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Electrodermal Activity (EDA) - State-of-the-art measurement and techniques for parapsychological purposes

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Abstract

In most of the direct mental interactions with living systems (DMILS)/Remote Staring studies, electrodermal activity (EDA) is the only dependent variable. Therefore the quality of EDA recording is crucial. This is the reason why we studied EDA-related literature and contacted some of the leading psychophysiological labs in Germany to debate critical topics of the EDA measurement. We also checked the Methods section of all studies using EDA data published from 1995 to 1999 in the leading psychophysiological journals. In addition, we surveyed all DMILS/Remote Staring publications using EDA to find out whether parapsychologists adhere to these standards. In the first part of our paper we outline a current state-of-the-art EDA methodology. We also address various technical problems and describe sources for potential artifacts. In the second part we compare 24 DMILS/Remote Staring with a sample of 39 recent psychophysiological studies published in Psychophysiology and International Journal of Psychophysiology. The analysis reveals that parapsychologists do not meet the current standards. There is not even one study conducted by parapsychologists which refers to psychophysiology's measurement standards published in 1981. Therefore, DMILS/Remote Staring data may either contain artifacts, or, on the other hand, may not detect the supposed effects. Although there is an ongoing trend of finding irregularities in EDA data of DMILS/Remote Staring experiments that can be related to different intentional conditions, there have not been any efforts to understand the results of EDA experiments or to address the origin of the irregularities in detail.
... • phasic_peak_amplitude represents the amplitude of phasic peaks [54]. • phasic_peak_longitude is the rise time/duration of the peaks [54]. ...
... • phasic_peak_amplitude represents the amplitude of phasic peaks [54]. • phasic_peak_longitude is the rise time/duration of the peaks [54]. • phasic_peak_slope represents the slope of the peaks [55]. ...
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Research on brain-computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithms before using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals.
... We ran deconvolution with the default parameters. Electrodermal activity responses were calculated as differences in the deconvoluted phasic signal greater than 0.05μS (Schmidt & Walach, 2000). We averaged responses across each condition and normalized by condition duration in minutes. ...
... We ran deconvolution using default parameters. We counted the differences in the deconvoluted transient signal greater than 0.05 μS (Schmidt & Walach, 2000) during training, divided by the duration of training in minutes. ...
Thesis
Humans must frequently adapt their posture to prevent loss of balance. Such balance control requires complex, precisely-timed coordination among sensory input, neural processing, and motor output. Despite its importance, our current understanding of cortical involvement during balance control remains limited by traditional neuroimaging methods, which are stationary and have poor time resolution. High-density electroencephalography (EEG), combined with independent component analysis, has become a promising tool for recording cortical dynamics during balance perturbations due to its portability and high temporal resolution. Additionally, recent improvements in immersive virtual reality headsets may provide new rehabilitative paradigms, but the effects of virtual reality on balance and cortical function remain poorly understood. In my first study, I recorded high-density EEG from healthy, young adult subjects as they walked along a beam with and without virtual reality high heights exposure. While virtual high heights did induce stress, the use of virtual reality during the task increased performance errors and EEG measures of cognitive loading compared to real-world viewing without a headset. In my second study, I collected high-density EEG from healthy young adults as they walked along a treadmill-mounted balance beam to determine the effect of a transient visual perturbation on training in virtual reality. Subjects in the perturbations group improved comparably to those that trained without virtual reality, indicating that the perturbation helped subjects overcome the negative effects of virtual reality on motor learning. The perturbation primarily elicited a cognitive change. In my third study, healthy, young adult EEG was recorded during physical pull and visual rotation perturbations to tandem walking and tandem standing. I found similar electrocortical patterns for both perturbation types, but different cortical areas were involved for each. In my fourth study, I used a phantom head to validate EEG connectivity methods based on Granger causality in a real-world environment. In general, connectivity measures could determine the underlying connections, but many were susceptible to high-frequency false positives. Using data from my third study, my fifth study analyzed corticomuscular connectivity patterns following sensorimotor balance perturbations. I found strong occipito-parietal connections regardless of perturbation type, along with evidence of direct muscular control from the supplementary motor area during the standing perturbation response. Taken together, the work presented in this dissertation greatly expands upon the current knowledge of cortical processing during sensorimotor balance perturbations and the effect of such perturbations on short-term motor learning, providing multiple avenues for future exploration.
