A new method to detect event-related potentials based on Pearson's correlation

Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience. Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise. The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP's waveform, these waveforms being time- and phase-locked. In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson's correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase-in consonance with the stimuli-in EEG signal correlation over all channels. This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs. These hidden components seem to be caused by variations (between each successive stimulus) of the ERP's inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology. The method we are proposing can be directly used in the form of a process written in the well-known Matlab programming language and can be easily and quickly written in any other software language

EEG correlation at a distance: a re-analysis of two studies using a machine learning approach

Background: In this paper, data from two studies relative to the relationship between the electroencephalogram (EEG) activities of two isolated and physically separated subjects were re-analyzed using machine-learning algorithms. The first dataset comprises the data of 25 pairs of participants where one member of each pair was stimulated with a visual and an auditory 500 Hz signals of 1 second duration. The second dataset consisted of the data of 20 pairs of participants where one member of each pair received visual and auditory stimulation lasting 1 second duration with on-off modulation at 10, 12, and 14 Hz. Methods and Results: Applying a 'linear discriminant classifier' to the first dataset, it was possible to correctly classify 50.74% of the EEG activity of non-stimulated participants, correlated to the remote sensorial stimulation of the distant partner. In the second dataset, the percentage of correctly classified EEG activity in the non-stimulated partners was 51.17%, 50.45% and 51.91%, respectively, for the 10, 12, and 14 Hz stimulations, with respect the condition of no stimulation in the distant partner. Conclusions: The analysis of EEG activity using machine-learning algorithms has produced advances in the study of the connection between the EEG activities of the stimulated partner and the isolated distant partner, opening new insight into the possibility to devise practical application for non-conventional "mental telecommunications" between physically and sensorially separated participants

EEG correlates of social interaction at distance

This study investigated EEG correlates of social interaction at distance between twenty-five pairs of participants who were not connected by any traditional channels of communication. Each session involved the application of 128 stimulations separated by intervals of random duration ranging from 4 to 6 seconds. One of the pair received a one-second stimulation from a light signal produced by an arrangement of red LEDs, and a simultaneous 500 Hz sinusoidal audio signal of the same length. The other member of the pair sat in an isolated sound-proof room, such that any sensory interaction between the pair was impossible. An analysis of the Event-Related Potentials associated with sensory stimulation using traditional averaging methods showed a distinct peak at approximately 300 ms, but only in the EEG activity of subjects who were directly stimulated. However, when a new algorithm was applied to the EEG activity based on the correlation between signals from all active electrodes, a weak but robust response was also detected in the EEG activity of the passive member of the pair, particularly within 9 – 10 Hz in the Alpha range. Using the Bootstrap method and the Monte Carlo emulation, this signal was found to be statistically significant

Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial

Background: The EMPA KIDNEY trial showed that empagliflozin reduced the risk of the primary composite outcome of kidney disease progression or cardiovascular death in patients with chronic kidney disease mainly through slowing progression. We aimed to assess how effects of empagliflozin might differ by primary kidney disease across its broad population. Methods: EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA). Patients were eligible if their estimated glomerular filtration rate (eGFR) was 20 to less than 45 mL/min per 1·73 m2, or 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher at screening. They were randomly assigned (1:1) to 10 mg oral empagliflozin once daily or matching placebo. Effects on kidney disease progression (defined as a sustained ≥40% eGFR decline from randomisation, end-stage kidney disease, a sustained eGFR below 10 mL/min per 1·73 m2, or death from kidney failure) were assessed using prespecified Cox models, and eGFR slope analyses used shared parameter models. Subgroup comparisons were performed by including relevant interaction terms in models. EMPA-KIDNEY is registered with ClinicalTrials.gov, NCT03594110. Findings: Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and followed up for a median of 2·0 years (IQR 1·5–2·4). Prespecified subgroupings by primary kidney disease included 2057 (31·1%) participants with diabetic kidney disease, 1669 (25·3%) with glomerular disease, 1445 (21·9%) with hypertensive or renovascular disease, and 1438 (21·8%) with other or unknown causes. Kidney disease progression occurred in 384 (11·6%) of 3304 patients in the empagliflozin group and 504 (15·2%) of 3305 patients in the placebo group (hazard ratio 0·71 [95% CI 0·62–0·81]), with no evidence that the relative effect size varied significantly by primary kidney disease (pheterogeneity=0·62). The between-group difference in chronic eGFR slopes (ie, from 2 months to final follow-up) was 1·37 mL/min per 1·73 m2 per year (95% CI 1·16–1·59), representing a 50% (42–58) reduction in the rate of chronic eGFR decline. This relative effect of empagliflozin on chronic eGFR slope was similar in analyses by different primary kidney diseases, including in explorations by type of glomerular disease and diabetes (p values for heterogeneity all >0·1). Interpretation: In a broad range of patients with chronic kidney disease at risk of progression, including a wide range of non-diabetic causes of chronic kidney disease, empagliflozin reduced risk of kidney disease progression. Relative effect sizes were broadly similar irrespective of the cause of primary kidney disease, suggesting that SGLT2 inhibitors should be part of a standard of care to minimise risk of kidney failure in chronic kidney disease. Funding: Boehringer Ingelheim, Eli Lilly, and UK Medical Research Council

