Basis Of EEG Data Research Articles

Influence of Hospital Outdoor Space on Physiological Electroencephalography (EEG) Feedback of Staff.

To investigate the differences and relationships between different outdoor spaces of hospitals on the physiological electroencephalography (EEG) feedback (PEEGF) of staff. Relieving the pressure of hospital staff is essential, and several studies have revealed that even short-term exposure to outdoor space has a decompression effect. Yet, the focus is scarcely centered on the differences and influential relationships between the PEEGF from different outdoor spaces where the staff spend time, particularly in large-scale hospitals in China. EEG measurement equipment was utilized to obtain the value of β wave (vβw) that represents the stress and anxiety of staff in three different outdoor spaces: open, traffic, and rest. On the basis of EEG data, correlation analysis was conducted in accordance with the proportion of space elements. The proportion of natural elements, such as landscape (r = -.800** p=.005) and waterscape (r = -.782* p=.013), were negatively correlated with the vβw produced by staff, while the proportion of hard paving was positive (r = .817** p=.004) with more vβw produced by staff. In other words, the percentage of landscape and waterscape can reduce stress, while hard paving has the opposite effect. Further, there was a difference in the amount of vβw generated between nurses and administrators in the open space at the entrance of the main building (p = .043). The present study revealed the influence of different outdoor space elements of the hospital on the physiological feedback of staff, demonstrated the practical necessity of evidence-based design, and proposed relevant optimization suggestions.

Prediction of Pilot's Reaction Time Based on EEG Signals.

The main hypothesis of this work is that the time of delay in reaction to an unexpected event can be predicted on the basis of the brain activity recorded prior to that event. Such mental activity can be represented by electroencephalographic data. To test this hypothesis, we conducted a novel experiment involving 19 participants that took part in a 2-h long session of simulated aircraft flights. An EEG signal processing pipeline is proposed that consists of signal preprocessing, extracting bandpass features, and using regression to predict the reaction times. The prediction algorithms that are used in this study are the Least Absolute Shrinkage Operator and its Least Angle Regression modification, as well as Kernel Ridge and Radial Basis Support Vector Machine regression. The average Mean Absolute Error obtained across the 19 subjects was 114 ms. The present study demonstrates, for the first time, that it is possible to predict reaction times on the basis of EEG data. The presented solution can serve as a foundation for a system that can, in the future, increase the safety of air traffic.

Mapping human visual representations by deep neural networks

A number of recent studies have shown that deep neural networks (DNN) map to the human visual hierarchy. However, based on a large number of subjects and accounting for the correlations between DNN layers, we show that there is no one-to-one mapping of DNN layers to the human visual system. This suggests that the depth of DNN, which is also critical to its impressive performance in object recognition, has to be investigated for its role in explaining brain responses. On the basis of EEG data collected from a large set of natural images we analyzed different DNN architectures a 7 layer, 16 layer and a 22 layer DNN network using weibull distribution for the representations at each layer. We find that the DNN architectures reveal temporal dynamics of object recognition, with early layers driving responses earlier in time and higher layers driving the responses later in time. Surprisingly the layers from the different architectures explain brain responses to a similar degree. However, by combining the representations of the DNN layers we observe that in the higher brain areas we explain more brain activity. This suggests that the higher areas in the brain are composed of multiple non-linearities that are not captured by the individual DNN layers. Overall, while DNNs form a highly promising model to map the human visual hierarchy, the representations in the human brain go beyond the simple one-to-one mapping of the DNN layers to the human visual hierarchy.

Decoding speech perception by native and non-native speakers using single-trial electrophysiological data.

Brain-computer interfaces (BCIs) are systems that use real-time analysis of neuroimaging data to determine the mental state of their user for purposes such as providing neurofeedback. Here, we investigate the feasibility of a BCI based on speech perception. Multivariate pattern classification methods were applied to single-trial EEG data collected during speech perception by native and non-native speakers. Two principal questions were asked: 1) Can differences in the perceived categories of pairs of phonemes be decoded at the single-trial level? 2) Can these same categorical differences be decoded across participants, within or between native-language groups? Results indicated that classification performance progressively increased with respect to the categorical status (within, boundary or across) of the stimulus contrast, and was also influenced by the native language of individual participants. Classifier performance showed strong relationships with traditional event-related potential measures and behavioral responses. The results of the cross-participant analysis indicated an overall increase in average classifier performance when trained on data from all participants (native and non-native). A second cross-participant classifier trained only on data from native speakers led to an overall improvement in performance for native speakers, but a reduction in performance for non-native speakers. We also found that the native language of a given participant could be decoded on the basis of EEG data with accuracy above 80%. These results indicate that electrophysiological responses underlying speech perception can be decoded at the single-trial level, and that decoding performance systematically reflects graded changes in the responses related to the phonological status of the stimuli. This approach could be used in extensions of the BCI paradigm to support perceptual learning during second language acquisition.

