Envelope reconstruction of speech and music highlights stronger tracking of speech at low frequencies.

The human brain tracks amplitude fluctuations of both speech and music, which reflects acoustic processing in addition to the encoding of higher-order features and one's cognitive state. Comparing neural tracking of speech and music envelopes can elucidate stimulus-general mechanisms, but direct comparisons are confounded by differences in their envelope spectra. Here, we use a novel method of frequency-constrained reconstruction of stimulus envelopes using EEG recorded during passive listening. We expected to see music reconstruction match speech in a narrow range of frequencies, but instead we found that speech was reconstructed better than music for all frequencies we examined. Additionally, models trained on all stimulus types performed as well or better than the stimulus-specific models at higher modulation frequencies, suggesting a common neural mechanism for tracking speech and music. However, speech envelope tracking at low frequencies, below 1 Hz, was associated with increased weighting over parietal channels, which was not present for the other stimuli. Our results highlight the importance of low-frequency speech tracking and suggest an origin from speech-specific processing in the brain.

Mathematical Modeling of Brain Activity under Specific Auditory Stimulation.

Understanding the connection between different stimuli and the brain response represents a complex research area. However, the use of mathematical models for this purpose is relatively unexplored. The present study investigates the effects of three different auditory stimuli on cerebral biopotentials by means of mathematical functions. The effects of acoustic stimuli (S1, S2, and S3) on cerebral activity were evaluated by electroencephalographic (EEG) recording on 21 subjects for 20 minutes of stimulation, with a 5-minute period of silence before and after stimulation. For the construction of the mathematical models used for the study of the EEG rhythms, we used the Box-Jenkins methodology. Characteristic mathematical models were obtained for the main frequency bands and were expressed by 2 constant functions, 8 first-degree functions, a second-degree function, a fourth-degree function, 6 recursive functions, and 4 periodic functions. The values obtained for the variance estimator are low, demonstrating that the obtained models are correct. The resulting mathematical models allow us to objectively compare the EEG response to the three stimuli, both between the stimuli itself and between each stimulus and the period before stimulation.

Generative Adversarial Networks-Based Data Augmentation for Brain-Computer Interface.

The performance of a classifier in a brain-computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled environment. However, users' attention may be diverted in real-life BCI applications and this may decrease the performance of the classifier. To improve the robustness of the classifier, additional data can be acquired in such conditions, but it is not practical to record electroencephalogram (EEG) data over several long calibration sessions. A potentially time- and cost-efficient solution is artificial data generation. Hence, in this study, we proposed a framework based on the deep convolutional generative adversarial networks (DCGANs) for generating artificial EEG to augment the training set in order to improve the performance of a BCI classifier. To make a comparative investigation, we designed a motor task experiment with diverted and focused attention conditions. We used an end-to-end deep convolutional neural network for classification between movement intention and rest using the data from 14 subjects. The results from the leave-one subject-out (LOO) classification yielded baseline accuracies of 73.04% for diverted attention and 80.09% for focused attention without data augmentation. Using the proposed DCGANs-based framework for augmentation, the results yielded a significant improvement of 7.32% for diverted attention ( ) and 5.45% for focused attention ( ). In addition, we implemented the method on the data set IVa from BCI competition III to distinguish different motor imagery tasks. The proposed method increased the accuracy by 3.57% ( ). This study shows that using GANs for EEG augmentation can significantly improve BCI performance, especially in real-life applications, whereby users' attention may be diverted.

Task Transfer Learning for EEG Classification in Motor Imagery-Based BCI System.

