Magnetoencephalography Signals Research Articles

Interactions of BDNF Val66Met Polymorphism and Menstrual Pain on Brain Complexity.

The irregularity and uncertainty of neurophysiologic signals across different time scales can be regarded as neural complexity, which is related to the adaptability of the nervous system and the information processing between neurons. We recently reported general loss of brain complexity, as measured by multiscale sample entropy (MSE), at pain-related regions in females with primary dysmenorrhea (PDM). However, it is unclear whether this loss of brain complexity is associated with inter-subject genetic variations. Brain-derived neurotrophic factor (BDNF) is a widely expressed neurotrophin in the brain and is crucial to neural plasticity. The BDNF Val66Met single-nucleotide polymorphism (SNP) is associated with mood, stress, and pain conditions. Therefore, we aimed to examine the interactions of BDNF Val66Met polymorphism and long-term menstrual pain experience on brain complexity. We genotyped BDNF Val66Met SNP in 80 PDM females (20 Val/Val, 31 Val/Met, 29 Met/Met) and 76 healthy female controls (25 Val/Val, 36 Val/Met, 15 Met/Met). MSE analysis was applied to neural source activity estimated from resting-state magnetoencephalography (MEG) signals during pain-free state. We found that brain complexity alterations were associated with the interactions of BDNF Val66Met polymorphism and menstrual pain experience. In healthy female controls, Met carriers (Val/Met and Met/Met) demonstrated lower brain complexity than Val/Val homozygotes in extensive brain regions, suggesting a possible protective role of Val/Val homozygosity in brain complexity. However, after experiencing long-term menstrual pain, the complexity differences between different genotypes in healthy controls were greatly diminished in PDM females, especially in the limbic system, including the hippocampus and amygdala. Our results suggest that pain experience preponderantly affects the effect of BDNF Val66Met polymorphism on brain complexity. The results of the present study also highlight the potential utilization of resting-state brain complexity for the development of new therapeutic strategies in patients with chronic pain.

Magnetoencephalography and normal pressure hydrocephalus: A case report

In a case study of an 82-year old male spontaneous magnetoencephalography recordings revealed lower magnetic fields at frontal and frontotemporal regions compared to central and posterior regions. This finding correlated well with the significant ventricular distention, and specifically the enlargement of the frontal horns of the lateral ventricles, observed in presurgical computed tomography. The regional pattern of magnetoencephalography signal decrease in normal pressure hydrocephalus seems to be quite different from that encountered in brain atrophy. In the latter case, a more generalized distribution of low magnetic fields is observed, possibly reflecting the high sensitivity of magnetoencephalography to activity originating in sulci. Acquired data suggest that magnetoencephalography may be able to differentiate between normal pressure hydrocephalus and brain atrophy. Furthermore, magnetoencephalography could potentially constitute a non-invasive, non-imaging tool, useful in the selection of patients with normal pressure hydrocephalus suited to undergo shunt surgery.

An enriched granger causal model allowing variable static anatomical constraints.

The anatomical connectivity constrains but does not fully determine functional connectivity, especially when one explores into the dynamics over the course of a trial. Therefore, an enriched granger causal model (GCM) integrated with anatomical prior information is proposed in this study, to describe the dynamic effective connectivity to distinguish the depression and explore the pathogenesis of depression. In the proposed frame, the anatomical information was converted via an optimized transformation model, which was then integrated into the normal GCM by variational bayesian model. Magnetoencephalography (MEG) signals and diffusion tensor imaging (DTI) of 24 depressive patients and 24 matched controls were utilized for performance comparison. Together with the sliding windowed MEG signals under sad facial stimuli, the enriched GCM was applied to calculate the regional-pair dynamic effective connectivity, which were repeatedly sifted via feature selection and fed into different classifiers. From the aspects of model errors and recognition accuracy rates, results supported the superiority of the enriched GCM with anatomical priors over the normal GCM. For the effective connectivity with anatomical priors, the best subject discrimination accuracy of SVM was 85.42% (the sensitivity was 87.50% and the specificity was 83.33%). Furthermore, discriminative feature analysis suggested that the enriched GCM that detect the variable anatomical constraint on function could better detect more stringent and less dynamic brain function in depression. The proposed approach is valuable in dynamic functional dysfunction exploration in depression and could be useful for depression recognition.

