Recurrence quantification analysis during a mental calculation task.

The identification of brain dynamical changes under different cognitive conditions with noninvasive techniques such as electroencephalography (EEG) is relevant for the understanding of their underlying neural mechanisms. The comprehension of these mechanisms has applications in the early diagnosis of neurological disorders and asynchronous brain computer interfaces. In both cases, there are no reported features that could describe intersubject and intra subject dynamics behavior accurately enough to be applied on a daily basis. The present work proposes the use of three nonlinear features (recurrence rate, determinism, and recurrence times) extracted from recurrence quantification analysis (RQA) to describe central and parietal EEG power series complexity in continuous alternating episodes of mental calculation and rest state. Our results demonstrate a consistent mean directional change of determinism, recurrence rate, and recurrence times between conditions. Increasing values of determinism and recurrence rate were present from the rest state to mental calculation, whereas recurrence times showed the opposite pattern. The analyzed features in the present study showed statistically significant changes between rest and mental calculation states in both individual and population analysis. In general, our study described mental calculation EEG power series as less complex systems in comparison to the rest state. Moreover, ANOVA showed stability of RQA features along time.

Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices.

Most methods for sudden cardiac death (SCD) prediction require long-term (24 h) electrocardiogram recordings to measure heart rate variability (HRV) indices or premature ventricular complex indices (with the heartprint method). This work aimed to identify the best combinations of HRV and heartprint indices for predicting SCD based on short-term recordings (1000 heartbeats) through a support vector machine (SVM). Eleven HRV indices and five heartprint indices were measured in 135 pairs of recordings (one before an SCD episode and another without SCD as control). SVMs (defined with a radial basis function kernel with hyperparameter optimization) were trained with this dataset to identify the 13 best combinations of indices systematically. Through 10-fold cross-validation, the best area under the curve (AUC) value as a function of γ (gamma) and cost was identified. The predictive value of the identified combinations had AUCs between 0.80 and 0.86 and accuracies between 80 and 86%. Further SVM performance tests on a different dataset of 68 recordings (33 before SCD and 35 as control) showed AUC = 0.68 and accuracy = 67% for the best combination. The developed SVM may be useful for preventing imminent SCD through early warning based on electrocardiogram (ECG) or heart rate monitoring.

Fabrication and Characterization of a Flexible Thin-Film-Based Array of Microelectrodes for Corneal Electrical Stimulation.

The electric stimulation (ES) of the cornea is a novel therapeutic approach to the treatment of degenerative visual diseases. Currently, ES is delivered by placing a mono-element electrode on the surface of the cornea that uniformly stimulates the eye along the electrode site. It has been reported that a certain degree of correlation exists between the location of the stimulated retinal area and the position of the electrode. Therefore, in this study, we present the development of a sectioned surface electrode for selective electric stimulation of the human cornea. The proposed device consists of 16 independent microelectrodes, a reference electrode, and 18 contact pads. The microelectrodes have a size of 200 µm × 200 µm, are arranged in a 4 × 4 matrix, and cover a total stimulation area of 16 mm2. The proposed fabrication process, based on surface micromachining technology and flexible electronics, uses only three materials: polyimide, aluminum, and titanium, which allow us to obtain a simplified, ergonomic, and reproducible fabrication process. The fabricated prototype was validated to laboratory level by electrical and electrochemical tests, showing a relatively high electrical conductivity and average impedance from 712 kΩ to 1.4 MΩ at the clinically relevant frequency range (from 11 Hz to 30 Hz). Additionally, the biocompatibility of the electrode prototype was demonstrated by performing in vivo tests and by analyzing the polyimide films using Fourier transform infrared spectroscopy (FTIR). The resulting electrode prototype is robust, mechanically flexible, and biocompatible, with a high potential to be used for selective ES of the cornea.

Exploration of the Severity of Hepatic Encephalopathy Deterioration Process Through Dynamics of the EEG Band Power time series.