The motor-imagery brain-computer interface system (MI-BCI) has a board prospect for development. However, long calibration time and lack of enough MI commands limit its use in practice. In order to enlarge the command set, we add the combinations of traditional MI commands as new commands into the command set. We also design an algorithm based on transfer learning so as to decrease the calibration time for collecting EEG signal and training model. We create feature extractor based on data from traditional commands and transfer patterns through the data from new commands. Through the comparison of the average accuracy between our algorithm and traditional algorithms and the visualization of spatial patterns in our algorithm, we find that the accuracy of our algorithm is much higher than traditional algorithms, especially as for the low-quality datasets. Besides, the visualization of spatial patterns is meaningful. The algorithm based on transfer learning takes the advantage of the information from source data. We enlarge the command set while shortening the calibration time, which is of significant importance to the MI-BCI application.

Auditory information enhances post-sensory visual evidence during rapid multisensory decision-making.

Despite recent progress in understanding multisensory decision-making, a conclusive mechanistic account of how the brain translates the relevant evidence into a decision is lacking. Specifically, it remains unclear whether perceptual improvements during rapid multisensory decisions are best explained by sensory (i.e., 'Early') processing benefits or post-sensory (i.e., 'Late') changes in decision dynamics. Here, we employ a well-established visual object categorisation task in which early sensory and post-sensory decision evidence can be dissociated using multivariate pattern analysis of the electroencephalogram (EEG). We capitalize on these distinct neural components to identify when and how complementary auditory information influences the encoding of decision-relevant visual evidence in a multisensory context. We show that it is primarily the post-sensory, rather than the early sensory, EEG component amplitudes that are being amplified during rapid audiovisual decision-making. Using a neurally informed drift diffusion model we demonstrate that a multisensory behavioral improvement in accuracy arises from an enhanced quality of the relevant decision evidence, as captured by the post-sensory EEG component, consistent with the emergence of multisensory evidence in higher-order brain areas.

Evaluation of Low-Value Diagnostic Testing for 4 Common Conditions in the Veterans Health Administration.

Importance: Low-value care is associated with harm among patients and with wasteful health care spending but has not been well characterized in the Veterans Health Administration. Objectives: To characterize the frequency of and variation in low-value diagnostic testing for 4 common conditions at Veterans Affairs medical centers (VAMCs) and to examine the correlation between receipt of low-value testing for each condition. Design, Setting, and Participants: This retrospective cohort study used Veterans Health Administration data from 127 VAMCs from fiscal years 2014 to 2015. Data were analyzed from April 2018 to March 2020. Exposures: Continuous enrollment in Veterans Health Administration during fiscal year 2015. Main Outcomes and Measures: Receipt of low-value testing for low back pain, headache, syncope, and sinusitis. For each condition, sensitive and specific criteria were used to evaluate the overall frequency and range of low-value testing, adjusting for sociodemographic and VAMC characteristics. VAMC-level variation was calculated using median adjusted odds ratios. The Pearson correlation coefficient was used to evaluate the degree of correlation between low-value testing for each condition at the VAMC level. Results: Among 1 022 987 veterans, the mean (SD) age was 60 (16) years, 1 008 336 (92.4%) were male, and 761 485 (69.8%) were non-Hispanic White. A total of 343 024 veterans (31.4%) were diagnosed with low back pain, 79 176 (7.3%) with headache, 23 776 (2.2%) with syncope, and 52 889 (4.8%) with sinusitis. With the sensitive criteria, overall and VAMC-level low-value testing frequency varied substantially across conditions: 4.6% (range, 2.7%-10.1%) for sinusitis, 12.8% (range, 8.6%-22.6%) for headache, 18.2% (range, 10.9%-24.6%) for low back pain, and 20.1% (range, 16.3%-27.7%) for syncope. With the specific criteria, the overall frequency of low-value testing across VAMCs was 2.4% (range, 1.3%-5.1%) for sinusitis, 8.6% (range, 6.2%-14.6%) for headache, 5.6% (range, 3.6%-7.7%) for low back pain, and 13.3% (range, 11.3%-16.8%) for syncope. The median adjusted odds ratio ranged from 1.21 for low back pain to 1.40 for sinusitis. At the VAMC level, low-value testing was most strongly correlated for syncope and headache (ρ = 0.56; P < .001) and low back pain and headache (ρ = 0.48; P < .001). Conclusions and Relevance: In this cohort study, low-value diagnostic testing was common, varied substantially across VAMCs, and was correlated between veterans' receipt of different low-value tests at the VAMC level. The findings suggest a need to address low-value diagnostic testing, even in integrated health systems, with robust utilization management practices.

Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine.

Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-based brain-computer interface (BCI). However, labeled EEG samples are generally scarce and expensive to collect, while unlabeled samples are considered to be abundant in real applications. Although the semi-supervised learning (SSL) allows us to utilize both labeled and unlabeled data to improve the classification performance as against supervised algorithms, it has been reported that unlabeled data occasionally undermine the performance of SSL in some cases. To overcome this challenge, we propose a collaborative representation-based semi-supervised extreme learning machine (CR-SSELM) algorithm to evaluate the risk of unlabeled samples by a new safety-control mechanism. Specifically, the ELM model is firstly used to predict unlabeled samples and then the collaborative representation (CR) approach is employed to reconstruct the unlabeled samples according to the obtained prediction results, from which the risk degree of unlabeled sample is defined. A risk-based regularization term is then constructed accordingly and embedded into the objective function of the SS-ELM. Experiments conducted on benchmark and EEG datasets demonstrate that the proposed method outperforms the ELM and SS-ELM algorithm. Moreover, the proposed CR-SSELM even offers the best performance while SS-ELM yields worse performance compared with its supervised counterpart (ELM). Graphical abstract This paper proposes a collaborative representation-based semi-supervised extreme learning machine (CR-SSELM) algorithm to evaluate the risk of unlabeled samples by a new safety-control mechanism. It is aim to solve the safety problem of SS-ELM method that SS-ELM yields worse performance than ELM. With the help of safety mechanism, the performance of our method is still better than supervised ELM method.

Faster automatic ASSR detection using sequential tests.

Objective: Objective Response Detection (ORD) can be used for auditory steady-state response (ASSR) detection. In conventional ORD methods, the statistical tests are applied at the end of data collection ('single-shot tests'). In sequential ORD methods, statistical tests are applied repeatedly, while data is being collected. However, repeated testing can increase False Positive (FP) rates. One solution is to infer that response is present only after the test remains significant for a predefined number of consecutive detections (NCD). Thus, this paper describes a new method for finding the required NCD that control the FP rate for ASSR detection.Design: NCD values are estimated using Monte Carlo simulations.Study sample: ASSR signals were recorded from 8 normal-hearing subjects.Results: The exam time was reduced by up to 38.9% compared to the single-shot test with loss of approximately 5% in detection rate. Alternatively, lower gains in time were achieved for a smaller (non-significant) loss in detection rate. The FP rates at the end of the test were kept at the nominal level expected (1%).Conclusion: The sequential test strategy with NCD as the stopping criterion can improve the speed of ASSR detection and prevent higher than expected FP rates.

Monitoring the level of hypnosis using a hierarchical SVM system.

Monitoring level of hypnosis is a major ongoing challenge for anesthetists to reduce anesthetic drug consumption, avoiding intraoperative awareness and prolonged recovery. This paper proposes a novel automated method for accurate assessing of the level of hypnosis with sevoflurane in 17 patients using the electroencephalogram signal. In this method, a set of distinctive features and a hierarchical classification structure based on support vector machine (SVM) methods, is proposed to discriminate the four levels of anesthesia (awake, light, general and deep states). The first stage of the hierarchical SVM structure identifies the awake state by extracting Shannon Permutation Entropy, Detrended Fluctuation Analysis and frequency features. Then deep state is identified by extracting the sample entropy feature; and finally light and general states are identified by extracting the three mentioned features of the first step. The accuracy of the proposed method of analyzing the brain activity during anesthesia is 94.11%; which was better than previous studies and also a commercial monitoring system (Response Entropy Index).