Relative Yield of MEG and EEG Spikes in Simultaneous Recordings.

Most clinical magnetoencephalography (MEG) centers record both MEG and EEG, but model only MEG sources. This may be related to the belief that MEG spikes are more prevalent, MEG is more sensitive, or to proprietary software limitations. Biophysics would contend, however, that EEG, being sensitive to radial and tangential source orientations, would provide complementary data for analysis. We recorded 306 channels of MEG and 25 channels of EEG simultaneously in 297 consecutive patients over 3 years. We inspected the MEG and EEG recordings separately, identified spikes in both, determined whether their voltage and/or magnetometer magnetic fields were dipolar and thus model-worthy, and segregated them into types based on similar and distinct field topography. We placed for each patient their spike types into categories, including those with both a recognizable MEG and EEG signal and those with only an MEG and only an EEG signal. Eighty-three percent of patients had spikes recorded, and these patients had an average of 2.7 spike types each. Fifty-six percent of spike types were present in both MEG and EEG. However, 36% of spike types were only evident in EEG, whereas 8% were noted in MEG alone. In 49% of patients with spikes, MEG review missed at least one spike type, whereas in 17% of patients, EEG review missed at least one spike type. To obtain an optimal yield of diagnostic information, EEG should also be subjected to source analysis in any clinical MEG study. EEG and MEG data are indeed complementary.

Interhemispheric connectivity during lateralized lexical decision.

The well-established right visual field (RVF-lh) advantage in word recognition is commonly attributed to the typical left hemisphere dominance in language; words presented to the LVF-rh are processed less efficiently due to the need for transcallosal transfer from the right to left hemisphere. The exact stage for this hemispheric transfer is currently unsettled. Some studies suggest that transfer occurs at very early stages between primary visual regions, whereas other studies suggest that transfer occurs between the left visual word form area and its right hemisphere homolog. This study explores these conflicting accounts and finds evidence for both. Participants conducted a lateralized lexical decision task with both unilateral and bilateral display conditions. Connectivity analyses were conducted from magnetoencephalography signals that were localized to the left middle occipital gyrus (LMOG), right middle occipital gyrus (RMOG), left visual word form area (LVWFA), and right visual word form area (RVWA). Results from unilateral trials showed asymmetrical interhemispheric connectivity from the RMOG to LMOG and symmetrical interhemispheric connectivity between the LVWFA and RVWFA. Furthermore, bilateral presentations led to reduced interhemispheric connectivity between both homologous region of interest pairs. Together, these results suggest that lateralized word recognition involves multiple stages of interhemispheric interactions and that these interactions are reduced with bilateral displays.

Feasibility of functional MRI at ultralow magnetic field via changes in cerebral blood volume

We investigate the feasibility of performing functional MRI (fMRI) at ultralow field (ULF) with a Superconducting QUantum Interference Device (SQUID), as used for detecting magnetoencephalography (MEG) signals from the human head. While there is negligible magnetic susceptibility variation to produce blood oxygenation level-dependent (BOLD) contrast at ULF, changes in cerebral blood volume (CBV) may be a sensitive mechanism for fMRI given the five-fold spread in spin-lattice relaxation time (T1) values across the constituents of the human brain. We undertook simulations of functional signal strength for a simplified brain model involving activation of a primary cortical region in a manner consistent with a blocked task experiment. Our simulations involve measured values of T1 at ULF and experimental parameters for the performance of an upgraded ULFMRI scanner. Under ideal experimental conditions we predict a functional signal-to-noise ratio of between 3.1 and 7.1 for an imaging time of 30 min, or between 1.5 and 3.5 for a blocked task experiment lasting 7.5 min. Our simulations suggest it may be feasible to perform fMRI using a ULFMRI system designed to perform MRI and MEG in situ.

