Channel Pz Research Articles

Oscillatory correlates of threat imminence during virtual navigation.

The Predatory Imminence Continuum Theory proposes that defensive behaviors depend on the proximity of a threat. While the neural mechanisms underlying this proposal are well studied in animal models, it remains poorly understood in humans. To address this issue, we recorded EEG from 24 (15 female) young adults engaged in a first-person virtual reality Risk-Reward interaction task. On each trial, participants were placed in a virtual room and presented with either a threat or reward conditioned stimulus (CS) in the same room location (proximal) or different room location (distal). Behaviorally, all participants learned to avoid the threat-CS, with most using the optimal behavior to actively avoid the proximal threat-CS (88% accuracy) and passively avoid the distal threat-CS (69% accuracy). Similarly, participants learned to actively approach the distal reward-CS (82% accuracy) and to remain passive to the proximal reward-CS (72% accuracy). At an electrophysiological level, we observed a general increase in theta power (4-8 Hz) over the right posterior channel P8 across all conditions, with the proximal threat-CS evoking the largest theta response. By contrast, distal cues induced two bursts of gamma (30-60 Hz) power over midline-parietal channel Pz (200 msec post-cue) and right frontal channel Fp2 (300 msec post-cue). Interestingly, the first burst of gamma power was sensitive to the distal threat-CS and the second burst at channel Fp2 was sensitive to the distal reward-CS. Together, these findings demonstrate that oscillatory processes differentiate between the spatial proximity information during threat and reward encoding, likely optimizing the selection of the appropriate behavioral response.

A Guided Tutorial on Modelling Human Event-Related Potentials with Recurrent Neural Networks

In cognitive neuroscience research, computational models of event-related potentials (ERP) can provide a means of developing explanatory hypotheses for the observed waveforms. However, researchers trained in cognitive neurosciences may face technical challenges in implementing these models. This paper provides a tutorial on developing recurrent neural network (RNN) models of ERP waveforms in order to facilitate broader use of computational models in ERP research. To exemplify the RNN model usage, the P3 component evoked by target and non-target visual events, measured at channel Pz, is examined. Input representations of experimental events and corresponding ERP labels are used to optimize the RNN in a supervised learning paradigm. Linking one input representation with multiple ERP waveform labels, then optimizing the RNN to minimize mean-squared-error loss, causes the RNN output to approximate the grand-average ERP waveform. Behavior of the RNN can then be evaluated as a model of the computational principles underlying ERP generation. Aside from fitting such a model, the current tutorial will also demonstrate how to classify hidden units of the RNN by their temporal responses and characterize them using principal component analysis. Statistical hypothesis testing can also be applied to these data. This paper focuses on presenting the modelling approach and subsequent analysis of model outputs in a how-to format, using publicly available data and shared code. While relatively less emphasis is placed on specific interpretations of P3 response generation, the results initiate some interesting discussion points.

Investigating the Cognitive Response of Brake Lights in Initiating Braking Action Using EEG

Half of all road accidents result from either lack of driver attention or from maintaining insufficient separation between vehicles. Collision from the rear, in particular, has been identified as the most common class of accident in the UK, and its influencing factors have been widely studied for many years. Rear-mounted stop lamps, illuminated when braking, are the primary mechanism to alert following drivers to the need to reduce speed or brake. This paper develops a novel brain response approach to measuring subject reaction to different brake light designs. A variety of off-the-shelf brake light assemblies are tested in a physical simulated driving environment to assess the cognitive reaction times of 22 subjects. Eight pairs of LED-based and two pairs of incandescent bulb-based brake light assemblies are used and electroencephalogram (EEG) data recorded. Channel Pz is utilised to extract the P3 component evoked during the decision making process that occurs in the brain when a participant decides to lift their foot from the accelerator and depress the brake. EEG analysis shows that both incandescent bulb-based lights are statistically slower to evoke cognitive responses than all tested LED-based lights. Between the LED designs, differences are evident, but not statistically significant, attributed to the significant amount of movement artifact in the EEG signal.

