The impact of skin preparation method on electrocardiogram quality in horses

Objective

Several skin preparation techniques are used in electrocardiogram (ECG) monitoring of horses. The objective of this study was to determine which methods produce the greatest signal quality using textile electrodes and standard silver/silver chloride (Ag/AgCl) electrodes.

Animals and samples

Electrocardiogram data were collected using textile and Ag/AgCl electrodes simultaneously for 4 skin preparation techniques in 6 horses.

Procedure

The effects of skin preparation (cleansing with isopropyl alcohol, with or without shaving the hair) and the effects of the application of a conductive gel were assessed using metrics of signal quality.

Results

Shaving and cleansing with alcohol had no effect on signal quality for either electrode type. The Ag/AgCl electrodes contain a solid gel, and the application of additional gel did not affect signal quality. Data quality was significantly improved when gel was applied to textile electrodes. Furthermore, there was no difference in signal quality between electrode types when gel was used.

Conclusion and clinical relevance

This study suggests that skin preparation by cleansing and/or shaving does not have a significant effect on equine ECG signal quality. When gel is used, textile electrodes are a practical alternative for Ag/AgCl electrodes, as they produce ECG recordings of the same quality.

Automated Multi-Wavelength Quality Assessment of Photoplethysmography Signals Using Modulation Spectrum Shape Features

Photoplethysmography (PPG) is used to measure blood volume changes in the microvascular bed of tissue. Information about these changes along time can be used for estimation of various physiological parameters, such as heart rate variability, arterial stiffness, and blood pressure, to name a few. As a result, PPG has become a popular biological modality and is widely used in wearable health devices. However, accurate measurement of various physiological parameters requires good-quality PPG signals. Therefore, various signal quality indexes (SQIs) for PPG signals have been proposed. These metrics have usually been based on statistical, frequency, and/or template analyses. The modulation spectrogram representation, however, captures the second-order periodicities of a signal and has been shown to provide useful quality cues for electrocardiograms and speech signals. In this work, we propose a new PPG quality metric based on properties of the modulation spectrum. The proposed metric is tested using data collected from subjects while they performed various activity tasks contaminating the PPG signals. Experiments on this multi-wavelength PPG dataset show the combination of proposed and benchmark measures significantly outperforming several benchmark SQIs with improvements of 21.3% BACC (balanced accuracy) for green, 21.6% BACC for red, and 19.0% BACC for infrared wavelengths, respectively, for PPG quality detection tasks. The proposed metrics also generalize for cross-wavelength PPG quality detection tasks.

Validation of an Equine Smart Textile System for Heart Rate Variability: A Preliminary Study

Electrocardiograms (ECGs), and associated heart rate (HR) and heart rate variability (HRV) measurements, are essential in assessing equine cardiovascular health and fitness. Smart textiles have gained popularity, but limited validation work has been conducted. Therefore, the objective of this study was to compare HR and HRV data obtained using a smart textile system (Myant) to the gold-standard telemetric device (Televet). Simultaneous ECGs were obtained using both systems in seven horses at rest and during a submaximal exercise test. Bland-Altman tests were used to assess agreement between the two devices. Strong to perfect correlations without significant differences between the two devices were observed for all metrics assessed. During exercise, mean biases of 0.31 bpm (95% limits of agreement: -1.99 to 2.61) for HR, 1.43 ms (-11.48 to 14.33) for standard deviation of R-R intervals (SDRR), and 0.04 (-2.30 to 2.38) for the HRV triangular index (TI) were observed. A very strong correlation was found between the two devices for HR (r = 0.9993, p < 0.0001) and for HRV parameters (SDRR r = 0.8765, p < 0.0001; TI r = 0.8712, p < 0.0001). This study demonstrates that a smart textile system is reliable for assessment of HR and HRV of horses at rest and during submaximal exercise.

A Smart Textile Band Achieves High-Quality Electrocardiograms in Unrestrained Horses

