No abstract available.
Epilepsy* ; Magnetoencephalography*

Establishing the significance of observed effects is a preliminary requirement for any meaningful interpretation of clinical and experimental Electroencephalography or Magnetoencephalography (MEG) data. We propose a method to evaluate significance on the level of sensors whilst retaining full temporal or spectral resolution. Input data are multiple realizations of sensor data. In this context, multiple realizations may be the individual epochs obtained in an evoked-response experiment, or group study data, possibly averaged within subject and event type, or spontaneous events such as spikes of different types. In this contribution, we apply Statistical non-Parametric Mapping (SnPM) to MEG sensor data. SnPM is a non-parametric permutation or randomization test that is assumption-free regarding distributional properties of the underlying data. The method, referred to as Maps SnPM, is demonstrated using MEG data from an auditory mismatch negativity paradigm with one frequent and two rare stimuli and validated by comparison with Topographic Analysis of Variance (TANOVA). The result is a time- or frequency-resolved breakdown of sensors that show consistent activity within and/or differ significantly between event or spike types. TANOVA and Maps SnPM were applied to the individual epochs obtained in an evoked-response experiment. The TANOVA analysis established data plausibility and identified latencies-of-interest for further analysis. Maps SnPM, in addition to the above, identified sensors of significantly different activity between stimulus types.
Electroencephalography ; Magnetoencephalography ; Methods ; Random Allocation

Magnetoencephalography (MEG) and magnetic source imaging (MSI) are becoming increasingly important modalities in the functional neuroimaging of children. MEG is the magnetic equivalent of electroencephalography (EEG) and is thus capable of noninvasively characterizing neuronal activity on a millisecond time scale. MSI combines this functional information provided by MEG with the high anatomic detail of conventional magnetic resonance imagings. Considerable effort is placed on analyzing the configuration and number of spike waves by MEG that relate to a primary epileptiform discharge. Such MEG spike clusters are corroborated now by intraoperative invasive subdural grid monitoring that show good correlation in the majority of cases. Another important role of MEG relates to the mapping of critical regions of brain function using known paradigms for speech, motor, sensory, visual, and auditory brain cortex. In this review, I would introduce the background of MEG, data acquisition and analysis, and clinical application of MEG in children with epilepsy.
Brain ; Child* ; Electroencephalography ; Epilepsy ; Functional Neuroimaging ; Humans ; Magnetoencephalography* ; Neurons

Data from magnetoencephalography (MEG) and electroencephalography (EEG) suffer from a rather limited signal-to-noise-ratio (SNR) due to cortical background activities and other artifacts. In order to study the effect of the SNR on the size and distribution of dipole clusters reconstructed from interictal epileptic spikes, we performed simulations using realistically shaped volume conductor models and extended cortical sources with different sensor configurations. Head models and cortical surfaces were derived from an averaged magnetic resonance image dataset (Montreal Neurological Institute). Extended sources were simulated by spherical patches with Gaussian current distributions on the folded cortical surface. Different patch sizes were used to investigate cancellation effects from opposing walls of sulcal foldings and to estimate corresponding changes in MEG and EEG sensitivity distributions. Finally, white noise was added to the simulated fields and equivalent current dipole reconstructions were performed to determine size and shape of the resulting dipole clusters. Neuronal currents are oriented perpendicular to the local cortical surface and show cancellation effects of source components on opposing sulcal walls. Since these mostly tangential aspects from large cortical patches cancel out, large extended sources exhibit more radial components in the head geometry. This effect has a larger impact on MEG data as compared to EEG, because in a spherical head model radial currents do not yield any magnetic field. Confidence volumes of single reconstructed dipoles from simulated data at different SNRs show a good correlation with the extension of clusters from repeated dipole reconstructions. Size and shape of dipole clusters reconstructed from extended cortical sources do not only depend on spike and timepoint selection, but also strongly on the SNR of the measured interictal MEG or EEG data. In a linear approximation the size of the clusters is proportional to the inverse SNR.
Artifacts ; Dataset ; Electroencephalography* ; Head ; Magnetic Fields ; Magnetoencephalography ; Neurons ; Noise