Cirrhosis is a liver disease that could impair the functionality of the neural system, limiting cognitive processes and reduce mobility, possibly because alterations on dynamics of neural communications. In this study the dynamics of band power fluctuations on ß and γ bands were studied on three groups: control, patients with cirrhosis and patients diagnosed with minimal hepatic encephalopathy. EEG signals were recorded on an oddball paradigm and analyzed to obtain the latency and amplitude of the P300 wave and Detrended Fluctuation Analysis (DFA) exponent values over ß and γ band power time series for each channel. As expected, the latency of the P300 wave was significantly different for control subjects ( ). Amplitudes were not as different, but they tend to decrease for cirrhosis and minimal hepatic encephalopathy groups. DFA exponent values also tends to describe a more regular process and seems to be related with presence of symptoms of hepatic encephalopathy. These subtle changes could be produced by the mechanisms of a mild neurological impairment and be used, if it is confirmed, as an index to evaluate such deterioration.

Disruption of the Cortical-Vagal Communication Network in Parkinson's Disease.

Parkinson's disease (PD) is a neuropathy characterized by motor disorders, but it has also been associated with the presence of autonomic alterations as a result of degradation of the dopaminergic system. Studying the relation between Band Power time series (BPts) and Heart Rate Variability (HRV), has been proposed as a tool to explore the bidirectional communication pathways between cortex and autonomic control. This work presents a primer analysis on study brain ↔ heart interaction on a databse of PD patients under two conditions: without and after levadopa (L-dopa) intake. Additionally a healthy control population was also analyzed, and used as comparison level between both conditions. Results show PD affects pathways by reducing the number of connections, specially association of beta and power and the second faster component of HRV seems to be more sensitive to L-dopa administration.

Spectral Electroencephalographic and Heart Rate Variability features enhance identification of medicated/non-medicated Parkinson's disease patients.

Parkinson's Disease is a neuropathy that produces changes in several biomarkers, these changes could be used to evaluate even sub-clinical conditions. This paper presents an evaluation of indices extracted from electroencephalography and Heart Rate Variability (HRV), when used to classify a sample of subjects from three groups: control (healthy), medicated and non medicated subjects diagnosed with Parkinson's disease. Classification performance was measured using accuracy over these classes and a cross validation scheme was used to assess repeatability for the classification process. Results tend to prove that inclusion of an autonomic index derived from HRV analysis enhances classification, suggesting that Parkinson's disease could be related with unperceptible to mild alterations of the Autonomic Nervous System.

Bidirectional intrinsic modulation of EEG band power time series and spectral components of heart rate variability.

Some hypotheses relate oscillations of EEG band power with autonomic processes derived from homeostatic control modulated by structures like the Central Autonomic Network and the Autonomic Nervous System. This research project studies the causal relationships between fluctuations of an autonomic process marker like the Heart Rate Variability (HRV) and the proposed EEG band power time series (BPts). To verify the existence of directional causal relationships, using Granger Causality (GC) test between HRV and BPts. Analyses were performed using two databases, of 9 and 14 subjects respectively. Experiments consisted of spontaneous breathing and a controlled breathing task (CBT). GC was tested over Intrinsinc Mode Functions of HRV derived from Empirical Mode Decomposition and BPts computed over α, ß and γ bands. Positive GC tests were observed through each experimental task, channels, IMFs, EEG band, and direction. The largest number of positive GC relationships were found from BPts to HRV when testing, higher EEG band and IMF with lower spectral content. Opposite direction achieves lower total counts, but more related with IMFs of higher spectral content. Its presence also suggests that some homeostatic condition alters the BPts course given its increment under the CBT. It is important to notice that in both cases γ band achieves larger values for almost all of the studied conditions. Suggesting that such band has an important influence over HRV, but alterations on breathing condition also produce changes on BPts evolution, suggesting that the closed loop for homeostatic control alters neural dynamics at cortical level.

Bi-dimensional representation of EEGs for BCI classification using CNN architectures.

An important challenge when designing Brain Computer Interfaces (BCI) is to create a pipeline (signal conditioning, feature extraction and classification) requiring minimal parameter adjustments for each subject and each run. On the other hand, Convolutional Neural Networks (CNN) have shown outstanding to automatically extract features from images, which may help when distribution of input data is unknown and irregular. To obtain full benefits of a CNN, we propose two meaningful image representations built from multichannel EEG signals. Images are built from spectrograms and scalograms. We evaluated two kinds of classifiers: one based on a CNN-2D and the other built using a CNN-2D combined with a LSTM. Our experiments showed that this pipeline allows to use the same channels and architectures for all subjects, getting competitive accuracy using different datasets: 71.3 ± 11.9% for BCI IV-2a (four classes); 80.7 ± 11.8 % for BCI IV-2a (two classes); 73.8 ± 12.1% for BCI IV-2b; 83.6 ± 1.0% for BCI II-III and 82.10% ± 6.9% for a private database based on mental calculation.

