Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations

GluN2C selective inhibition is a target to develop new antiepileptic compounds

OBJECTIVE

Many early-onset epilepsies present as developmental and epileptic encephalopathy associated with refractory seizures, altered psychomotor development, and disorganized interictal cortical activity. Abnormal upregulation of specific N-methyl-d-aspartate receptor (NMDA-R) subunits is being disentangled as one of the mechanisms of severe early-onset epilepsies. In tuberous sclerosis complex (TSC), upregulation of the GluN2C subunit of the NMDA-R with slow deactivation kinetic results in increased neuronal excitation and synchronization.

METHODS

Starting from an available GluN2C/D antagonist, NMDA-R-modulating compounds were developed and screened using a patch clamp on neuronal culture to select those with the strongest inhibitory effect on glutamatergic NMDA currents. For these selected compounds, blood pharmacokinetics and passage through the blood-brain barrier were studied. We tested the effect of the most promising compounds on epileptic activity in Tsc1^+/- mice brain slices with multielectrode array, and then in vivo at postnatal ages P14-P17, comparable with the usual age at epilepsy onset in human TSC.

RESULTS

Using a double-electrode voltage clamp on isolated NMDA currents, we identified the most prominent antagonists of the GluN2C subunit with no effect on GluN2A as a means of preventing side effects. The best compound passing through the blood-brain barrier was selected. Applied in vivo in six Tsc1^+/- mice at P14-P17, this compound reduced or completely stopped spontaneous seizures in four of them, and decreased the background activity disorganization. Furthermore, ictal-like discharges stopped on a human brain sample from an infant with epilepsy due to TSC.

INTERPRETATION

Subunit-selective inhibition is a valuable target for developing drugs for severe epilepsies resulting from an upregulation of NMDA-R subunit-mediated transmission.

Nonlinear Statistical Analysis of Normal and Pathological Infant Cry Signals in Cepstrum Domain by Multifractal Wavelet Leaders

Multifractal behavior in the cepstrum representation of healthy and unhealthy infant cry signals is examined by means of wavelet leaders and compared using the Student t-test. The empirical results show that both expiration and inspiration signals exhibit clear evidence of multifractal properties under healthy and unhealthy conditions. In addition, expiration and inspiration signals exhibit more complexity under healthy conditions than under unhealthy conditions. Furthermore, distributions of multifractal characteristics are different across healthy and unhealthy conditions. Hence, this study improves the understanding of infant crying by providing a complete description of its intrinsic dynamics to better evaluate its health status.

Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition

The ability to control external devices through thought is increasingly becoming a reality. Human beings can use the electrical signals of their brain to interact or change the surrounding environment and more. The development of this technology called brain-computer interface (BCI) will increasingly allow people with motor disabilities to communicate or use assistive devices to walk, manipulate objects and communicate. Using data from the PhysioNet database, this study implemented a pattern classification system for use in a BCI on 109 healthy volunteers during real movement activities and motor imagery recorded by 64-channels electroencephalography (EEG) system. Different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Trees (TREE) were applied on different combinations of EEG channels. Starting from two channels (C3, C4 and CP3 and CP4) positioned on the contralateral and ipsilateral sensorimotor cortex, the Region of Interest (RoI) centred on C3/Cp3 and C4/Cp4 and, finally, a data-driven automatic channels selection was tested to explore the best channel combination able to increase the classification accuracy. The results showed that the proposed automatic channels selection was able to significantly improve the performance of each classifier achieving 98% of accuracy for classification of real and imagined hand movement (sensitivity = 97%, specificity = 99%, AUC = 0.99) by SVM. While the accuracy of the classification between the imagery of hand and foot movements was 91% (sensitivity = 87%, specificity = 86%, AUC = 0.93) also with SVM. In the proposed approach, the data-driven automatic channels selection outperforms classical a priori channel selection models such as C3/C4, Cp3/Cp4, or RoIs around those channels with the utmost accuracy to help remove the boundaries of human communication and improve the quality of life of people with disabilities.

Detection of crossover points in detrended fluctuation analysis: an application to EEG signals of patients with epilepsy

MOTIVATION

The quantification of long-range correlation of electroencephalogram (EEG) signals is an important research direction for its relevance in helping understanding the brain activity. Epileptic seizures have been studied in the past years where different non-linear statistical approaches have been employed to understand the relationship between the EEG signal and the epileptic discharge. One of the most widely used method for to analyse long memory processes is the detrended fluctuation analysis (DFA). However, no objective and pragmatic methods have been developed to detect crossover points and reference channels in DFA.