Decreased event-related theta power and phase-synchrony in young binge drinkers during target detection: An anatomically-constrained MEG approach.

The prevalence of binge drinking has risen in recent years. It is associated with a range of neurocognitive deficits among adolescents and young emerging adults who are especially vulnerable to alcohol use. Attention is an essential dimension of executive functioning and attentional disturbances may be associated with hazardous drinking. The aim of the study was to examine the oscillatory neural dynamics of attentional control during visual target detection in emerging young adults as a function of binge drinking. In total, 51 first-year university students (18 ± 0.6 years) were assigned to light drinking ( n = 26), and binge drinking ( n = 25) groups based on their alcohol consumption patterns. A high-density magnetoencephalography signal was combined with structural magnetic resonance imaging in an anatomically constrained magnetoencephalography model to estimate event-related source power in a theta (4-7 Hz) frequency band. Phase-locked co-oscillations were further estimated between the principally activated regions during task performance. Overall, the greatest event-related theta power was elicited by targets in the right inferior frontal cortex and it correlated with performance accuracy and selective attention scores. Binge drinkers exhibited lower theta power and dysregulated oscillatory synchrony to targets in the right inferior frontal cortex, which correlated with higher levels of alcohol consumption. These results confirm that a highly interactive network in the right inferior frontal cortex subserves attentional control, revealing the importance of theta oscillations and neural synchrony for attentional capture and contextual maintenance. Attenuation of theta power and synchronous interactions in binge drinkers may indicate early stages of suboptimal integrative processing in young, highly functioning binge drinkers.

A signal-to-image transformation approach for EEG and MEG signal classification

The classification of magnetic signals has become one of the challenging research problems in brain computer interfaces (BCIs). Magnetic signals, as measured with electroencephalography (EEG) and magnetoencephalography (MEG), contain lots of additional information on the bioelectrical activity of the brain. In this paper, we propose a simple transformation method that utilises signal-to-image conversion. This conversion is a kind of finite amplitude frequency transformation based on the changing points of the signals. In other words, arbitrary time domain signals are converted to two-dimensional finite images, which are then used in the classification of the signals. In feature extraction, the Harris corner detector and scale-invariant feature transform are combined with the bag of visual words, and these features are then classified by using the k-nearest neighbour algorithm. To confirm the validity of the proposed method, experiments are conducted on the BCI Competition 2003 Datasets Ia and BCI Competition 2008 Dataset III. The classification accuracy of the proposed method is over 96.21% for Dataset Ia and 78.99% for Dataset III. It is apparent from the results that the EEG and MEG signals are quite successfully classified by employing the proposed method.

Hexadirectional Modulation of High-Frequency Electrophysiological Activity in the Human Anterior Medial Temporal Lobe Maps Visual Space

Grid cells are one of the core building blocks of spatial navigation [1]. Single-cell recordings of grid cells in the rodent entorhinal cortex revealed hexagonal coding of the local environment during spatial navigation [1]. Grid-like activity has also been identified in human single-cell recordings during virtual navigation [2]. Human fMRI studies further provide evidence that grid-like signals are also accessible on a macroscopic level [3-7]. Studies in both non-humanprimates [8] and humans [9, 10] suggest that grid-like coding in the entorhinal cortex generalizes beyond spatial navigation during locomotion, providing evidence for grid-like mapping of visual space during visual exploration-akin to the grid cell positional code in rodents during spatial navigation. However, electrophysiological correlates of thegrid code in humans remain unknown. Here, we provide evidence for grid-like, hexadirectional coding of visual space by human high-frequency activity, based on two independent datasets: non-invasive magnetoencephalography (MEG) in healthy subjects and entorhinal intracranial electroencephalography (EEG) recordings in an epileptic patient. Both datasets consistently show a hexadirectional modulation of broadband high-frequency activity (60-120Hz). Our findings provide first evidence for a grid-like MEG signal, indicating that the human entorhinal cortex codes visual space in a grid-like manner [8-10], and support the view that grid coding generalizes beyond environmental mapping during locomotion [4-6, 11]. Due to their millisecond accuracy, MEG recordings allow linking of grid-like activity to epochs during relevant behavior, thereby opening up the possibility for new MEG-based investigations of grid coding at high temporal resolution.

