A Systematic Comparison of High-End and Low-Cost EEG Amplifiers for Concealed, Around-the-Ear EEG Recordings.

Wearable electroencephalography (EEG) has the potential to improve everyday life through brain-computer interfaces (BCI) for applications such as sleep improvement, adaptive hearing aids, or thought-based digital device control. To make these innovations more practical for everyday use, researchers are looking to miniaturized, concealed EEG systems that can still collect neural activity precisely. For example, researchers are using flexible EEG electrode arrays that can be attached around the ear (cEEGrids) to study neural activations in everyday life situations. However, the use of such concealed EEG approaches is limited by measurement challenges such as reduced signal amplitudes and high recording system costs. In this article, we compare the performance of a lower-cost open-source amplification system, the OpenBCI Cyton+Daisy boards, with a benchmark amplifier, the MBrainTrain Smarting Mobi. Our results show that the OpenBCI system is a viable alternative for concealed EEG research, with highly similar noise performance, but slightly lower timing precision. This system can be a great option for researchers with a smaller budget and can, therefore, contribute significantly to advancing concealed EEG research.

Respiratory Sinus Arrhythmia (RSA) stress response in preschool age varies by serotonin transporter polymorphism (5-HTTLPR): A preliminary report.

The serotonin transporter promoter region polymorphism (5-HTTLPR) has been implicated in stress regulation, with increased stress reactivity often being found in carriers of the low-expressing short (S) allele. Nevertheless, the role of the 5-HTTLPR in influencing parasympathetic stress reactivity, as indexed by Respiratory Sinus Arrhythmia (RSA), is still unknown. This study examined, for the first time, whether the 5-HTTLPR was associated with variations in RSA response to maternal separation in a sample of 69 healthy 5-year-old children. Preschoolers' RSA was measured during an age-adapted version of the Strange Situation Procedure (SSP). The 5-HTTLPR polymorphism was tested as a predictor of RSA dynamic response to the SSP through multilevel models. A significant interaction between 5-HTTLPR and SSP episodes was found. In particular, whereas a significant decrease in RSA levels was observed during the stranger episode in the whole sample, S allele carriers showed a significant decrease in RSA levels from the stranger episode to the first separation episode, followed by an increase for the rest of the procedure. Albeit preliminary, data support the view that the 5-HTTLPR may contribute to individual differences in RSA stress reactivity from preschool age.

Information Retrieval from Photoplethysmographic Sensors: A Comprehensive Comparison of Practical Interpolation and Breath-Extraction Techniques at Different Sampling Rates.

The increasingly widespread diffusion of wearable devices makes possible the continuous monitoring of vital signs, such as heart rate (HR), heart rate variability (HRV), and breath signal. However, these devices usually do not record the "gold-standard" signals, namely the electrocardiography (ECG) and respiratory activity, but a single photoplethysmographic (PPG) signal, which can be exploited to estimate HR and respiratory activity. In addition, these devices employ low sampling rates to limit power consumption. Hence, proper methods should be adopted to compensate for the resulting increased discretization error, while diverse breath-extraction algorithms may be differently sensitive to PPG sampling rate. Here, we assessed the efficacy of parabola interpolation, cubic-spline, and linear regression methods to improve the accuracy of the inter-beat intervals (IBIs) extracted from PPG sampled at decreasing rates from 64 to 8 Hz. PPG-derived IBIs and HRV indices were compared with those extracted from a standard ECG. In addition, breath signals extracted from PPG using three different techniques were compared with the gold-standard signal from a thoracic belt. Signals were recorded from eight healthy volunteers during an experimental protocol comprising sitting and standing postures and a controlled respiration task. Parabola and cubic-spline interpolation significantly increased IBIs accuracy at 32, 16, and 8 Hz sampling rates. Concerning breath signal extraction, the method holding higher accuracy was based on PPG bandpass filtering. Our results support the efficacy of parabola and spline interpolations to improve the accuracy of the IBIs obtained from low-sampling rate PPG signals, and also indicate a robust method for breath signal extraction.