RESULTS

In this article, we propose: (i) two automatic approaches that successfully detect crossover points in DFA related methods on the log-log plot and (ii) a criteria to choose the reference channel for the log-amplitude function. Moreover, the DFA is applied to EEG signals of 10 epileptic patients collected from the CHB-MIT database, being the log-amplitude function used to compare the different brain hemispheres by making use of the methodology proposed in the article. The existence of long-range power-law correlations is demonstrated and indicates that the EEG signals of epileptic patients present three well-defined regions with the first region showing a 1/f noise (pink noise) for seven subjects and a random walk behaviour for three subjects. The second and third regions show anti-persistence behaviour. Moreover, the results of the log-amplitude function were divided in two groups: the first, including seven subjects, where the increase in the scales results in an increase in the fluctuation in the frontal channels and the second, included three subjects, where the fluctuation for large scales are greater for the parietal channels.

AVAILABILITY AND IMPLEMENTATION

The functions used in this article are available in the R package DFA (Mesquita et al., 2020).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Exploring brain activity and transforming knowledge in visual and textual programming using neuroeducation approaches

Eight (8) computer science students, novice programmers, who were in the first semester of their studies, participated in a field study in order to explore potential differences in their brain activity during programming with a visual programming language versus a textual programming language. The eight students were asked to develop two specific programs in both programming languages (a total of four tasks). The order of these programs was determined, while the order of languages in which they worked differed between the students. Measurement of cerebral activity was performed by the electroencephalography (EEG) imaging method. According to the analysis of the data it appears that the type of programming language did not affect the students' brain activity. Also, six students needed more time to successfully develop the programs they were asked with the first programming language versus the second one, regardless of the type of programming language that was first. In addition, it appears that six students did not show reducing or increasing brain activity as they spent their time on tasks and at the same time did not show a reduction or increase in the time they needed to develop the programs. Finally, the students showed higher average brain activity in the development of the fourth task than the third, and six of them showed higher average brain activity when developing the first versus the second program, regardless of the programming language. The results can contribute to: a) highlighting the need for a diverse educational approach for students when engaging in program development and b) identifying appropriate learning paths to enhance student education in programming.

Causal Shannon-Fisher Characterization of Motor/Imagery Movements in EEG

The electroencephalogram (EEG) is an electrophysiological monitoring method that allows us to glimpse the electrical activity of the brain. Neural oscillations patterns are perhaps the best salient feature of EEG as they are rhythmic activities of the brain that can be generated by interactions across neurons. Large-scale oscillations can be measured by EEG as the different oscillation patterns reflected within the different frequency bands, and can provide us with new insights into brain functions. In order to understand how information about the rhythmic activity of the brain during visuomotor/imagined cognitive tasks is encoded in the brain we precisely quantify the different features of the oscillatory patterns considering the Shannon–Fisher plane H×F. This allows us to distinguish the dynamics of rhythmic activities of the brain showing that the Beta band facilitate information transmission during visuomotor/imagined tasks.

Rhythmic activities of the brain: Quantifying the high complexity of beta and gamma oscillations during visuomotor tasks

Electroencephalography (EEG) signals depict the electrical activity that takes place at the surface of the brain and provide an important tool for understanding a variety of cognitive processes. The EEG is the product of synchronized activity of the brain, and variations in EEG oscillations patterns reflect the underlying changes in neuronal synchrony. Our aim is to characterize the complexity of the EEG rhythmic oscillations bands when the subjects perform a visuomotor or imagined cognitive tasks (imagined movement), providing a causal mapping of the dynamical rhythmic activities of the brain as a measure of attentional investment. We estimate the intrinsic correlational structure of the signals within the causality entropy-complexity plane H×C, where the enhanced complexity in the gamma 1, gamma 2, and beta 1 bands allows us to distinguish motor-visual memory tasks from control conditions. We identify the dynamics of the gamma 1, gamma 2, and beta 1 rhythmic oscillations within the zone of a chaotic dissipative behavior, whereas in contrast the beta 2 band shows a much higher level of entropy and a significant low level of complexity that correspond to a non-invertible cubic map. Our findings enhance the importance of the gamma band during attention in perceptual feature binding during the visuomotor/imagery tasks.