A test of weak separability for multi-way functional data, with application to brain connectivity studies

This paper concerns the modeling of multi-way functional data where double or multiple indices are involved. We introduce a concept of weak separability. The weakly separable structure supports the use of factorization methods that decompose the signal into its spatial and temporal components. The analysis reveals interesting connections to the usual strongly separable covariance structure, and provides insights into tensor methods for multi-way functional data. We propose a formal test for the weak separability hypothesis, where the asymptotic null distribution of the test statistic is a chi-square type mixture. The method is applied to study brain functional connectivity derived from source localized magnetoencephalography signals during motor tasks.

Neural processing of task-irrelevant but salient object: an MEG study

In a cluttered visual environment, in addition to focusing on task-relevant objects, it is also important for attention to be occasionally captured to task-irrelevant but salient stimuli. Our previous EEG study has revealed that attention dynamically coordinates neural representations of multiple objects and flexibly modulates the sampling profile to manage different task demands (Jia et al., 2017). Meanwhile, it remains unclear how the brain deals with task-irrelevant but salient objects and further registers them into the task framework. To address the issue, we recorded magnetoencephalography (MEG) signals from 20 human subjects while they were presented with a multi-object visual display and performed a central fixation task. Notably, one of the items was made salient by being distinct from the others (e.g., red among green, etc.) but was completely task-irrelevant (i.e., demanding central fixation task). Next, we employed a temporal response function (TRF) approach (Liu et al., in press) to dissociate the time-resolved neuronal response that specifically tracks the salient (S-TRF) and non-salient (NS-TRF) items from the same MEG signals. We then performed source analysis on the S-TRF and NS-TRF responses to examine the fine spatiotemporal neuronal profiles. Our data demonstrates that salient object, compared to the non-salient one, first initiated response in right temporoparietal area (TPJ) at about 120 ms, followed by activation in left intraparietal sulcus (IPS) and bilateral frontal area, and finally reached early visual area (EVA). Interestingly, after the information was conveyed back to EVA, salient object showed higher alpha-band response compared to the non-salient one, suggesting a registration process during which the salient but task-irrelevant item was further inhibited. Our results support the crucial role of dorsal attentional network in saliency representation and provide a temporally precise and whole-brain description of how the salient information is processed, transferred, and integrated into the current task context. Meeting abstract presented at VSS 2018

Magnetoencephalography decoding reveals structural differences within integrative decision processes.

When confronted with complex inputs consisting of multiple elements, humans use various strategies to integrate the elements quickly and accurately. For instance, accuracy may be improved by processing elements one at a time1-4 or over extended periods5-8; speed can increase if the internal representation of elements is accelerated9,10. However, little is known about how humans actually approach these challenges because behavioural findings can be accounted for by multiple alternative process models11 and neuroimaging investigations typically rely on haemodynamic signals that change too slowly. Consequently, to uncover the fast neural dynamics that support information integration, we decoded magnetoencephalographic signals that were recorded as human subjects performed a complex decision task. Our findings reveal three sources of individual differences in the temporal structure of the integration process-sequential representation, partial reinstatement and early computation-each having a dissociable effect on how subjects handled problem complexity and temporal constraints. Our findings shed new light on the structure and influence of self-determined neural integration processes.

Interictal Abnormalities of Neuromagnetic Gamma Oscillations in Migraine Following Negative Emotional Stimulation.