Changes in blood parameters and electroencephalogram of cattle as affected by different stunning and slaughter methods in cattle

The present study aimed to provide a comparative analysis of the effects of penetrative stunning, non-penetrative stunning and post-slaughter stunning on biochemical parameters and electroencephalogram (EEG) associated with stress in heifers and steers. Ten animals were assigned to each of the following four treatment groups: (1) animals were subjected to conventional halal slaughter (a clean incision through the structures on the ventral neck at the approximate level of vertebrae C2–C3 – the trachea, oesophagus, carotid arteries and jugular veins) and post-cut penetrating mechanical stun within 10–20 s of the halal cut (U); (2) high-power non-penetrating mechanical stunning using a mushroom-headed humane killer, followed by conventional halal slaughter (HPNP); (3) low-power non-penetrating mechanical percussive stunning using a mushroom-headed humane killer, followed by conventional halal slaughter (LPNP); and (4) penetrative stunning using a captive-bolt pistol humane killer, followed by conventional halal slaughter (P). For each animal, blood samples and electroencephalogram recordings were taken before stunning, post-stunning (if applicable) and post-slaughter, and plasma concentrations of cortisol, adrenocorticotrophic hormone (ACTH), adrenaline, noradrenaline and β-endorphin were determined. Irrespective of the stunning method, except for percentage change in plasma concentrations of noradrenaline, the values of blood parameters attained before and after stunning were not significantly different. The plasma noradrenaline concentration of the HPNP animals was significantly elevated following stunning. Following slaughter, the percentage change of plasma ACTH concentration in the P animals was significantly elevated. Neither stunning method nor sampling time had a significant effect on plasma β-endorphin concentration. On the basis of the EEG results, penetrative stunning seemed to be better in maximising the possibility of post-stunning insensibility, whereas U animals appeared to demonstrate an evident increase in EEG activity which is consistent with the presence of post-slaughter noxious stimuli associated with tissue cut and injury. The U animals had consistently higher, if not the highest, RMS values than did other stunned animals. This indicates a degree of EEG changes associated with stress and pain. On the basis of EEG data, our results suggested that penetrative stunning would be the most reliable method of ensuring insensibility and minimising pain. However, at slaughter, the P animals showed a dramatic elevation in the percentage change of circulating ACTH, suggesting physiological stress response. On a cautionary note, the results are not unequivocal, and it may be that the range of analyses available to researchers at this point of time are not sufficiently specific to allow definitive conclusions to be drawn.

Temporal and spatial congruence of components of motion-onset evoked responses investigated by whole-head magneto-electroencephalography

Motion-onset related components in averaged whole head co-recorded MEG and EEG responses of 24 adults to a low-contrast checkerboard pattern were studied. The aims were to identify these components, to characterize quantitatively their maps and to localize the underlying sources by equivalent-current-dipole (ECD) analyses with a spherical head model. After a weak P1, a large start-elicited negativity arises, comprising the novel N2a (occipital positive and parieto-central negative, peak-latency 141 ms) and the N2 like N2b (bilateral parieto-temporal, 175 ms) component. It is followed by a large positive stop-related component, P2 (156 ms after motion-offset). The corresponding MEG components N2am and N2bm showed bilateral dipole fields with considerable overlap. P1m has a single dipole field around the midline. N2a(m) and N2b(m) can be modelled with two bilateral ECDs with significant different locations. The study shows that accurate mapping and ECD analyses can distinguish two neighbouring areas of the visual cortex, 21 ± 4 (SE) mm separated, which activities are reflected in both spatio-temporally closely related N2(m) components. N2a(m) and N2b(m) originate in the extrastriate cortex, possibly close to or in V3/V3A and MT/V5 respectively. Motion-evoked activity in (near) V3/V3A is novel on the basis of EEG data.

Use of a computer diagnostic system to assess cerebral cortical function in rabbits on the basis of eeg data

Computer evaluation of the state of cerebral cortical function in rabbits on the basis of EEG data enabled its particular features to be distinguished during dark and light adaptation of varied duration and during the action of pentobarbital, and also revealed individual differences in cortical activity of different rabbits. A system for automatic conversion, recording, and processing of multiprocess information (the APROMIN system) and a computer diagnostic system were used in combination for multistage processing of multiprocess information. The final results were presented as a two-dimensional dot display, reflecting multidimensional dynamic patterns of the state of cortical function in rabbits.