Electrocardiography (ECG) is an essential tool in assessing equine health and fitness. However, standard ECG devices are expensive and rely on the use of adhesive electrodes, which may become detached and are associated with reduced ECG quality over time. Smart textile electrodes composed of stainless-steel fibers have previously been shown to be a suitable alternative in horses at rest and during exercise. The objective of this study was to compare ECG quality using a smart textile girth band knit with silver and carbon yarns to standard adhesive silver/silver chloride (Ag/AgCl) electrodes. Simultaneous three-lead ECGs were recorded using a smart textile band and Ag/AgCl electrodes in 22 healthy, mixed-breed horses that were unrestrained in stalls. ECGs were compared using the following quality metrics: Kurtosis (k) value, Kurtosis signal quality index (kSQI), percentage of motion artifacts (%MA), peak signal amplitude, and heart rate (HR). Two-way ANOVA with Tukey’s multiple comparison tests was conducted to compare each metric. No significant differences were found in any of the assessed metrics between the smart textile band and Ag/AgCl electrodes, with the exception of peak amplitude. Kurtosis and kSQI values were excellent for both methods (textile mean k = 21.8 ± 6.1, median kSQI = 0.98 [0.92−1.0]; Ag/AgCl k = 21.2 ± 7.6, kSQI = 0.99 [0.97−1.0]) with <0.5% (<1 min) of the recording being corrupted by MAs for both. This study demonstrates that smart textiles are a practical and reliable alternative to the standard electrodes typically used in ECG monitoring of horses.

Exploring textile-based electrode materials for electromyography smart garments

Introduction

In recent years, electromyography (EMG) has been increasingly studied for wearable applications. Conventional gel electrodes for electrophysiological recordings have limited use in everyday applications such as prosthetic control or muscular therapy at home. This study investigates the efficacy and feasibility of dry-contact electrode materials employed in smart textiles for EMG recordings.

Methods

Dry-contact electrode materials were selected and implemented on textile substrates. Using these electrodes, EMG was recorded from the forearm of able-bodied subjects. 25% and 50% isometric maximum voluntary contractions were captured. A comparative investigation was performed against gel electrodes, assessing the effect of material properties on signal fidelity and strength compared.

Results

When isolating for electrode surface area and pressure, 31 of the 40 materials demonstrated strong positive correlations in their mean PSD with gel electrodes (r > 95, p < 0.001). The inclusion of ionic liquids in the material composition, and using raised or flat electrodes, did not demonstrate a significant effect in signal quality.

Conclusions

For EMG dry-contact electrodes, comparing the performance against gel electrodes for the application with the selected material is important. Other factors recommended to be studied are electrodes’ durability and long-term stability.

Evaluation of dry textile electrodes for long-term electrocardiographic monitoring

Background

Continuous long-term electrocardiography monitoring has been increasingly recognized for early diagnosis and management of different types of cardiovascular diseases. To find an alternative to Ag/AgCl gel electrodes that are improper for this application scenario, many efforts have been undertaken to develop novel flexible dry textile electrodes integrated into the everyday garments. With significant progresses made to address the potential issues (e.g., low signal-to-noise ratio, high skin–electrode impedance, motion artifact, and low durability), the lack of standard evaluation procedure hinders the further development of dry electrodes (mainly the design and optimization).

Results

A standard testing procedure and framework for skin–electrode impedance measurement is demonstrated for the development of novel dry textile electrodes. Different representative electrode materials have been screen-printed on textile substrates. To verify the performance of dry textile electrodes, impedance measurements are conducted on an agar skin model using a universal setup with consistent frequency and pressure. In addition, they are demonstrated for ECG signals acquisition, in comparison to those obtained using conventional gel electrodes.

Conclusions

Dry textile electrodes demonstrated similar impedance when in raised or flat structures. The tested pressure variations had an insignificant impact on electrode impedance. Looking at the effect of impedance on ECG signals, a noticeable effect on ECG signal performance metrics was not observed. Therefore, it is suggested that impedance alone is possibly not the primary indicator of signal quality. As well, the developed methods can also serve as useful guidelines for future textile dry-electrode design and testing for practical ECG monitoring applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12938-021-00905-4.

Modeling and Reproducing Textile Sensor Noise: Implications for Textile-Compatible Signal Processing Algorithms

Smart textiles provide an opportunity to simultaneously record various electrophysiological signals from the human body, such as ECG, in a non-invasive and continuous manner. Accurate processing of ECG signals recorded using textile sensors is challenging due to the very low signal-to-noise ratio (SNR). Signal processing algorithms that can extract ECG signal out of textile-based electrode recordings, despite low SNR are needed. Presently, there are no textile ECG datasets available to develop, test and validate these algorithms. In this paper we attempted to model textile ECG signals by adding the textile sensor noise to open access ECG signals. We employed the linear predictive coding method to model different features of this noise. By approximating the linear predictive coding residual signals using Kernel Density Estimation, an artificial textile ECG noise signal was generated by filtering the residual signal with the linear predictive coding coefficients. The obtained textile sensor noise was added to the MIT-BIH Arrhythmia Database (MITDB), thus creating Textile-like ECG dataset consisting of 108 channels (30 min each). Furthermore, a Python code for generating textile-like ECG signals with variable SNR was also made available online. Finally, to provide a benchmark for the performance of R-peak detection algorithms on textile ECG, the five common R-peak detection algorithms: Pan & Tompkins, improved Pan & Tompkins (in Biosppy), Hamilton, Engelse, and Khamis, were tested on textile-like MITDB. This work provides an approach to generating noisy textile ECG signals, and facilitating the development, testing, and evaluation of signal processing algorithms for textile ECGs.