Here, we aimed to investigate brain activity in migraineurs in response to emotional stimulation. Magnetoencephalography (MEG) was used to examine 20 patients with episodic migraine (EM group), 15 patients with chronic migraine (CM group), and 35 healthy participants (control group). Neuromagnetic brain activity was elicited by emotional stimulation using photographs of facial expressions. We analyzed the latency and amplitude of M100 and M170 components and used Morlet wavelet and beamformers to analyze the spectral and spatial signatures of MEG signals in gamma band (30–100 Hz). We found that the timing and frequency of MEG activity differed across the three groups in response negative emotional stimuli. First, peak M170 amplitude was significantly lower in the CM group than in the control group. Second, compared with the control group, the average spectral power was significantly lower in the EM group and CM group at M100 and M170. Third, the average spectral powers of the M100 and M170 in the CM group were negatively correlated with either HAM-D scores or migraine attack frequency. No significant differences across groups was found for positive or neutral emotional stimuli. Furthermore, after negative emotional stimuli, the MEG source analysis demonstrated that the CM group showed a significantly higher percentage of amygdala activation than the control group for M100 and M170. Thus, during headache free phases, migraineurs have abnormal brain activity in the gamma band in response to negative emotional stimuli.Trial Registration: ChiCTR-RNC-17012599. Registered 7 September, 2017.

Developmental Trajectory of Infant Brain Signal Variability: A Longitudinal Pilot Study.

The infant brain shows rapid neural network development that considerably influences cognitive and behavioral abilities in later life. Reportedly, this neural development process can be indexed by estimating neural signal complexity. However, the precise developmental trajectory of brain signal complexity during infancy remains elusive. This study was conducted to ascertain the trajectory of magnetoencephalography (MEG) signal complexity from 2 months to 3 years of age in five infants using multiscale entropy (MSE), which captures signal complexity at multiple temporal scales. Analyses revealed scale-dependent developmental trajectories. Specifically, signal complexity predominantly increased from 5 to 15 months of age at higher temporal scales, whereas the complexity at lower temporal scales was constant across age, except in one infant who showed decreased complexity. Despite a small sample size limiting this study’s power, this is the first report of a longitudinal investigation of changes in brain signal complexity during early infancy and is unique in its application of MSE analysis of longitudinal MEG data during infancy. The results of this pilot study may serve to further our understanding of the longitudinal changes in the neural dynamics of the developing infant brain.

Altered Gamma Oscillations during Motor Control in Children with Autism Spectrum Disorder.

Autism is hypothesized to result in a cortical excitatory and inhibitory imbalance driven by inhibitory interneuron dysfunction, which is associated with the generation of gamma oscillations. On the other hand, impaired motor control has been widely reported in autism. However, no study has focused on the gamma oscillations during motor control in autism. In the present study, we investigated the motor-related gamma oscillations in autism using magnetoencephalography. Magnetoencephalographic signals were recorded from 14 right-handed human children with autism (5 female), aged 5–7 years, and age- and IQ-matched 15 typically developing children during a motor task using their right index finger. Consistent with previous studies, the autism group showed a significantly longer button response time and reduced amplitude of motor-evoked magnetic fields. We observed that the autism group exhibited a low peak frequency of motor-related gamma oscillations from the contralateral primary motor cortex, and these were associated with the severity of autism symptoms. The autism group showed a reduced power of motor-related gamma oscillations in the bilateral primary motor cortex. A linear discriminant analysis using the button response time and gamma oscillations showed a high classification performance (86.2% accuracy). The alterations of the gamma oscillations in autism might reflect the cortical excitatory and inhibitory imbalance. Our findings provide an important clue into the behavioral and neurophysiological alterations in autism and a potential biomarker for autism.SIGNIFICANCE STATEMENT Currently, the diagnosis of autism has been based on behavioral assessments, and a crucial issue in the diagnosis of autism is to identify objective and quantifiable clinical biomarkers. A key hypothesis of the neurophysiology of autism is an excitatory and inhibitory imbalance in the brain, which is associated with the generation of gamma oscillations. On the other hand, motor deficits have also been widely reported in autism. This is the first study to demonstrate low motor performance and altered motor-related gamma oscillations in autism, reflecting a brain excitatory and inhibitory imbalance. Using these behavioral and neurophysiological parameters, we classified autism and control group with good accuracy. This work provides important information on behavioral and neurophysiological alterations in patients with autism.

