02116nas a2200277 4500008004100000022001400041245011300055210006900168260001200237300001500249490000700264520130300271653001001574653002701584653001201611653001101623653000901634653001901643653004101662653001601703100001801719700002001737700001801757700001501775856004801790 2009 eng d a1001-551500aPower spectrum analysis on the multiparameter electroencephalogram features of physiological mental fatigue.0 aPower spectrum analysis on the multiparameter electroencephalogr c02/2009 a162-6, 1720 v263 a

The aim of this experiment is to find a feasible impersonal index for analyzing the physiological mental fatigue level. Three characteristic parameters, relative power in different rhythm, barycenter frequency and power spectral entropy, are extracted from two channels' electroencephalogram (EEG) under two physiological mental fatigue states. Then relationships between such three parameters and physiological mental fatigue are analyzed to explore whether they can be of use for detecting (or monitoring) the mental fatigue level. The experiment results show that the relative power, barycenter frequency and power spectral entropy of EEG exhibit strong correlation with physiological mental fatigue level. While physiological mental fatigue level increases, the relative power in theta, alpha and beta rhythms, barycenter frequency and power spectral entropy of EEG decrease, but the relative power in delta rhythm of EEG increases. The relative power in four rhythms, barycenter frequency and power spectral entropy of EEG reflect the change of physiological mental fatigue level sensitively, and may hopefully be used as indexes for detecting physiological mental fatigue level.

10aAdult10aElectroencephalography10aEntropy10aHumans10aMale10aMental Fatigue10aSignal Processing, Computer-Assisted10aYoung Adult1 aZhang, Ai-hua1 aZheng, Shi Dong1 aPei, Xiao-Mei1 aOuyang, Yi uhttp://www.ncbi.nlm.nih.gov/pubmed/1933457704357nas a2200385 4500008004100000022001400041245009700055210006900152260001200221300001200233490000800245520323100253653001503484653001003499653001403509653001003523653001803533653004203551653002703593653003003620653001103650653001103661653000903672653003203681653002303713653002203736653002803758100002503786700002603811700001903837700002003856700002603876700002103902856004803923 2008 eng d a1388-245700aTowards an independent brain-computer interface using steady state visual evoked potentials.0 aTowards an independent braincomputer interface using steady stat c02/2008 a399-4080 v1193 a

OBJECTIVE: 

Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze.

METHODS: 

Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs.

RESULTS: 

Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control.

CONCLUSIONS: 

The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user.

SIGNIFICANCE: 

SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.

10aAdolescent10aAdult10aAttention10aBrain10aBrain Mapping10aDose-Response Relationship, Radiation10aElectroencephalography10aEvoked Potentials, Visual10aFemale10aHumans10aMale10aPattern Recognition, Visual10aPhotic Stimulation10aSpectrum Analysis10aUser-Computer Interface1 aAllison, Brendan, Z.1 aMcFarland, Dennis, J.1 aSchalk, Gerwin1 aZheng, Shi Dong1 aMoore-Jackson, Melody1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/18077208