Toward New Modalities in VEP-Based BCI Applications Using Dynamical Stimuli: Introducing Quasi-Periodic and Chaotic VEP-Based BCI

Visual evoked potentials (VEPs) to periodic stimuli are commonly used in brain computer interfaces for their favorable properties such as high target identification accuracy, less training time, and low surrounding target interference. Conventional periodic stimuli can lead to subjective visual fatigue due to continuous and high contrast stimulation. In this study, we compared quasi-periodic and chaotic complex stimuli to common periodic stimuli for use with VEP-based brain computer interfaces (BCIs). Canonical correlation analysis (CCA) and coherence methods were used to evaluate the performance of the three stimulus groups. Subjective fatigue caused by the presented stimuli was evaluated by the Visual Analogue Scale (VAS). Using CCA with the M2 template approach, target identification accuracy was highest for the chaotic stimuli (M = 86.8, SE = 1.8) compared to the quasi-periodic (M = 78.1, SE = 2.6, p = 0.008) and periodic (M = 64.3, SE = 1.9, p = 0.0001) stimulus groups. The evaluation of fatigue rates revealed that the chaotic stimuli caused less fatigue compared to the quasi-periodic (p = 0.001) and periodic (p = 0.0001) stimulus groups. In addition, the quasi-periodic stimuli led to lower fatigue rates compared to the periodic stimuli (p = 0.011). We conclude that the target identification results were better for the chaotic group compared to the other two stimulus groups with CCA. In addition, the chaotic stimuli led to a less subjective visual fatigue compared to the periodic and quasi-periodic stimuli and can be suitable for designing new comfortable VEP-based BCIs.

Optimization of Visual Stimulus Sequence in a Brain-Computer Interface Based on Code Modulated Visual Evoked Potentials

Brain-computer interfaces based on code-modulated visual evoked potentials provide high information transfer rates, which make them promising alternative communication tools. Circular shifts of a binary sequence are used as the flickering pattern of several visual stimuli, where the minimum correlation between them is critical for recognizing the target by analyzing the EEG signal. Implemented sequences have been borrowed from communication theory without considering visual system physiology and related ergonomics. Here, an approach is proposed to design optimum stimulus sequences considering physiological factors, and their superior performance was demonstrated for a 6-target c-VEP BCI system. This was achieved by defining a time-factor index on the frequency response of the sequence, while the autocorrelation index ensured a low correlation between circular shifts. A modified version of the non-dominated sorting genetic algorithm II (NSGAII) multi-objective optimization technique was implemented to find, for the first time, 63-bit sequences with simultaneously optimized autocorrelation and time-factor indexes. The selected optimum sequences for general (TFO) and 6-target (6TO) BCI systems, were then compared with m-sequence by conducting experiments on 16 participants. Friedman tests showed a significant difference in perceived eye irritation between TFO and m-sequence (p = 0.024). Generalized estimating equations (GEE) statistical test showed significantly higher accuracy for 6TO compared to m-sequence (p = 0.006). Evaluation of EEG responses showed enhanced SNR for the new sequences compared to m-sequence, confirming the proposed approach for optimizing the stimulus sequence. Incorporating physiological factors to select sequence(s) used for c-VEP BCI systems improves their performance and applicability.

Modeling and Reconstructing Textile Sensor Noise: Implications for Wearable Technology