Simultaneous Monitoring of Wireless Electrophysiology and Memory Behavioral Test as a Tool to Study Hippocampal Neurogenesis.

Brainwaves amplitude obtained from electroencephalography (EEG) has been well-recognized as a basis for cognitive capacity, memory, and learning on animals and humans. Adult neurogenesis mechanism is also linked to memory and learning improvement. Traditionally, researchers used to assess learning and memory parameters in rodent models by behavioral tasks. Therefore, the simultaneous monitoring of behavioral changes and EEG is particularly interesting in correlating data between brain activity and task-related behaviors. However, most of the equipment required to perform both studies are either complex, expensive, or uses a wired setup network that hinders the natural animals' movement. In this study, EEG was recorded with a wireless electrophysiology device along with the execution of a novel object recognition task (NORT). The animal's behavior was monitored simultaneously by a video tracking system. Both recordings were analyzed offline by their timestamps which were synched to link EEG signals with the animal's actions. Subjects consist of adult Wistar rats after medium-term environmental enrichment treatment. Six skull screw electrodes were fixed in pairs on both hemispheres over frontal, central, and parietal regions and were referenced to an electrode located posterior of the nasal bone. NORT protocol consists of exposing the animal to two identical objects for 10 min. After 2 h and 24 h, one of the objects was replaced with a novel one. Exploration time for each object was monitored by a behavioral tracking software (BTS) and EEG data recording. The analysis of the EEG synced with behavioral data consists of estimations of alpha and beta relative band power and comparisons between novel object recognition versus familiar object exploration, between three experimental stages. In this manuscript, we have discussed electrodes manufacturing process, epidural electrodes implantation surgery, environmental enrichment protocol, NORT protocol, BTS setup, EEG - BTS coupling for simultaneous monitoring in real-time, and EEG data analysis based on automatic events detection.

Latency and amplitude changes in cognitive event related potentials due to hemodialysis.

Chronic kidney disease impacts the cognitive abilities of patients, and yet few works have analyzed functional electroencephalographic changes on event-related potentials before and after an hemodialysis session. This work shows that, as a consequence of hemodialysis, cognitive potential waveforms suffer changes, occurring with reduced latency $( \sim 50$ ms) and with larger amplitude $( \sim 0.128\mathrm {a}$.u.) after the treatment session. While only a limited sample of five patients is reported herein, the observed changes immediately after hemodialysis could be a sign of central nervous system alterations that are not clinically evaluated. Signs like these could entail an early indicator of possible evolution into some neuropathology.

Evaluation of the continuous detection of mental calculation episodes as a BCI control input.

This paper presents an evaluation of the continuous detection of mental calculation episodes, which may be useful for users who strive to operate current BCI paradigms or even for augmenting degrees of freedom. The experimentation consisted in the alternated realization of basic arithmetic mental calculations and resting periods. EEG data were analyzed using sliding windows of 2s length. The experimental population was comprised of fifteen healthy subjects who participated in three sessions on different days. The features used for the classification process were the power spectral density over the beta band ([14-35] Hz) and the scaling exponent obtained via detrended fluctuation analysis. Both indices were estimated over four channels, specifically selected for each subject. The performance was evaluated using the Area Under the ROC Curve (AUC) by measuring the overall classification performance of each experimental session with a cross-validation procedure, and by transferring the model obtained from one session to the others called inter Session Validation (iSV). The best AUC values computed in each cross-validation session were: 0.87±0.067, 0.89±0.056 and 0.88±0.040 respectively; and the iSV provided a value of 0.67±0.122. These high values indicate that a mental calculation paradigm and a combination of features can efficiently control a BCI system. Notwithstanding that several days passed between sessions, the AUC mean value estimated for the iSV is similar to the performance of a motor imagery-BCI calibrated on the same day.