Diagnosis of mild Alzheimer's disease by EEG and ERP signals using linear and nonlinear classifiers

A new method for the diagnosis of Alzheimer's disease in the mild stage is presented due to its association with the characteristics of the EEG brain signal. In the proposed method, three channels Pz, Cz, and Fz of brain signals were recorded from 40 participants in the four modes of closed eyes, open eyes, recall, and stimulation. After preprocessing, wide various features of EEG signals were extracted. Results have shown that after stimulation, the amplitude and the latency of the P300 component change with increasing age and the stages of Alzheimer's disease. In addition, the first changes in brain signal in mild Alzheimer's patients are increased activity in the theta band and decreased activity in the beta band, which is associated with a decrease in alpha-band activity. After selecting the proper features, linear discriminant analysis, and Elman and convolutional neural networks were used to classify the participants' features. The results showed that the Pz channel among three EEG signals and the stimulation mode among the four recording steps had greater accuracy than the others. By using features of the Pz channel, the accuracy of LDA was 59.4% and 66.4% in the recall and excitation modes, accuracy of Elman neural network was 92.3% and 94.1%, and CNN was 97.5% and 99% respectively. Results have shown that extracting appropriate linear and nonlinear features has increased the accuracy of the classifiers. In addition, due to the dynamic nature of the brain signal, the convolutional neural network had better performance than LDA and Elman.

Single channel EEG analysis for detection of depression

Abstract Purpose This study is aimed at finding a simple method for detection of depression based on the analysis of single channel short-term EEG signals. Materials and methods The accuracy of linear, spectral asymmetry index (SASI), and nonlinear, detrended fluctuation analysis (DFA), methods for differentiating depressive and healthy subjects was compared. The eyes closed EEG was recorded from 18 common reference (Cz) channels for 34 subjects (17 depressive and 17 control). The signals were stored at 400 Hz sampling frequency and digitally filtered with cutoff frequencies at 0.5 Hz and at 40 Hz. The first 5 min of each recording was selected for further analysis. Results The experiments indicated maximum difference for SASI values in channel Pz and for DFA values in channels Pz and O2. Therefore, channel Pz was selected for comparison of two methods. The results of statistical analysis show that SASI values are significantly higher for depressive than for control group (p = 3.577e–05), while DFA values are significantly lower for depressive group (p = 0.033). SASI has superior discrimination ability with classification accuracy of 76.5%, while the classification accuracy of DFA was 70.6%. Linear combination of SASI and DFA resulted in 91.2% classification accuracy. Conclusions Our results demonstrate that the analysis of single channel signal can provide high accuracy of differentiation depression EEG.

Effect of Anodal-tDCS on Event-Related Potentials: A Controlled Study.

We aim to measure the postintervention effects of A-tDCS (anodal-tDCS) on brain potentials commonly used in BCI applications, namely, Event-Related Desynchronization (ERD), Event-Related Synchronization (ERS), and P300. Ten subjects were given sham and 1.5 mA A-tDCS for 15 minutes on two separate experiments in a double-blind, randomized order. Postintervention EEG was recorded while subjects were asked to perform a spelling task based on the “oddball paradigm” while P300 power was measured. Additionally, ERD and ERS were measured while subjects performed mental motor imagery tasks. ANOVA results showed that the absolute P300 power exhibited a statistically significant difference between sham and A-tDCS when measured over channel Pz (p = 0.0002). However, the difference in ERD and ERS power was found to be statistically insignificant, in controversion of the the mainstay of the litrature on the subject. The outcomes confirm the possible postintervention effect of tDCS on the P300 response. Heightening P300 response using A-tDCS may help improve the accuracy of P300 spellers for neurologically impaired subjects. Additionally, it may help the development of neurorehabilitation methods targeting the parietal lobe.

Combining findings from gaze and electroencephalography recordings to study timing in a visual tracking task

Electroencephalography (EEG) and gaze data have traditionally been separated in neurocognitive studies because of the artefacts that even small controlled eye movements produce. Study of gaze control in a visual tracking task provides information about an individual's prospective control. By including gaze events in the EEG analysis, we studied prospective control and its neural correlates during deceleration in a visual tracking task. Adult participants followed with their gaze a small car moving horizontally on a large screen, where the final approach of the car was temporarily occluded, and pushed a button to stop the car at the reappearance point. Two gaze events, the behavioural push button response and the nonbehavioural stimulus onset, were used to time-lock the averaged event-related potential (ERP) waveform. A significant effect of deceleration on peak amplitude in parietal channel Pz (P<0.05) was found when ERP waveforms were time-locked to the prospective gaze shift over the occluder. The peak decreased in amplitude as car deceleration increased when participants successfully stopped the car, indicating successful deceleration discrimination. No such effect was found when ERP waveforms were time-locked to any of the other events. Thus, a traditional stimulus onset time-locking procedure is likely to distort the averaged signal and consequently hide important Pz-peak amplitude differences on the prospective timing of decelerating object motion during occlusion. This study shows the importance of including behavioural data when studying neural correlates of prospective control and proposes active incorporation of behavioural data into the EEG analysis.