Wearable sensors enable the simultaneous recording of several electrophysiological signals from the human body in a non-invasive and continuous manner. Textile sensors are garnering substantial interest in the wearable technology because they can be knitted directly into the daily-used objects like underwear, bra, dress, etc. However, accurate processing of signals recorded by textile sensors is extremely challenging due to the very low signal-to-noise ratio (SNR). Systematic classification of textile sensor noise (TSN) is necessary to: (i) identify different types of noise and their statistical characteristics, (ii) explore how each type of noise influences the electrophysiological signal, (iii) develop optimal textile-specific electronics that suppress TSN, and (iv) reproduce TSN and create large dataset of textile sensors to validate various machine learning and signal processing algorithms. In this paper, we develop a novel technique to classify textile sensor artifacts in ECG signals. By simultaneously recording signals from the waist (textile sensors) and chest (gel electrode), we extract TSN by removing the chest ECG signal from the recorded textile data. We classify TSN based on its morphological and statistical features in two main categories, namely, slow and fast. Linear prediction coding (LPC) is utilized to model each class of TSN by auto-regression coefficients and residues. The residual signal can be approximated by Gaussian distribution which enables reproducing slow and fast artifacts that not only preserve the similar morphological features but also fulfill the statistical properties of TSN. By reproducing TSN and adding them to clean ECG signals, we create a textile-like ECG signal which can be used to develop and validate different signal processing algorithms.

Multichannel ECG recording from waist using textile sensors

BACKGROUND

The development of wearable health monitoring systems is garnering tremendous interest in research, technology and commercial applications. Their ability of providing unique capabilities in continuous, real-time, and non-invasive tracking of the physiological markers of users can provide insights into the performance and health of individuals. Electrocardiogram (ECG) signals are of particular interest, as cardiovascular disease is the leading cause of death globally. Monitoring heart health and its conditions such as ventricular disturbances and arrhythmias can be achieved through evaluating various features of ECG such as R-peaks, QRS complex, T-wave, and P-wave. Despite recent advances in biosensors for wearable applications, most of the currently available solutions rely solely on a single system attached to the body, limiting the ability to obtain reliable and multi-location biosignals. However, in engineering systems, sensor fusion, which is the optimal integration and processing of data from multiple sensors, has been a common theme and should be considered for wearables. In recent years, due to an increase in the availability and variety of different types of sensors, the possibility of achieving sensor fusion in wearable systems has become more attainable. Sensor fusion in multi-sensing systems results in significant enhancements of information inferences compared to those from systems with a sole sensor. One step towards the development of sensor fusion for wearable health monitoring systems is the accessibility to multiple reliable electrophysiological signals, which can be recorded continuously.

RESULTS

In this paper, we develop a textile-based multichannel ECG band that has the ability to measure ECG from multiple locations on the waist. As a proof of concept, we demonstrate that ECG signals can be reliably obtained from different locations on the waist where the shape of the QRS complex is nearly comparable with recordings from the chest using traditional gel electrodes. In addition, we develop a probabilistic approach-based on prediction and update strategies-to detect R-peaks from noisy textile data in different statuses, including sitting, standing, and jogging. In this approach, an optimal search method is utilized to detect R-peaks based on the history of the intervals between previously detected R-peaks. We show that the performance of our probabilistic approach in R-peak detection is significantly better than that based on Pan-Tompkins and optimal-threshold methods.

CONCLUSION

A textile-based multichannel ECG band was developed to track the heart rate changes from multiple locations on the waist. We demonstrated that (i) the ECG signal can be detected from different locations on the waist, and (ii) the accuracy of the detected R-peaks from textile sensors was improved by using our proposed probabilistic approach. Despite the limitations of the textile sensors that might compromise the quality of ECG signals, we anticipate that the textile-based multichannel ECG band can be considered as an effective wearable system to facilitate the development of sensor fusion methodology for pervasive and non-invasive health monitoring through continuous tracking of heart rate variability (HRV) from the waist. In addition, from the commercialization point of view, we anticipate that the developed band has the potential to be integrated into garments such as underwear, bras or pants so that individuals can use it on a daily basis.

Comparing Steady-State Visually Evoked Potentials Frequency Estimation Methods in Brain-Computer Interface With the Minimum Number of EEG Channels

Introduction

Brain-Computer Interface (BCI) systems provide a communication pathway between users and systems. BCI systems based on Steady-State Visually Evoked Potentials (SSVEP) are widely used in recent decades. Different feature extraction methods have been introduced in the literature to estimate SSVEP responses to BCI applications.

Methods

In this study, the new algorithms, including Canonical Correlation Analysis (CCA), Least Absolute Shrinkage and Selection Operator (LASSO), L1-regularized Multi-way CCA (L1-MCCA), Multi-set CCA (MsetCCA), Common Feature Analysis (CFA), and Multiple Logistic Regression (MLR) are compared using proper statistical methods to determine which one has better performance with the least number of EEG electrodes.

Results

It was found that MLR, MsetCCA, and CFA algorithms provided the highest performances and significantly outperformed CCA, LASSO, and L1-MCCA algorithms when using 8 EEG channels. However, when using only 1 or 2 EEG channels d, CFA method provided the highest F-scores. This algorithm not only outperformed MLR and MsetCCA when applied on different electrode montages but also provided the fastest computation time on the test set.