It's a Question of Methods: Computational Factors Influencing the Frontal Asymmetry in Measuring the Emotional Valence

The prefrontal asymmetry (FA) in the alpha band is a well-known physiological correlate of the emotional valence. Several methods for assessing the FA have been proposed in literature, but no studies have compared their effectiveness in a comprehensive way. In this study we first investigated whether the association between FA and valence depends on the computational methods and then, we identified the best one, namely the one giving the highest correlation with the self-reports. The investigated factors were the presence of a normalization factor, the computation in time or frequency domain and the cluster of electrodes used. All the analyses were implemented on the validated DEAP dataset. We found that the number and position of the electrodes do not influence the FA, in contrast with both the power computation method and the normalization. By using a spectrogram-based approach and by adding a normalization factor, a correlation of 0.36 between the FA and the self-reported valence was obtained

Looking through blue glasses: bioelectrical measures to assess the awakening after a calm situation

Colors can elicit cognitive and emotional states. In particular, blue colour is associated to "refresh" and "restart" effects and is suggested to enhance a wake-up after a calm situation. In this exploratory study, these claims are investigated using Electroencephalographic (EEG), Skin Conductance (SC) and pupil diameter data. The results confirmed the "wake-up effect" for subjects wearing the lenses, as measured by Global Field Power (GFP) in Theta Band, Skin Conductance Response (SCR) and pupil diameter data

Yellow (Lens) Better: Bioelectrical and Biometrical Measures to Assess Arousing and Focusing Effects

Colours can induce several psychological effects, conditioning perceptions, cognitive/emotional states and human performances. In this exploratory study we investigated the effect of a yellow light exposure, obtained filtering the ambient light with coloured glasses, on the human's psychological functioning. In particular we wanted to assess if people are more able to focus when exposed to a yellow light. We recorded EEG, SC, HR and gaze-related data from 16 subjects (50% split in experimental and control group) during the execution of a reactivity test (the Hazard Perception Test, HPT). Compared with the control group, the experimental group showed increases in concentration, focus, visual attention and arousal, as measured by increases of first fixation duration and Beta over-Alpha ratio (BAR) as well as by decreases of distraction, workload, and number of gaze revisits

Spectral differences in resting-state EEG associated to individual Emotional Styles

The ability to manage the emotions has been associated to the Emotional Styles (ES), a set of coherent ways to deal with life's experiences. Recently, the Emotional Style Questionnaire (ESQ) has been proposed as a self-report mea-sure to assess the individual ES. The present study investigates the spectral differences in the resting-state EEG due to the individual ES, in order to support the psychometric reliability of the ESQ with associated neurophysiological measurements. In the alpha and beta band, Social Intuition showed significant and large (d > 0.8) effect sizes on the parietal and parieto-occipital regions, as well as a significant and large effect size in the gamma band on the pre-frontal region. In the beta band, Attention showed a significant and large effect size on the parieto-occipital region