Exposure of humans to unusual spaces is effective to observe the adaptive strategy for an environment. Though adaptation to such spaces has been typically tested with vision, little has been examined about adaptation to left–right reversed audition, partially due to the apparatus for adaptation. Thus, it is unclear if the adaptive effects reach early auditory processing. Here, we constructed a left–right reversed stereophonic system using only wearable devices and asked two participants to wear it for 4 weeks. Every week, the magnetoencephalographic responses were measured under the selective reaction time task, where they immediately distinguished between sounds delivered to either the left or the right ear with the index finger on the compatible or incompatible side. The constructed system showed high performance in sound localization and achieved gradual reduction of a feeling of strangeness. The N1m intensities for the response-compatible sounds tended to be larger than those for the response-incompatible sounds until the third week but decreased on the fourth week, which correlated with the initially shorter and longer reaction times for the compatible and incompatible conditions, respectively. In the second week, disruption of the auditory-motor connectivity was observed with the largest N1m intensities and the longest reaction times, irrespective of compatibility. In conclusion, we successfully produced a high-quality space of left–right reversed audition using our system. The results suggest that a 4-week exposure to the reversed audition causes optimization of the auditory-motor coordination according to the new rule, which eventually results in the modulation of early auditory processing.
Ear ; Fingers ; Hearing* ; Humans ; Magnetoencephalography* ; Reaction Time ; Sound Localization

Speech comprehension is a central cognitive function of the human brain. In cognitive neuroscience, a fundamental question is to understand how neural activity encodes the acoustic properties of a continuous speech stream and resolves multiple levels of linguistic structures at the same time. This paper reviews the recently developed research paradigms that employ electroencephalography (EEG) or magnetoencephalography (MEG) to capture neural tracking of acoustic features or linguistic structures of continuous speech. This review focuses on two questions in speech processing: (1) The encoding of continuously changing acoustic properties of speech; (2) The representation of hierarchical linguistic units, including syllables, words, phrases and sentences. Studies have found that the low-frequency cortical activity tracks the speech envelope. In addition, the cortical activities on different time scales track multiple levels of linguistic units and constitute a representation of hierarchically organized linguistic units. The article reviewed these studies, which provided new insights into the processes of continuous speech in the human brain.
Acoustic Stimulation ; Electroencephalography ; Humans ; Magnetoencephalography ; Speech ; physiology ; Speech Perception

Advances in network science and computer engineering have enabled brain connectivity analysis using clinical big data such as brain magnetic resonance imaging (MRI), electroencephalography (EEG), or magnetoencephalography (MEG). Resting-state functional connectivity analysis aims to reveal the characteristics of functional brain network in various diseases and normal brain maturation using resting-state EEG. Simplified sequence of resting-state functional connectivity analysis methods will be reviewed in this article. The outcomes from EEG resting-state connectivity analysis are comprised of connectivity itself of the specific condition and the network topology measure which describe the characteristics of specific connectivity. An increasing number of studies report the differences in the functional connection itself, global network measures including segregation (connectedness), integration (efficiency), and importance of specific nodes (centrality or node degree). Several issues that are relevant in the resting-state connectivity analysis are obtaining good quality EEG for analysis, consideration of particular features of EEG signal, understanding different types of association measures, and statistics for comparison of connectivities. Well-designed and carefully analyzed EEG resting-state connectivity analysis can provide useful information for patient care in pediatric neurology.
Brain ; Electroencephalography* ; Magnetic Resonance Imaging ; Magnetoencephalography ; Neurology ; Patient Care

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.
Bayes Theorem ; Brain ; physiology ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Magnetoencephalography ; Multivariate Analysis ; Principal Component Analysis

Independent component analysis (ICA) is a new method of signal statistical processing and widely used in many fields. We face several problems such as the different nature of source signals (e.g. both super-Gaussian and sub-Gaussian sources exist), unknown number of sources and contamination of the sensor signals with a high level of additive noise in the analysis of signal. A robust approach was proposed to solve these problems in this paper. Firstly, observations (noisy data) possessing high dimensionality were preprocessed and decomposed into a source signal subspace and a noise subspace. Then the number of sources was got through the cross-validation method, and this solved the problem that ICA could not confirm the number of sources. At last the transformed low-dimensional source signals were further separated with the fast and stable ICA algorithm. Through the analysis of artificially synthesized data and the real-world Magnetoencephalographic data, the efficacy of this robust approach was illustrated.
Algorithms ; Humans ; Magnetoencephalography ; methods ; Principal Component Analysis ; Signal Processing, Computer-Assisted

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.
Algorithms ; Brain ; physiology ; Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Humans ; Magnetoencephalography ; Principal Component Analysis