Conclusion

Although MLR method has already demonstrated to have higher performance in comparison with other frequency recognition algorithms, this study showed that in a practical SSVEP-based BCI system with 1 or 2 EEG channels and short-time windows, CFA method outperforms other algorithms. Therefore, it is proposed that CFA algorithm is a promising choice for the expansion of practical SSVEP-based BCI systems.

Introducing chaotic codes for the modulation of code modulated visual evoked potentials (c-VEP) in normal adults for visual fatigue reduction

Code modulated Visual Evoked Potentials (c-VEP) based BCI studies usually employ m-sequences as a modulating codes for their broadband spectrum and correlation property. However, subjective fatigue of the presented codes has been a problem. In this study, we introduce chaotic codes containing broadband spectrum and similar correlation property. We examined whether the introduced chaotic codes could be decoded from EEG signals and also compared the subjective fatigue level with m-sequence codes in normal subjects. We generated chaotic code from one-dimensional logistic map and used it with conventional 31-bit m-sequence code. In a c-VEP based study in normal subjects (n = 44, 21 females) we presented these codes visually and recorded EEG signals from the corresponding codes for their four lagged versions. Canonical correlation analysis (CCA) and spatiotemporal beamforming (STB) methods were used for target identification and comparison of responses. Additionally, we compared the subjective self-declared fatigue using VAS caused by presented m-sequence and chaotic codes. The introduced chaotic code was decoded from EEG responses with CCA and STB methods. The maximum total accuracy values of 93.6 ± 11.9% and 94 ± 14.4% were achieved with STB method for chaotic and m-sequence codes for all subjects respectively. The achieved accuracies in all subjects were not significantly different in m-sequence and chaotic codes. There was significant reduction in subjective fatigue caused by chaotic codes compared to the m-sequence codes. Both m-sequence and chaotic codes were similar in their accuracies as evaluated by CCA and STB methods. The chaotic codes significantly reduced subjective fatigue compared to the m-sequence codes.

An Adaptive SSVEP-Based Brain-Computer Interface to Compensate Fatigue-Induced Decline of Performance in Practical Application

Brain-computer interfaces based on steady-state visual evoked potentials are promising communication systems for people with speech and motor disabilities. However, reliable SSVEP response requires user's attention, which degrades over time due to significant eye-fatigue when low-frequency visual stimuli (5-15 Hz) are used. Previous studies have shown that eye-fatigue can be reduced using high-frequency flickering stimuli (>25 Hz). Here, it is quantitatively demonstrated that the performance of a high-frequency SSVEP BCI decreases over time, but this amount of decrease can be compensated effectively by using two proposed adaptive algorithms. This leaded to a robust alternative communication system for practical applications. The asynchronous spelling system implemented in this study uses a threshold-based version of LASSO algorithm for frequency recognition. In long online experiments, when participants typed a sentence with the BCI system for 16 times, accuracy of the system was close to its maximum along the experiment. However, regression analysis on typing speed of each sentence demonstrated a significant decrease in all 7 subjects ( ) when thresholds obtained from a calibration test were kept fixed over the experiment. In comparison, no significant change in typing speed was observed when the proposed adaptive algorithms were used. The analysis of variances revealed that the average typing speed of the last four sentences when using adaptive relational algorithm (8.7 char/min) was significantly higher than the tolerance-based algorithm (8.1 char/min) and significantly above 6 char/min when the fixed thresholds were used. Therefore, the relational algorithm proposed in this paper could successfully compensate for the effect of fatigue on performance of the SSVEP BCI system.

Use of Sine Shaped High-Frequency Rhythmic Visual Stimuli Patterns for SSVEP Response Analysis and Fatigue Rate Evaluation in Normal Subjects

