Impaired wakefulness of machine operators presents a danger not only for themselves, but often for the public at large as well. While on duty, such persons are expected to be continuously, i.e. without interruption, on the alert. For that purpose, we designed and carried out an experimental model of continuous vigilance monitoring using electroencephalography (EEG) and reaction time measured as the latency of the volunteers’ reaction to a sound stimulus. In this article, we focus on two different approaches of EEG signal analysis. Spectral analysis, which is based on linear stochastic approach, is the hrst type. On the other hand, there is nonlinear analysis formally called the chaos theory. For both methods, we will show typical markers which represent the state of the vigilance. Both methods will be compared and the outputs will be discussed.
EEG activities with open eyes in a quiet state (OA), during pseudo-Raven's test (PRA), in a hypnagogic state (HYP) and REM sleep (REM) are marked by similar, nearly flat curves. Further we observed states with eyes closed (OC), with hyperventilation (HV), with calculation (CAL) and in NONREM 1 sleep (NR 1). During OA, the EEG spectrum contains some delta and but rudimental alpha activity while during PRA and in HY there is an increase in delta-theta and a significant decrease in alpha activities. Hence, not even Fast Fourier Transformation (FFT) can differentiate between the states with fkat curves. This made us introduce another EEG curve analysis for coherence function (CF). We investigated 24 healthy volunteers aged 22 -- 55 years, 19 men and 5 women, in the above mentioned eight states with simultaneous EEG recording.
Vigilance was controlled by means of acoustic stimulation, reactivity was expressed in reaction time (ReT), i.e. latency of response in milliseconds (ms). Imitation Raven's test (pseudo-Raven' = PRA) was used for psychic testing. Recorded in the afternoon hours after partial sleep deprivation, the EEG curve was described optically using FFT and CF as well. FFT results have already been mentioned above. CF showed lower values during OA with up to 400 ms of ReT, a diffuse increase during HYP with ReT of 800 - 1200 ms, and a multifocal rise of delta activity in the EEG curve during PRA.
Consequently, EEG analysis can help differentiate between the above eight states, otherwise barely distinguishable with the naked eye especially in cases with flat EEG curves. Using similar analyses, it is possible to discriminate all stages of NONREM and REM sleep without polysomnography.
The paper technically describes the principles of incorporation of the biofeedback system into the system of a driving simulator. After a brief introduction of the basic features of EEG biofeedback, the most important scenarios where such simulator enhancement can be successfully used are described. The system is introduced with the use of an analysis of the major technical and construction aspects, such as the software design, hardware realization and its incorporation into the driving simulator system. Finally, the paper sketches pilot experiments which were performed using EEG biofeedback incorporated into the driving simulator.
In paper connections among dissociation, neural and EEG complexity are presented. They implicate the EEG correlate for dissociated rnental representations of neural assemblies which actually act in the brain-mind system. As a consequence of dissociation among these rnental representations biirst EEG activity is present. Burst activity is explained as a consequence of deterrninistic chaos, which leads to emerging of the underlying order of attractors in brain physiology. This chaos is comparable to the world of possibilities and their collapse in quanturn theory. The chaos may thus serve to link quanturn events to globál brain dyriamics and rriay be connected to the quanturn superposition of brain States and the collapse.
EEG activities with open eyes in a quiet state (OA), during the pseudo-Raven's test (PRA), in hypnagogic state (HYP) and in the course of REM sleep (REM) are characteristic by nearly flat curves. We observed the states with eyes closed (OC), with hyperventilation (HV), during mental activity of calculation (CAL) and in NONREM 1 sleep (NR 1). 24 tested persons (probands) were investigated. We have found 8 typical states of EEG signals, which all have relation to attention and mental activity. Consequently, the EEG analysis can help in the differentiation between the above eight states. Using similar analyses, it is possible to discriminate all stages of NONREM and REM sleep without polysomnography.