Direct lingam and visibility graphs for analyzing brain connectivity in BCI.

The brain-computer interface (BCI) is a direct pathway of communication between the electrical activity of the brain and an external device. The present paper was aimed to investigate directed connectivity between different areas of the brain during motor imagery (MI)-based BCI. For this purpose, two methods were implemented including, Limited Penetrable Horizontal Visibility Graph (LPHVG) and Direct Lingam. The visibility graph (VG) is a robust algorithm for analyzing complex systems such as the brain. Direct Lingam uses a non-Gaussian model to extract causal links which is appropriate for analyzing large-scale connectivity. First, LPHVG map MI-EEG (electroencephalogram) signals into networks. After extracting the topological features of the networks, a support vector machine classifier was applied to categorize multi-classes MI. The network of all classes was found to be different from one another, and the kappa value of classification was 0.68. The degree sequence of LPHVG was calculated for each channel in order to obtain the direction of brain information flow. Transfer entropy (TE) is used to compute the relations of the channel degree sequence. Therefore, the directed graph between channels was formed. This method is called LPHVG_TE directed graph. The Bayesian network, also known as the Direct LiNGAM model, was implemented for the second method. Finally, images of the LPHVG and Direct Lingam were classified by convolutional neural network (CNN). In this study, Data sets 2a of BCI competition IV was used. The outcomes reveal that the brain network developed by LPHVG (92.7%) might be more effective to distinguish 4 classes of MI than the Direct Lingam (90.6%) and it was shown that graph theory has the potential to get better efficiency of BCI.

Quantitative Comparison of Brain Waves of Dyslexic Students With Perceptual and Linguistic Types With Normal Students in Reading.

Introduction: The aim of this study was to compare the brain wave pattern of two groups of dyslexic students with perceptual and linguistic types with normal students in reading. Methods: In this study, 27 students (24 boys and 3 girls) from first to fifth grade with an Mean±SD of age 8.16±10.09 years participated. Eight students with perceptual type dyslexia, ten students with linguistic type dyslexia, and nine normal students with reading were selected by purposive sampling method. Results: After removing noise and artifacts, the data were converted into quantitative digits using Neuroguide software and analyzed using multivariate analysis of variance (MANOVA) and univariate analysis of variance (ANOVA). Based on the results, the linguistic group and the normal group differed in the relative power of the alpha wave in the two channels Fp1 and Fp2, but there was no difference between the three linguistic, perceptual, and normal groups in the absolute power of the four waves of the delta, theta, alpha, and beta. Conclusion: The relative power spectrum of the alpha band in the forehead can be significantly related to dyslexia problems as seen in the linguistic type.

A Novel Method Based on ERP and Brain Graph for the Simultaneous Assessment of Various Types of Attention.

Assessment of attention is of great importance as one of human cognitive abilities. Although neuropsychological tests have been developed and used to evaluate the ability to pay attention, their validity and reliability have been reduced due to some limitations such as the presence of intervention factors, including human factors, limited range of languages, and cultural influences. Therefore, direct outputs of the brain system, represented by event-related potentials (ERPs), and the analysis of its function in cognitive activities have become very important as a complementary tool to assess various types of attention. This research tries to assess 4 types of attention including sustained, alternative, selective, and divided, using an integrated visual-auditory test and brain signals simultaneously. Thus, the electroencephalogram (EEG) data were recorded using 19 channels, and the integrated visual and auditory (IVA-AE) test was simultaneously performed on twenty-eight healthy volunteers including 22 male and 6 female subjects with the average age of 27 ± 5.3 years. Then ERPs related to auditory and visual stimuli with synchronous averaging technique were extracted. A topographic brain mapping (topo-map) was plotted for each frame of stimulation. Next, an optical flow method was implemented on different topo-maps to obtain motion vectors from one map to another. After obtaining the overall brain graph of an individual, some features were extracted and used as measures of local and global connectivity. The extracted features were consequently evaluated along with the parameters of the IVA test by support vector machine regression (SVM-R). The volume of attention was then quantified by combining the IVA parameters. Ultimately, estimation accuracy of each type of attention including focused attention (86.1%), sustained attention (83.4%), selective attention (80.9%), and divided attention (79.9%) was obtained. According to the present study, there is a significant relationship between response control and attention indicators of the IVA test as well as ERP brain signals.

Contextual Interference Effects on Approach Motivation When Learning Timing Tasks: A Frontal Electroencephalography (EEG) Alpha Asymmetry Study in Older Adults.