Background: Recent EEG-SSVEP signal based BCI studies have used high frequency square pulse visual stimuli to reduce subjective fatigue. However, the effect of total harmonic distortion (THD) has not been considered. Compared to CRT and LCD monitors, LED screen displays high-frequency wave with better refresh rate. In this study, we present high frequency sine wave simple and rhythmic patterns with low THD rate by LED to analyze SSVEP responses and evaluate subjective fatigue in normal subjects. Materials and Methods: We used patterns of 3-sequence high-frequency sine waves (25, 30, and 35 Hz) to design our visual stimuli. Nine stimuli patterns, 3 simple (repetition of each of above 3 frequencies e.g., P25-25-25) and 6 rhythmic (all of the frequencies in 6 different sequences e.g., P25-30-35) were chosen. A hardware setup with low THD rate (<0.1%) was designed to present these patterns on LED. Twenty two normal subjects (aged 23-30 (25 ± 2.1) yrs) were enrolled. Visual analog scale (VAS) was used for subjective fatigue evaluation after presentation of each stimulus pattern. PSD, CCA, and LASSO methods were employed to analyze SSVEP responses. The data including SSVEP features and fatigue rate for different visual stimuli patterns were statistically evaluated. Results: All 9 visual stimuli patterns elicited SSVEP responses. Overall, obtained accuracy rates were 88.35% for PSD and > 90% for CCA and LASSO (for TWs > 1 s). High frequency rhythmic patterns group with low THD rate showed higher accuracy rate (99.24%) than simple patterns group (98.48%). Repeated measure ANOVA showed significant difference between rhythmic pattern features (P < 0.0005). Overall, there was no significant difference between the VAS of rhythmic [3.85 ± 2.13] compared to the simple patterns group [3.96 ± 2.21], (P = 0.63). Rhythmic group had lower within group VAS variation (min = P25-30-35 [2.90 ± 2.45], max = P35-25-30 [4.81 ± 2.65]) as well as least individual pattern VAS (P25-30-35). Discussion and Conclusion: Overall, rhythmic and simple pattern groups had higher and similar accuracy rates. Rhythmic stimuli patterns showed insignificantly lower fatigue rate than simple patterns. We conclude that both rhythmic and simple visual high frequency sine wave stimuli require further research for human subject SSVEP-BCI studies.

An Efficient Asynchronous High-Frequency Steady-State Visual Evoked Potential-Based Brain-Computer Interface speller: The Problem of Individual Differences

Background

Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) provide high rates of accuracy and information transfer rate, but need user's attention to flickering visual stimuli. This quickly leads to eye-fatigue when the flickering frequency is in the low-frequency range. High-frequency flickering stimuli (>30 Hz) have been proposed with significantly lower eye-fatigue. However, SSVEP responses in this frequency range are remarkably weaker, leading to doubts about usability of high-frequency stimuli to develop efficient BCI systems. The purpose of this study was to evaluate if a practical SSVEP Speller can be developed with Repetitive Visual Stimuli in the high-frequency range.

Methods

An asynchronous high-frequency (35-40 Hz) speller for typing in Persian language was developed using five flickering visual stimuli. Least absolute shrinkage and selection operator algorithm with two user-calibrated thresholds was used to detect the user's selections. A total of 14 volunteers evaluated the system in an ordinary office environment to type 9 sentences consist of 81 characters with a multistage virtual keyboard.

Results

Despite very high performance of 6.9 chars/min overall typing speed, average accuracy of 98.3%, and information transfer rate of 64.9 bpm for eight of the participants, the other six participants had serious difficulty in spelling with the system and could not complete the typing experiment.

Conclusions

The results of this study in accordance with some previous studies suggest that high rate of illiteracy in high-frequency SSVEP-based BCI systems may be a major burden for their practical application.

Evaluation of a Low-cost and Low-noise Active Dry Electrode for Long-term Biopotential Recording

Wet Ag/AgCl electrodes, although very popular in clinical diagnosis, are not appropriate for expanding applications of wearable biopotential recording systems which are used repetitively and for a long time. Here, the development of a low-cost and low-noise active dry electrode is presented. The performance of the new electrodes was assessed for recording electrocardiogram (ECG) and electroencephalogram (EEG) in comparison with that of typical gel-based electrodes in a series of long-term recording experiments. The ECG signal recorded by these electrodes was well comparable with usual Ag/AgCl electrodes with a correlation up to 99.5% and mean power line noise below 6.0 μV_RMS. The active electrodes were also used to measure alpha wave and steady state visual evoked potential by recording EEG. The recorded signals were comparable in quality with signals recorded by standard gel electrodes, suggesting that the designed electrodes can be employed in EEG-based rehabilitation systems and brain-computer interface applications. The mean power line noise in EEG signals recorded by the active electrodes (1.3 μV_RMS) was statistically lower than when conventional gold cup electrodes were used (2.0 μV_RMS) with a significant level of 0.05, and the new electrodes appeared to be more resistant to the electromagnetic interferences. These results suggest that the developed low-cost electrodes can be used to develop wearable monitoring systems for long-term biopotential recording.