EEG signal based classification before and after combined Yoga and Sudarshan Kriya.

Nowadays, the style of living is restless and busy which has resulted in increased stress among many people. Stress causes various mental and health illness such as depression, anxiety, mood disorders, and aggressive behavior. Yoga and Sudarshan Kriya (SK) meditation are healthy ways to eradicate stress from people's lives. Based on the previous study, it has been analyzed that SK practice helps to enhance relaxation, management of emotion, alertness, focus, and antidepressant effect. In this paper, the combined impact of yoga and SK meditation has been analyzed on brain signals by using statistical parameters. To the best of the authors' knowledge, no such study has been conducted in the past. In this study, the pre and post Electroencephalogram (EEG) signals were captured from the control and study group before and after three months regular practice of combined yoga and SK. Discrete Wavelet Transform (DWT) has been used to decompose the signal into 6 sub-bands (0-4, 4-8, 8-16, 16-32, 32-64, 64-128) hertz (Hz) by using db4 wavelet for analysis, statistical features such as variance, standard deviation, kurtosis, zero crossing, maximum and minimum have been calculated from each sub-band. The obtained parameters have been validated by using Kruskal-Wallis statistical test. Further, Artificial Neural Network (ANN) has been applied on aforementioned statistical parameters to classify subjects as meditators and non-meditators. The experimental results indicated that the proposed method achieved 87.2% accuracy for classification and could be further extended to construct an accurate classification system for detection of meditators and non-meditators. This study forms a scientific foundation to encourage the use of meditation in clinical practices.

When the statistical MMN meets the physical MMN.

How do listeners respond to prediction errors within patterned sequence of sounds? To answer this question we carried out a statistical learning study using electroencephalography (EEG). In a continuous auditory stream of sound triplets the deviations were either (a) statistical, in terms of transitional probability, (b) physical, due to a change in sound location (left or right speaker) or (c) a double deviants, i.e. a combination of the two. Statistical and physical deviants elicited a statistical mismatch negativity and a physical MMN respectively. Most importantly, we found that effects of statistical and physical deviants interacted (the statistical MMN was smaller when co-occurring with a physical deviant). Results show, for the first time, that processing of prediction errors due to statistical learning is affected by prediction errors due to physical deviance. Our findings thus show that the statistical MMN interacts with the physical MMN, implying that prediction error processing due to physical sound attributes suppresses processing of learned statistical properties of sounds.

Frequency following responses to tone glides: Effects of frequency extent, direction, and electrode montage.

The spectral (frequency) and amplitude cues in speech change rapidly over time. Study of the neural encoding of these dynamic features may help to improve diagnosis and treatment of speech-perception difficulties. This study uses tone glides as a simple approximation of dynamic speech sounds to better our understanding of the underlying neural representation of speech. The frequency following response (FFR) was recorded from 10 young normal-hearing adults using six signals varying in glide direction (rising and falling) and extent of frequency change (13, 23, and 1 octave). In addition, the FFR was simultaneously recorded using two different electrode montages (vertical and horizontal). These factors were analyzed across three time windows using a measure of response strength (signal-to-noise ratio) and a measure of temporal coherence (stimulus-to-response correlation coefficient). Results demonstrated effects of extent, montage, and a montage-by-window interaction. SNR and stimulus-to-response correlation measures differed in their sensitivity to these factors. These results suggest that the FFR reflects dynamic acoustic characteristics of simple tonal stimuli very well. Additional research is needed to determine how neural encoding may differ for more natural dynamic speech signals and populations with impaired auditory processing.

Review of the therapeutic neurofeedback method using electroencephalography: EEG Neurofeedback.