Assessing stress variations in children during the strange situation procedure: comparison of three widely used respiratory sinus arrhythmia estimation methods.

Objective.The respiratory sinus arrhythmia (RSA) is a well-known marker of vagal activity that can be exploited to measure stress changes. RSA is usually estimated from heart rate variability (HRV). This study aims to compare the RSA obtained with three widely adopted methods showing their strengths and potential pitfalls.Approach.The three methods are tested on 69 healthy preschoolers undergoing a stressful protocol, the strange situation procedure (SSP). We compare the RSA estimated by the Porges method, the univariate autoregressive (AR) spectral analysis of the HRV signal, and the bivariate AR spectral analysis of HRV and respirogram signals. We examine RSA differences detected across the SSP episodes and correlation between the estimates provided by each method.Main results.The Porges and the bivariate AR approaches both detected significant differences (i.e. stress variations) in the RSA measured across the SSP. However, the latter method showed higher sensitivity to stress changes induced by the procedure, with the mean RSA variation between baseline and first separation from the mother (the most stressful condition) being significantly different among methods: Porges, -17.5%; univariate AR, -18.3%; bivariate AR, -23.7%. Moreover, the performances of the Porges algorithm were found strictly dependent on the applied preprocessing.Significance.Our findings confirm the bivariate AR analysis of the HRV and respiratory signals as a robust stress assessment tool that does not require any population-specific preprocessing of the signals and warn about using RSA estimates that neglect breath information in more natural experiments, such as those involving children, in which respiratory frequency changes are extremely likely.

Exploration of the physiological response to an online gambling task by frequency domain analysis of the electrodermal activity.

Online gambling has dramatically increased over the last decades, thus the study of the underlying physiological mechanisms could be helpful to better understand related disorders. Specifically, physiological arousal is well-known to play a key role in gambling behavior. In the present study, unconventional frequency feature of the electrodermal activity (EDA) was extracted (EDASympn) and compared to the most common heart rate variability (HRV) spectral parameters (LF, HF, HFn, LF/HF) to measure arousal during an online gambling session. 46 subjects played online slot machines for 30 minutes, while EDA and ECG were recorded. In the analysis the gaming session was divided into three 10-minutes-long phases. A one-way repeated measures analysis of variance was carried out for each spectral parameter, with the game phases as within-subjects factor. All the calculated parameters showed significant differences between the initial phase of the game and the last two (p < 0.001). In particular, EDAsympn displayed a reciprocal trend with respect to HFn: an initial increase (decrease for HFn) was followed by a plateau phase. LF exhibited a significant difference also between the second and the third phases. EDA frequency-domain analysis appears to be a promising method for physiological arousal assessment, by showing the same discriminative power of HRV spectral components. Further research is needed to emphasize these findings.Clinical Relevance-This promotes the use of a new and easy-to-implement method to assess sympathetic activity.

Bicoherence Interpretation in EEG Requires Signal to Noise Ratio Quantification: An Application to Sensorimotor Rhythms.

OBJECTIVE: In the electroencephalogram (EEG) the quadratic phase coupling (QPC) phenomenon indicates the presence of non-linear interactions among brain rhythms that could affect the interpretation of their physiological meaning. We propose the use of the bicoherence as a QPC quantification method to understand the nature of brain rhythm interplay. METHODS: We firstly provide a simulation study to show under which condition of signal to noise ratio (SNR) the bicoherence is a reliable QPC quantifier and how to interpret the results. Secondly, in the light of the simulation results, we applied the bicoherence analysis to real EEG data acquired on thirteen volunteers during a cue-paced reaching motor task to quantify coupling and decoupling between mu and beta rhythms. An inter-trial averaging procedure was adopted in order to allow the correct calculation of the bicoherence during a motor task. RESULTS: Simulations demonstrated that SNR has a strong impact on the correct quantification of bicoherence and that a reliable detection of QPC is possible when the SNR is favorable (>-5 dB). Results from EEG data demonstrated a QPC between mu and beta rhythms during the resting state and its fading during movement planning and execution, providing valuable information for the interpretation of their dynamics. CONCLUSION: The bicoherence was proven to be an effective tool for the investigation of coupling between the sensorimotor rhythms during all the phases of a motor task. This was assessed in relation to the physiological changing of the SNR characterizing the frequency components of interest.