A practical efficient human computer interface based on saccadic eye movements for people with disabilities

Human computer interfaces (HCI) provide new channels of communication for people with severe motor disabilities to state their needs, and control their environment. Some HCI systems are based on eye movements detected from the electrooculogram. In this study, a wearable HCI, which implements a novel adaptive algorithm for detection of saccadic eye movements in eight directions, was developed, considering the limitations that people with disabilities have. The adaptive algorithm eliminated the need for calibration of the system for different users and in different environments. A two-stage typing environment and a simple game for training people with disabilities to work with the system were also developed. Performance of the system was evaluated in experiments with the typing environment performed by six participants without disabilities. The average accuracy of the system in detecting eye movements and blinking was 82.9% at first tries with an average typing rate of 4.5cpm. However an experienced user could achieve 96% accuracy and 7.2cpm typing rate. Moreover, the functionality of the system for people with movement disabilities was evaluated by performing experiments with the game environment. Six people with tetraplegia and significant levels of speech impairment played with the computer game several times. The average success rate in performing the necessary eye movements was 61.5%, which increased significantly with practice up to 83% for one participant. The developed system is 2.6×4.5cm in size and weighs only 15g, assuring high level of comfort for the users.

Design and Implementation of a Portable Impedance Cardiography System for Noninvasive Stroke Volume Monitoring

Measurement of the stroke volume (SV) and its changes over time can be very helpful for diagnosis of dysfunctions in the blood circulatory system and monitoring their treatments. Impedance cardiography (ICG) is a simple method of measuring the SV based on changes in the instantaneous mean impedance of the thorax. This method has received much attention in the last two decades because it is noninvasive, easy to be used, and applicable for continuous monitoring of SV as well as other hemodynamic parameters. The aim of this study was to develop a low-cost portable ICG system with high accuracy for monitoring SV. The proposed wireless system uses a tetrapolar configuration to measure the impedance of the thorax at 50 kHz. The system consists of carefully designed precise voltage-controlled current source, biopotential recorder, and demodulator. The measured impedance was analyzed on a computer to determine SV. After evaluating the system's electronic performance, its accuracy was assessed by comparing its measurements with the values obtained from Doppler echocardiography (DE) on 5 participants. The implemented ICG system can noninvasively provide a continuous measure of SV. The signal to noise ratio of the system was measured above 50 dB. The experiments revealed that a strong correlation (r = 0.89) exists between the measurements by the developed system and DE (P < 0.05). ICG as the sixth vital sign can be measured simply and reliably by the developed system, but more detailed validation studies should be conducted to evaluate the system performance. There is a good promise to upgrade the system to a commercial version domestically for clinical use in the future.

Individual Differences in Personality Traits

Abstract. During the last decade, an increasing number of studies have used neuroscientific methods to examine the relationships between different personality traits and brain structures. This includes the Magnetic Resonance Imaging (MRI)-based analysis of correlations between individual differences in personality traits and the structural variance of specific brain regions. Perfectionism is a personality trait that remains relatively stable over time, and it is influenced by heredity. In this study, the possible brain regions that structurally correlated with both positive and negative perfectionism were investigated. Voxel-based morphometry was used to analyze the whole brain MRI images of 49 participants, and their levels of perfectionism were also evaluated using a standard scale. The statistical analysis revealed significant correlations between negative perfectionism and the gray matter volume of the thalamus and left posterior parietal cortex (precuneus) structures. This finding suggests that differences in perfectionism between individuals may reflect structural variances in these regions of the brain.

An Arbitrary Waveform Wearable Neuro-stimulator System for Neurophysiology Research on Freely Behaving Animals

Portable wireless neuro-stimulators have been developed to facilitate long-term cognitive and behavioral studies on the central nervous system in freely moving animals. These stimulators can provide precisely controllable input(s) to the nervous system, without distracting the animal attention with cables connected to its body. In this study, a low power backpack neuro-stimulator was developed for animal brain researches that can provides arbitrary stimulus waveforms for the stimulation, while it is small and light weight to be used for small animals including rats. The system consists of a controller that uses an RF link to program and activate a small and light microprocessor-based stimulator. A Howland current source was implemented to produce precise current controlled arbitrary waveform stimulations. The system was optimized for ultra-low power consumption and small size. The stimulator was first tested for its electrical specifications. Then its performance was evaluated in a rat experiment when electrical stimulation of medial longitudinal fasciculus induced circling behavior. The stimulator is capable of delivering programmed stimulations up to ± 2 mA with adjusting steps of 1 μA, accuracy of 0.7% and compliance of 6 V. The stimulator is 15 mm × 20 mm × 40 mm in size, weights 13.5 g without battery and consumes a total power of only 5.l mW. In the experiment, the rat could easily carry the stimulator and demonstrated the circling behavior for 0.1 ms current pulses of above 400 μA. The developed system has a competitive size and weight, whereas providing a wide range of operation and the flexibility of generating arbitrary stimulation patterns ideal for long-term experiments in the field of cognitive and neuroscience research.