Electroencephalographic neurofeedback (EEG-NFB) represents a broadly used method that involves a real-time EEG signal measurement, immediate data processing with the extraction of the parameter(s) of interest, and feedback to the individual in a real-time. Using such a feedback loop, the individual may gain better control over the neurophysiological parameters, by inducing changes in brain functioning and, consequently, behavior. It is used as a complementary treatment for a variety of neuropsychological disorders and improvement of cognitive capabilities, creativity or relaxation in healthy subjects. In this review, various types of EEG-NFB training are described, including training of slow cortical potentials (SCPs) and frequency and coherence training, with their main results and potential limitations. Furthermore, some general concerns about EEG-NFB methodology are presented, which still need to be addressed by the NFB community. Due to the heterogeneity of research designs in EEG-NFB protocols, clear conclusions on the effectiveness of this method are difficult to draw. Despite that, there seems to be a well-defined path for the EEG-NFB research in the future, opening up possibilities for improvement.

Stimuli and Feature Extraction Algorithms for Brain-Computer Interfaces: A Systematic Comparison.

A brain-computer interface (BCI) is a system that allows communication between the central nervous system and an external device. The BCIs developed by various research groups differ in their main features and the comparison across studies is therefore challenging. Here, in the same group of 19 healthy participants, we investigate three different tasks (SSVEP, P300, and hybrid) that allowed four choices to the user without previous neurofeedback training. We used the same 64-channel EEG equipment to acquire data, while participants performed each of the tasks. We systematically compared the participants' offline performance on the following parameters: 1) accuracy; 2) BCI Utility (in bits/min); and 3) inefficiency/illiteracy. In addition, we evaluated the accuracy as a function of the number of electrodes. In this paper, the SSVEP task outperformed the other tasks in bit rate, reaching an average and maximum BCI Utility of 63.4 and 91.3 bits/min, respectively. All participants achieved an accuracy level above70% on both SSVEP and P300 tasks. Furthermore, the average accuracy of all tasks was highest if a reduced subset with 4-12 electrodes was used. These results are relevant for the development of online BCIs intended for the real-life applications.

Research on electroencephalogram to measure thermal pleasure in thermal alliesthesia in temperature step-change environment.

Thermal pleasure is currently measured along psychological and physiological variables. However, in transient environments where temperatures change, it is hard to correlate psychological and physiological measures, because there is a delay in physiological changes. This study tests a method for correlating both measures using electroencephalogram (EEG), which can capture physiological feedback with a rapid response rate. In this experimental study, thermal pleasure was induced in a temperature step-change environment, one of non-uniform and transient environments. During the experiment, EEG was monitored and psychological responses of thermal sensation and thermal comfort votes were collected via survey questionnaire. A total of 50 males in their twenties participated in a climate chamber experiment. An experimental group of 25 men were exposed to temperature step-change between two different room conditions (32°C, 65% and 25°C, 50%), experiencing thermal pleasure. The control group of the remaining 25 men were exposed to an unchanging condition, experiencing thermal comfort close to thermal neutrality. The EEG spectral analysis demonstrated that EEG frequency band associated with pleasant emotional (theta) increased while frequency band related to pleasantness, satisfaction or relaxation (beta) decreased with thermal pleasure.

A Novel Technique for Selecting EMG-Contaminated EEG Channels in Self-Paced Brain-Computer Interface Task Onset.

Electromyography artifacts are a well-known problem in electroencephalography studies [brain-computer interfaces (BCIs), brain mapping, and clinical areas]. Blind source separation (BSS) techniques are commonly used to handle artifacts. However, these may remove not only the EMG artifacts but also some useful electroencephalography (EEG) sources. To reduce this useful information loss, we propose a new technique for statistically selecting EEG channels that are contaminated with class-dependent EMG (henceforth called EMG-CCh). The EMG-CCh is selected based on the correlation between EEG and facial EMG channels. They were compared (using a Wilcoxon test) to determine whether the artifacts played a significant role in class separation. To ensure that the promising results are not due to the weak EMG removal, reliability tests were done In our data set, the comparison results between BSS artifact removal applied in two ways, to all channels and only to EMG-CCh showed that ICA, PCA, and BSS-CCA can yield significantly better ( ) class separation with the proposed method (79% of the cases for ICA, 53% for PCA, and 11% for BSS-CCA). With BCI competition data, we saw improvement in 60% of the cases for ICA and BSS-CCA. The simple method proposed in this paper showed improvement in class separation with both our data and the BCI competition data. There are no existing methods for removing EMG artifacts based on the correlation between the EEG and EMG channels. Also, the EMG-CCh selection can be used on its own or it can be combined with pre-existing artifact handling methods. For these reasons, we believe that this method can be useful for other EEG studies.