Heart Rate Variability from Wearables: A Comparative Analysis Among Standard ECG, a Smart Shirt and a Wristband.

Heart Rate Variability (HRV) derived from standard one-lead electrocardiography (ECG) was compared with HRV computed by a commercial ECG shirt and with the inter-beat-intervals (IBI) measured by a research-grade photoplethysmographic (PPG) wristband. Signals from 8 subjects were recorded in three experimental phases: during sit; in upright position ("stand"); during controlled respiration. HRV and IBI from both the wearables were computed online (i.e. during the experiment) and stored for subsequent analyses, while the standard ECG was processed offline with state-of-the-art methods to obtain a clean reference HRV. Shirt and wristband signals accuracies were assessed, with respect to the reference HRV, through a between-phase and a beat-to-beat analyses. The former considered several time and frequency domain parameters; the latter was carried out through the Bland-Altman method. Time and frequency domain parameters computed from shirt HRV resulted very similar to the ones extracted from the reference HRV and generally more accurate than the parameters computed from wristband IBI. The Bland-Altman analysis showed that wristband IBI is significantly different from ECG-derived HRV, especially during "stand". Therefore, our results support the idea that some care should be paid in the interpretation of online PPG-derived IBI, while HRV measures online-derived from ECG-shirts seem to be more reliable in the analyzed conditions. The high number of missing beats also suggest that the design of wristband devices should be taken into account to reduce the rate of incorrect measurements, by maximizing sensor adhesion to the skin.

Investigating the Optimal Baseline Positioning to Maximize Cognitive Experimental Outcome.

In emotional and cognitive research, the baseline is commonly used for standardization purposes in order to have a reference for the identification of the activation. Since no previous studies have investigated which moment of the experiment could be considered optimal for baseline evaluation, we designed an experimental protocol to analyze which time interval could be considered more effective in highlighting differences between the baseline state and the cognitive effort exhibited during tasks (specifically, reaction and working memory tasks). Several indexes were extracted from EEG signals during the visualization of the considered baseline stimuli and the execution of tasks. From our results, as regards to the considered Global Field Power (GFP) indexes (Attention and Memorization indexes), the last baseline stimulus seems to be the best one to highlight the difference in cognitive workload between the individual baseline condition and the two cognitive tasks. Instead, in terms of Engagement index (EI), the difference between Reaction Task (RT) and the individual baseline condition seems to be best highlighted by the relaxing video right after performed tasks. In conclusion, the best baseline position to maximize the differences in cognitive workload may vary among the considered indexes because of confounding effects and individual differences, but further analyses are required to validate this result.

Towards the development of physiological models for emotions evaluation.

In the last decades numerous researches have revealed a strong link between emotions and several physiological responses. However, the automatic recognition of emotions still remains a challenge. In this work we describe a novel approach to estimate valence, arousal and dominance values from various biological parameters (derived from electrodermal activity, heart rate variability signal and electroencephalography), by means of multiple linear regression models. The models training was performed by using a set of pictures pre-evaluated in terms of valence, arousal and dominance, selected from the International Affective Picture System (IAPS) database. By using the step-wise regression method, all the possible combinations of considered biological parameters were tested as input variables for the models. The three multiple linear regression models that could provide the best fit for IAPS pictures valence, arousal and dominance values were selected. The features included in the optimal models were the average of the inter-beat duration (mean RR), the EEG spectral power computed in alpha, beta and theta frequency bands (Alpha, Beta and Theta power) and the average value of EDA signal (mean EDA). The obtained models show an overall good performance in predicting valence, arousal and dominance values.