Functional connectivity analysis of steady-state visual evoked potentials

In this paper, functional connectivity of steady-state visual evoked potential (SSVEP) was investigated. Directed transfer function (DTF) was applied to cortical signals recorded from electroencephalography (EEG) in order to obtain connectivity patterns. Flow gain was proposed to assess the role of the specific brain region involved in the information transmission process. We found network connections exist in many regions beyond occipital region. Flow gain mapping both in 8–12 Hz and 13–30 Hz showed that parietal region seemed to serve as the sole hub of information transmission. Further studies of flow gain obtained from channel Pz showed two distinct peaks centered at about 12 Hz low frequency and 20 Hz medium frequency respectively. The low frequency region had a larger value of flow gain. The present study introduced functional connectivity into SSVEP. Furthermore, we put forward the concept of flow gain for the first time to explore the exchange and processing of brain information during SSVEP.

Modulated low-level electromagnetic field effects on EEG visual event-related potentials

The aim of this study was experimental investigation of the effects of low-level modulated microwaves on the human central nervous system function using electroencephalo- graphic (EEG) visual event-related potentials. Thirty healthy volunteers were exposed and sham- exposed to the electromagnetic field (EMF) (450 MHz, 0.16 mW/cm 2 ) modulated at 21 Hz frequency during visual oddball tasks. During the task the participants had to distinguish infrequent target stimuli from frequent standard stimuli. Th e task consisted of detecting visually presented random ball-shape stimuli of which 25% were target and 75% standard stimuli, in one task the subject was EMF-exposed and in another task sham-exposed. One task lasted 10 min, during the exposed task the EMF was switched on and off in one-minute intervals, so that the participants performed half of the task in exposed condition (EMF ON) and half of the task without the exposure (EMF OFF). The sham-exposed task was identical to the exposed task, except that the EMF generator was switched off all the time. Thr ee-channel EEG was recorded during all the tasks using electrodes Fz, Pz and Cz. For analysis of the EEG event-related responses, the channel Pz was used. The EEG responses of a task were grouped into four different categories, taking into account the EMF exposure condition (ON, OFF) during the response and the response type (standard, target). The responses were averaged by categories; finally we had four types of average EEG event-related potentials for a task for every subject. The EMF ON and OFF potentials were compared pairwise. The hypothesis was that the EEG event-related potential component latencies, amplitudes and reaction times (RT) of the subjects to the target stimuli are not different in EMF ON and EMF OFF conditions during the same task. There were no statistically significant differences in responses in sham-exposed tasks, neither in standard nor in target-response components. In the exposed task the group mean N100 amplitude (measured from standard response) was -3.41 ± 4.54 µV in EMF ON category and -2.50 ± 4.71 µV in EMF OFF category; this difference was statistically highly significant ( p < 0.00009). Differences in amplitudes, latencies and in RT between EMF ON and OFF conditions of other components were not significant. It is concluded that the modulated microwave effect has stronger impact on early (sensory) components of EEG visual event-related potentials compared with later stages of visual information processing during the oddball task.

INVESTIGATION OF RADIAL ENERGY FLOWS IN AN ARC HEATER CHANNEL

The integral quantities measured on an arc heater where the electrical arc is stabilized by flowing gas serve as input data of a mathematical-physical model of phenomena inside the arc heater channel. The model is based on the mass and energy conservation equations and Ohm law and uses the known material properties of the gas medium. The main simplifying assumptions are LTE, relatively small kinetic energy of plasma, and axial symmetry of the investigated area. The main measured values are the arc current I (50 to 220 A), the arc voltage U (approx. 100 volts), the flow rate of the working gas G (argon, up to 22 g/s), energy loss of the arc heater channel Pz determined from the warming of the cooling water. By means of the mentioned model, the energy flows in axial (enthalpy flow) and radial (conduction and radiation energy loss) directions are computed and presented in diagrams.