Time estimation and beta segregation: An EEG study and graph theoretical approach.

Elucidation of the neural correlates of time perception constitutes an important research topic in cognitive neuroscience. The focus to date has been on durations in the millisecond to seconds range, but here we used electroencephalography (EEG) to examine brain functional connectivity during much longer durations (i.e., 15 min). For this purpose, we conducted an initial exploratory experiment followed by a confirmatory experiment. Our results showed that those participants who overestimated time exhibited lower activity of beta (18-30 Hz) at several electrode sites. Furthermore, graph theoretical analysis indicated significant differences in the beta range (15-30 Hz) between those that overestimated and underestimated time. Participants who underestimated time showed higher clustering coefficient compared to those that overestimated time. We discuss our results in terms of two aspects. FFT results, as a linear approach, are discussed within localized/dedicated models (i.e., scalar timing model). Second, non-localized properties of psychological interval timing (as emphasized by intrinsic models) are addressed and discussed based on results derived from graph theory. Results suggested that although beta amplitude in central regions (related to activity of BG-thalamocortical pathway as a dedicated module) is important in relation to timing mechanisms, the properties of functional activity of brain networks; such as the segregation of beta network, are also crucial for time perception. These results may suggest subjective time may be created by vector units instead of scalar ticks.

Objective measures for detecting the auditory brainstem response: comparisons of specificity, sensitivity and detection time.

OBJECTIVE: To evaluate and compare the specificity, sensitivity and detection time of various time-domain and multi-band frequency domain methods when detecting the auditory brainstem response (ABR). DESIGN: Simulations and subject recorded data were used to assess and compare the performance of the Hotelling's T2 test (applied in either time or frequency domain), two versions of the modified q-sample uniform scores test and both the Fsp and Fmp, which were evaluated using both conventional F-distributions with assumed degrees of freedom and a bootstrap approach. STUDY SAMPLE: Data consisted of click-evoked ABRs and recordings of EEG background activity from 12 to 17 normal hearing adults, respectively. RESULTS: An overall advantage in sensitivity and detection time was demonstrated for the Hotelling's T2 test. The false-positive rates (FPRs) of the Fsp and Fmp were also closer to the nominal alpha-level when evaluating statistical significance using the bootstrap approach, as opposed to using conventional F-distributions. The FPRs of the remaining methods were slightly higher than expected. CONCLUSIONS: In this work, Hotelling's T2 outperformed the alternative methods for automatically detecting ABRs. Its promise as a sensitive and efficient detection method should now be tested in a larger clinical study.

Estudio aleatorizado controlado de la coherencia interhemisférica del electroencefalograma tras terapia asistida con delfines en niños con trastorno del espectro autista / Randomised controlled study of inter-hemispheric electroencephalographic coherence following assisted therapy with dolphins in children with autism spectrum disorders