... The complexity of the EDA signal creates a need for multiple processing steps, such as removing noise and movement artifacts from the signal, extracting meaningful features, and applying appropriate analyses [3,4]. In other scholarly disciplines, EDA guidelines are available for processing, for example, fear, stress, or emotion-evoking stimuli [35,37,38]. However, such guidelines are not explicitly available for research on learning and education as approaches for processing EDA differ in this field. ...
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There is a strong increase in the use of devices that measure physiological arousal through electrodermal activity (EDA). Although there is a long tradition of studying emotions during learning, researchers have only recently started to use EDA to measure emotions in the context of education and learning. This systematic review aimed to provide insight into how EDA is currently used in these settings. The review aimed to investigate the methodological aspects of EDA measures in educational research and synthesize existing empirical evidence on the relation of physiological arousal, as measured by EDA, with learning outcomes and learning processes. The methodological results pointed to considerable variation in the usage of EDA in educational research and indicated that few implicit standards exist. Results regarding learning revealed inconsistent associations between physiological arousal and learning outcomes, which seem mainly due to underlying methodological differences. Furthermore, EDA frequently fluctuated during different stages of the learning process. Compared to this unimodal approach, multimodal designs provide the potential to better understand these fluctuations at critical moments. Overall, this review signals a clear need for explicit guidelines and standards for EDA processing in educational research in order to build a more profound understanding of the role of physiological arousal during learning.
... In the study mentioned above, electrodermal activity (EDA) was measured through the use of the Féré method, with a constant voltage of 0.5 V. In order to analyze the recording of the galvanic skin response, a band-pass filter was employed with the lower frequency limit of 0.2 Hz (in order to separate the phasic components of the electrodermal activity from the tonic components), and the higher frequency limit of 1 Hz (in order to filter out the noise and to suppress artifacts caused by Ebbecke waves; Schmidt and Walach, 2000). Both the EDA signal and other signals were registered for the entire duration of the film. ...
... While SCR rides on top of the tonic level with rapid fluctuations, it is easy to find the peaks and bursts which could provide us with valuable information to identify the emotional stimulus events. Generally, the duration of the stimulus event is approximately 1 to 5 s after the onset of the emotional stimulus [27]. According to Fig. 3, four features could be utilized to characterize SCR. ...
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Conventionally, multiple physiological signals are used in the field of stress realization. Although many studies have applied various methods in feature selection and classification, a desirable performance has not yet been achieved. This paper presents a novel method of stress level classification using physiological signals during the real-world driving task. Exploring the most reliable analysis method on a comprehensive physiological signal for stress realization has been commonly investigated in various studies. To obtain a high accuracy approach, a proper classification method should be applied to the most relevant physiological signal. In this study, we evaluate the feasibility and effectiveness of the analysis of variance (ANOVA) classifier learner on the single Galvanic Skin Response (GSR) signal. Three levels of stress are taken into account and two independent features including rising time and amplitude are extracted. These two features are extracted from foot and hand GSR signals in three different scenarios for the sake of training. The result indicates that the foot amplitude feature of the GSR signal solely is a reliable source of stress classification with an accuracy rate of 95.83% by applying the ANOVA approach. Accordingly, this methodology can substantially reduce the necessity of resorting to the high number of sensors and the corresponding computational burden associated with signal analysis. Besides, reducing the number of sensors during the measurement procedure would increase drivers’ safety by reducing the interference between human and measurement devices. In this study, the real data collected by Picard and his co-workers are used, available in the PHYSIONET database.
... Each participant performed three consecutive tours when immersed into the virtual environment (ImVE). 48 . For additional noise reduction in the phasic part, the signal was once more low-pass filtered with a cut-off frequency of 5 Hz. ...
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ABSTRACTA provisional standard method of measuring tonic skin conductance (SCL) and GSR (SCR) is advocated, using a constant-voltage method for which circuits are provided useable with Beckman, Grass, and other common polygraphs. A standard electrode methodology is also presented. The problem of units of measurement is considered in detail with an analysis of the so-called Law of Initial Values. Methods are given for correcting both tonic SC and SCRs for individual differences in their respective ranges of variation and the purpose and relative advantages of these range-correction methods are discussed.
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