Computational insights into the neuroprotective action of riluzole on 3-acetylpyridine-induced ataxia in rats

OBJECTIVE

Intra-peritoneal administration of riluzole has been shown to preserve the membrane properties and firing characteristics of Purkinje neurons in a rat model of cerebellar ataxia induced by 3-acetylpyridine (3-AP). However, the exact mechanism(s) by which riluzole restores the normal electrophysiological properties of Purkinje neurons is not completely understood. Changes in the conductance of several ion channels, including the BK channels, have been proposed as a neuro protective target of riluzole. In this study, the possible cellular effects of riluzole on Purkinje cells from 3-AP-induced ataxic rats that could be responsible for its neuro protective action have been investigated by computer simulations.

MATERIALS AND METHODS

This is a computational stimulation study. The simulation environment enabled a change in the properties of the specific ion channels as the possible mechanism of action of riluzole. This allowed us to study the resulted changes in the firing activity of Purkinje cells without concerns about its other effects and interfering parameters in the experiments. Simulations were performed in the NEURON environment (Version 7.1) in a time step of 25 μs; analyses were conducted using MATLAB r2010a (The Mathworks). Data were given as mean ± SEM. Statistical analyses were performed by the student's t test, and differences were considered significant if p< 0.05.

RESULTS

The computational findings demonstrated that modulation of an individual ion channel current, as suggested by previous experimental studies, should not be considered as the only possible target for the neuro protective effects of riluzole to restore the normal firing activity of Purkinje cells from ataxic rats.

CONCLUSION

Changes in the conductance of several potassium channels, including voltage- gated potassium (Kv1, Kv4) and big Ca(2+)-activated K(+) (BK) channels may be responsible for the neuro protective effect of riluzole against 3-AP induced alterations in the firing properties of Purkinje cells in a rat model of ataxia.

Measurement of the current-distance relationship using a novel refractory interaction technique

It is important to know the spatial extent of neural activation around the stimulating electrodes when using extracellular electrical stimulation for the determination of the structure and function of neural circuit connections or for the restoration of function. The current-distance relationship quantifies the relationship between the threshold current for excitation of a neuron, I(th), and the distance between the electrode and the neuron, r, with two parameters: the offset, I(0), and the current-distance constant, k, with a quadratic equation, I(th)(r) = I(0) + kr(2). We proposed a new method to determine the parameters of the current-distance relationship, and thereby estimate the spatial extent of activation, based on the refractory interaction technique. Refractory interaction is a method that exploits the interaction between the regions of activation produced by two electrodes, when the second stimulus is delivered while neurons activated by the first electrode are in their refractory period. Computer simulations of electrical stimulation of a population of nerve fibers were used to determine the accuracy of the method. The mean relative error in k was 19% and in I(0) was 17%, and the spatial extent of stimulation could be determined with an absolute error of 19 microm and a relative error less than 11%. Subsequently, the method was applied to measure the current-distance properties of peripheral motor nerve fibers and indicated that k = 27 microA mm(-2) and I(0) = 49 microA. This method provided robust estimates of the current-distance properties, and provides a means to determine the spatial extent of activation by extracellular stimulation.

Computational evaluation of methods for measuring the spatial extent of neural activation

Knowing of the spatial extent of neural activation around extracellular stimulating electrodes is necessary to ensure that only the desired neurons are activated or to determine which neurons are responsible for an observed response. Various approaches have been used to estimate the current-distance relationship and thereby the spatial extent of activation resulting from extracellular stimulation. However, these approaches all require underlying assumptions and simplifications, and since the actual extent of activation cannot be directly measured, the impact of deviations from these assumptions cannot be determined. We implemented a computer-based model of excitation of a population of nerve fibers and used the model to evaluate a range of approaches proposed for measuring the spatial extent of neural activation. The estimates with each method were compared with measurements of the true spatial extent of activation that were accessible in the simulations to quantify the accuracy of the estimates and to determine the dependence of accuracy on measurement parameters (interelectrode distance, stimulation amplitude, noise). A newly proposed method, based on the refractory interaction technique, provided the most accurate and most robust estimates of the spatial extent of neural activation.