Introducción. El trastorno del espectro autista (TEA) es un trastorno del neurodesarrollo asociado con trastornos de la función ejecutiva, el lenguaje, la función emocional y la función social, cuyo sustrato anatomofuncional se relaciona con una desorganización de las conexiones funcionales cerebrales. El objetivo es investigar las conexiones cerebrales en sujetos con TEA mediante análisis de la coherencia interhemisférica (CIH) del electroencefalograma cuantificado y sus cambios tras la terapia asistida con delfines (TAD) frente a la intervención terapéutica sin delfines (ITSD). Pacientes y métodos. Se determinó la CIH en 44 sujetos con TEA antes de asignarse aleatoriamente a dos grupos de tratamiento: TAD (n = 22) e ITSD (n = 22). Los resultados se analizaron estadísticamente mediante el test de la ANOVA multimedida para los factores intrasujeto (tiempo) e intersujeto (TAD frente a ITSD). Resultados. La CIH mostró una reducción significativa (p < 0,05) para ambos grupos en las frecuencias delta, theta, beta y alfa (p < 0,001) en la región frontal anterior (F3-F4). Se halló también una reducción en la frecuencia alfa en la región central (C3-C4) (p < 0,05), y alfa (p < 0,05) y beta (p < 0,001) en la región temporal (T3-T4). En la intersección con el tratamiento específico (TAD), la coherencia en la banda alfa aumentó en Fp1-Fp2 (p < 0,05) y no descendió la delta en F3-F4 (p < 0,05). Conclusión. En niños de 5 años con TEA, la TAD aumenta la CIH en la región frontal anterior y estabiliza la tendencia a la reducción de la banda delta en la región frontal posterior (AU)

Introduction. Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with impairments in executive function, language, emotional function, and social function. Its anatomofunctional substrate is related to a disorganization of the brain’s functional connections. The aim is to investigate the cerebral connections in subjects with ASD through the analysis of the interhemispheric coherence (IHC) of the quantified electroencephalogram and its changes after dolphin assisted therapy (DAT) versus therapeutical intervention without dolphins (TIWD). Patients and methods. The IHC was determined in 44 subjects with ASD before randomly assigning them to two therapeutic groups: DAT (n = 22) and TIWD (n = 22). The results were statistically analyzed through the multi-measure ANOVA test for within-subject (time) and between-subject (DAT vs TIWD) factors. Results. The IHC showed a significant reduction (p < 0.05) for both groups in the delta, theta, beta, and alpha frequencies (p < 0.001) in the anterior frontal region (F3-F4), alpha in the central region (C3-C4) (p < 0.05), and alpha (p < 0.05) and beta (p < 0.001) in the temporal region (T3-T4). In the intersection with the specific treatment (DAT), the coherence in the alpha band increased in Fp1-Fp2 (p < 0.05), and the delta did not decline in F3-F4 (p < 0.05). Conclusion. In 5-year-old children with ASD, DAT increases the IHC in the anterior frontal region and stabilizes the tendency to reduce the delta band in the posterior frontal region (AU)

Optimising the use of EEG in non-epileptic attack disorder: Results of a UK national service evaluation.

PURPOSE: To produce an evidence base to formulate guidelines for optimal performance of EEG in patients referred with a possible diagnosis of non-epileptic attack disorder (NEAD). METHODS: 51 UK EEG departments participated in the prospective study. A pro-forma was completed for all consecutive patients aged 5 years and over referred for EEG over a six month period. Information obtained included referral diagnosis, occurrence/type of attack during EEG, the use of suggestion, length of recording and who was present during the EEG. RESULTS: 11,298 patients were entered into the study. 376 psychogenic non-epileptic seizures (PNES) occurred of which 337 were considered to be of the habitual type. In those patients suspected of having NEAD prior to referral, the use of verbal suggestion increased the yield of habitual attacks by a factor of three in both adults and children. Using suggestive techniques twice, improved the yield further. Non-habitual attacks occurred equally whether or not suggestion was used. At least 90% of habitual PNES occurred within the first 30 min of recording even in those patients having prolonged EEGs. In EEGs where additional professional personnel were present, PNES occurred more frequently. CONCLUSION: This large multicentre study provides evidence to inform recommendations for EEG to investigate NEAD. We recommend the use of verbal suggestion at least twice and where practical the presence of additional professional staff. A thirty minute recording is sufficient to record a habitual PNES in most instances.