In this study, we aimed to explore the effects of contextual interference on motivational regulation, as measured by EEG (frontal alpha asymmetry), in older adults. Participants practiced a sequenced timing task in random, algorithmic, or blocked schedules in both similar and dissimilar task difficulty conditions, with task difficulty defined by absolute timing goals (in ms) that were either close to each other (1350, 1500, 1650) or far from each other (1050, 1500, 1950), respectively. We assessed older participants' timing accuracy in these conditions, during acquisition and delayed retention learning, using the frontal alpha asymmetry index, recorded during practice, to measure motivation. On the accuracy measure in delayed retention, the algorithm practice schedule (in both similar and dissimilar conditions) was associated with significantly more accurate performance than random and blocked practice schedules. Also during delayed retention and in both task difficulty conditions, performance was better with a random schedule than a blocked schedule. On the EEG motivational measure, frontal alpha asymmetry was more often higher as practice progressed in the algorithm practice condition than in other practice conditions. However, in the random practice schedule, in late, versus early, acquisition, motivational regulation was higher. The blocked groups showed decreased motivation as practice progressed. We interpreted these findings to be in accordance with the challenge point framework and with OPTIMAL motor learning theory and valence hypothesis.

Evaluating the Effect of Quran Memorizing on the Event-related Potential Features by Using Graphs Created from the Neural Gas Networks.

Background: Quran memorizing causes a state of trance, which its result is the changes in the amplitude and time of P300 and N200 components in the event related potential (ERP) signal. Nevertheless, a limited number of studies that have examined the effects of Quran memorizing on brain signals to enhance relaxation and attention, and improve the lives of patients with autism and stroke, generally have not presented any analysis based on comparing structural differences relevant to features extracted from ERP signal obtained from the two groups of Quran memorizer and nonmemorizer by using the hybrid of graph theory and competitive networks. Methods: In this study, we investigated structural differences relevant to the graph obtained from the weight of neural gas (NG) and growing NG (GNG) networks trained by features extracted from the ERP signal recorded from two groups during the PRM test. In this analysis, we actually estimated the ERP signal by averaging the brain background data in the recovery phase. Then, we extracted six features related to the power and the complexity of these signals and selected optimal channels in each of the features by using the t test analysis. Then, these features extracted from the optimal channels are applied for developing the NG and GNG networks. Finally, we evaluated different parameters calculated from graphs, in which their connection matrix was obtained from the weight matrix of the networks. Results: The outcomes of this analysis show that increasing the power of low frequency components and the power ratio of low frequency components to high frequency components in the memorizers, which represents patience, concentration, and relaxation, is more than that of the nonmemorizers. These outcomes also show that the optimal channels in different features, which were often in frontal, peritoneal, and occipital regions, had a significant difference (P < 0.05). It is remarkable that two parameters of the graphs established based on two competitive networks, i.e. average path length and the average of the weights in the memorizers, were larger than the nonmemorizers, which means more data scattering in this group. Conclusion: This condition in the mentioned graphs suggests that the Quran memorizing causes a significant change in ERP signals, so that its features have usually more scattering.

Efficacy of repetitive transcranial magnetic stimulation in patients with obsessive-compulsive disorder: a pilot study.

BACKGROUND: Obsessive-compulsive disorder (OCD) is a common disabling psychiatric disorder. Considering the lack of an acceptable treatment response in many patients, several efforts have been made to increase the efficacy of therapy. We aimed to evaluate the efficacy of repetitive transcranial magnetic stimulation (rTMS) on the supplementary motor area in the treatment of patients with drug-resistant OCD and examine changes in brain function. METHODS: This quasi-experimental study was performed on 12 patients who were referred to outpatient clinics of Ibn-e-Sina psychiatric hospital and were diagnosed with OCD according to the clinical and diagnostic criteria of Diagnostic and Statistical Manual of Mental Disorders (DSM-5). All patients received 20 rTMS sessions in their right supplementary motor region. Main outcomes were assessed using quantitative electroencephalography (qEEG) and the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) before and after the intervention. In addition, Y­BOCS was completed after 10 rTMS sessions and after the 6­week follow-up. Data were analyzed with SPSS. RESULTS: Ten of 12 patients completed this study, of whom 7 (70%) were female. The mean age was 36.66 ± 10.28 years. Y­BOCS overall score significantly decreased over time during the course of study compared to baseline (P < 0.05). A significant decrease in beta wave activity of the parietal and occipital regions was seen in posttreatment qEEG, compared with baseline (P < 0.05). CONCLUSIONS: rTMS over the supplementary motor area at 20 sessions could effectively improve Y­BOCS score and decrease beta wave activity in parietal and occipital regions. Further studies are needed to approve these findings in a controlled design.

Algorithm-Based Practice Schedule and Task Similarity Enhance Motor Learning in Older Adults.

According to the challenge point framework, task difficulty has to be appropriate to learner skill level. The pure blocked or random practice controls the task difficulty during practice monotonically. Therefore, the purpose of this study was to investigate the effect of algorithm-based practice schedule and task similarity on motor learning in older adults. For this purpose, 60 older adults were randomly assigned into six groups of blocked-similar, algorithm-similar, random-similar, blocked-dissimilar, algorithm-dissimilar, and random-dissimilar. Sequential motor tasks were used for learning. Participants practiced absolute timing goals in similar (1350, 1500, 1650 ms) or dissimilar (1050, 1500, 1950 ms) conditions according to their practice schedule. Twenty-four hours after the acquisition phase, retention, and transfer tests were performed. Algorithm-practice was a hybrid practice schedule (blocked, serial, and random practice in forward/backward switching) that switching the schedules was according to error trial number (n ≤ 33%) in each block based on error range of absolute timing goals (± 5%). The results showed that the blocked-practice outperforms the other groups during the acquisition phase, whereas the algorithm-practice outperforms the other groups in retention and transfer in both similar and dissimilar conditions. These findings were discussed according to the challenge point framework.

