown in Figure 2.…”

Section: Contingent Negative Variation Extractionmentioning

confidence: 80%

“…The main rhythms in ascending order of frequency are delta (1-3.5 Hz), theta (4)(5)(6)(7)(8), alpha (8)(9)(10)(11)(12) and beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Alpha waves are characteristic of a diffuse awake state for healthy subjects and can be used to discern the normal awake and relaxed states, which is the topic of this experimental study.…”

Section: Introductionmentioning

confidence: 99%

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Detecting mental states of alertness with genetic algorithm variable selection

Vezard

Chavent

Legrand

et al. 2013

2013 IEEE Congress on Evolutionary Computation

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The objective of the present work is to develop a method able to automatically determine mental states of vigilance; i.e., a person's state of alertness. Such a task is relevant to diverse domains, where a person is expected or required to be in a particular state. For instance, pilots or medical staffs are expected to be in a highly alert state, and this method could help to detect possible problems. In this paper, an approach is developed to predict the state of alertness ("normal" or "relaxed") from the study of electroencephalographic signals (EEG) collected with a limited number of electrodes. The EEG of 58 participants in the two alertness states (116 records) were collected via a cap with 58 electrodes. After a data validation step, 19 subjects were retained for further analysis. A genetic algorithm was used to select an optimal subset of electrodes. Common spatial pattern (CSP) coupled to linear discriminant analysis (LDA) was used to build a decision rule and thus predict the alertness of the participants. Different subset sizes were investigated and the best result was obtained by considering 9 electrodes (correct classification rate of 73.68%).

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