Neural Mechanisms of the Contextual Interference Effect and Parameter Similarity on Motor Learning in Older Adults: An EEG Study.

The purpose of this study was to investigate the neural mechanisms of the contextual interference effect (CIE) and parameter similarity on motor learning in older adults. Sixty older adults (mean age, 67.68 ± 3.95 years) were randomly assigned to one of six experimental groups: blocked-similar, algorithm-similar, random-similar, blocked-dissimilar, algorithm-dissimilar, and random-dissimilar. Algorithm practice was a hybrid practice schedule (a combination of blocked, serial, and random practice) that switching between practice schedules were based on error trial number, ≤33%. The sequential motor task was used to record the absolute timing for the absolute timing goals (ATGs). In similar conditions, the participants' performance was near ATGs (1,350, 1,500, 1,650 ms) and in dissimilar conditions, they performed far ATGs (1,050, 1,500, 1,950 ms) with the same spatial sequence for all groups. EEG signals were continuously collected during the acquisition phase and delayed retention. Data were analyzed in different bands (alpha and beta) and scalp locations (frontal: Fp1, Fp2, F3, F4; central: C3, C4; and parietal: P3, P4) with repeated measures on the last factor. The analyses were included motor preparation and intertrial interval (motor evaluation) periods in the first six blocks and the last six blocks, respectively. The results of behavioral data indicated that algorithm practice resulted in medium error related to classic blocked and random practice during the acquisition, however, algorithm practice outperformed the classic blocked and random practice in the delayed retention test. The results of EEG data demonstrated that algorithm practice, due to optimal activity in the frontal lobe (medium alpha and beta activation at prefrontal), resulted in increased activity of sensorimotor areas (high alpha activation at C3 and P4) in older adults. Also, EEG data showed that similar conditions could affect the intertrial interval period (medium alpha and beta activation in frontal in the last six-block), while the dissimilar conditions could affect the motor preparation period (medium alpha and beta activation in frontal in the first six-block). In conclusion, algorithm practice can enhance motor learning and optimize the efficiency of brain activity, resulting in the achievement of a desirable goal in older adults.

Quantifying clinical improvements in patients with depression under the treatment of transcranial direct current stimulation using event related potentials.

The main goal of this study was to assess the changes in brain activities of patients with severe depression by applying transcranial direct current stimulation (tDCS) using event related potentials (ERPs). Seven patients (four males, with the mean age 34.85 ± 4.25) were asked to fill out Beck's depression questionnaires. EEG signals of subjects were recorded during Stroop test. This test entailed 360 stimulations, which included 120 congruent, 120 incongruent, and 120 neutral stimulations lasting for 12 min. Subsequently, the dorso lateral prefrontal cortex in patients' left hemisphere was stimulated for six sessions using tDCS. At the end of tDCS treatment period, subjects filled out Beck's depression questionnaires again and EEG signal recordings were repeated simultaneously with Stroop test. Wavelet coefficients of EEG frequency bands in every stimulation type were extracted from ERP components. The changes in Beck score before and after tDCS were estimated using neural network model. The ERP results showed that the latency period of N400 component after applying tDCS decreased significantly. Moreover, a significant correlation was observed between percentage changes of congruent and incongruent accuracy and the increase in the average energy of wavelet coefficients in alpha band in Pz electrode with p = 0.0128, r = 0.9060 and p = 0.0037, r = 0.95, respectively. Additionally, the results of neural network model revealed that the changes in Beck score were estimated with an average error of 0.0519. Consequently, the improvement of depressed patients treated with tDCS could be estimated with good accuracy using average energy of wavelet coefficients in alpha band.

Electroencephalography Pattern Variations During Motor Skill Acquisition.

The present study examined how motor skill acquisition affects electroencephalography patterns and compared short- and long-term electroencephalography variations. For this purpose, 17 volunteers with no history of disease, aged 18 to 22 years, attended seven training sessions every other day to practice a pursuit tracking motor skill. Electroencephalography brainwaves were recorded and analyzed on the first and last days within pre- and post-training intervals. The results showed a significant decrease in performance error and variability with practice over time. This progress slowed at the end of training, and there was no significant improvement in individual performance at the last session. In accordance with performance variations, some changes occurred in brainwaves. Specifically, θ power at Fz and α power at Cz increased on the last test day, compared with the first, while the coherence of α at Fz-T3 and Fz-Cz decreased. ß Coherence between Fz-Cz was significantly reduced from pre- to posttest. Based on these results, power changes seem to be more affected by long-term training, whereas coherence changes are sensitive to both short- and long-term training. Specifically, ß coherence at Fz-Cz was more influenced by short-term effects of training, whereas θ power at Fz, α power at Cz, and α coherence at Fz-T3 and Fz-Cz were affected by longer training.