Noise cancellation for a whole-head magnetometer-based MEG system in hospital environment

We describe a strategy of removing magnetic field interference for a whole-head pediatric magnetoencephalography (MEG) system (‘babyMEG’) installed in a hospital. The 375-channel sensor array of babyMEG consists entirely of magnetometers in two layers to maximize the sensitivity for detecting MEG signals from infants, toddlers, and young children. It is equipped with a continuously operating closed-cycle helium recycler to reduce the operating costs. These two features pose special challenges for noise cancellation. Our strategy uses a combination of several methods. The system is installed in a light-weight, magnetically shielded room (MSR) equipped with an active external shielding. In addition we employ two software-based techniques—a signal space projection (SSP) technique and a synthetic gradiometer (SG) method—for removing the environmental magnetic noise in real time and displaying the output online. The shielding effects are: passive shielding–36 dB, active shielding–12 dB, SSP–40 dB and SG–40 dB, for a combined maximum shielding of about 90 dB at 0.1 Hz. We evaluated the performance of the babyMEG after applying the noise cancellation techniques. The dipole localization errors were <3 mm after averaging 50 epochs with empty room noise in a simulation study for dipoles >10 nAm, which is in the low range of empirically observed dipole moments. In a phantom study with realistic environmental noise, we could clearly recover an evoked cortical magnetic field produced by a 20 nAm dipole after averaging 50 epochs. The localization error was ∼6 mm after averaging 20 epochs. In infants, we could clearly detect a somatic evoked field after averaging ∼20 responses. The unique two-layer sensor design combined with the SSP or SG provides effective noise suppression for a magnetometer-based pediatric MEG system in hospital environment with the closed-cycle helium recycler operating continuously during MEG measurements.

Decoding the processing stages of mental arithmetic with magnetoencephalography

Elementary arithmetic is highly prevalent in our daily lives. However, despite decades of research, we are only beginning to understand how the brain solves simple calculations. Here, we applied machine learning techniques to magnetoencephalography (MEG) signals in an effort to decompose the successive processing stages and mental transformations underlying elementary arithmetic. Adults subjects verified single-digit addition and subtraction problems such as 3 + 2 = 9 in which each successive symbol was presented sequentially. MEG signals revealed a cascade of partially overlapping brain states. While the first operand could be transiently decoded above chance level, primarily based on its visual properties, the decoding of the second operand was more accurate and lasted longer. Representational similarity analyses suggested that this decoding rested on both visual and magnitude codes. We were also able to decode the operation type (additions vs. subtraction) during practically the entire trial after the presentation of the operation sign. At the decision stage, MEG indicated a fast and highly overlapping temporal dynamics for (1) identifying the proposed result, (2) judging whether it was correct or incorrect, and (3) pressing the response button. Surprisingly, however, the internally computed result could not be decoded. Our results provide a first comprehensive picture of the unfolding processing stages underlying arithmetic calculations at a single-trial level, and suggest that externally and internally generated neural codes may have different neural substrates.

Magnetoencephalography With Optically Pumped 4He Magnetometers at Ambient Temperature.

In this paper, we present the first proof of concept confirming the possibility to record magnetoencephalographic (MEG) signals with optically pumped magnetometers (OPMs) based on the parametric resonance of 4He atoms. The main advantage of this kind of OPM is the possibility to provide a tri-axis vector measurement of the magnetic field at room-temperature (the 4He vapor is neither cooled nor heated). The sensor achieves a sensitivity of 210 fT/ √ Hz in the bandwidth [2-300 Hz]. MEG simulation studies with a brain phantom were cross-validated with real MEG measurements on a healthy subject. For both studies, MEG signal was recorded consecutively with OPMs and superconducting quantum interference devices (SQUIDs) used as reference sensors. For healthy subject MEG recordings, three MEG proofs of concept were carried out: auditory evoked fields, visual evoked fields, and spontaneous activity. M100 peaks have been detected on evoked responses recorded by both OPMs and SQUIDs with no significant difference in latency. Concerning spontaneous activity, an attenuation of the signal power between 8-12 Hz (alpha band) related to eyes opening has been observed with OPM similarly to SQUID. All these results confirm that the room temperature vector 4He OPMs can record MEG signals and provide reliable information on brain activity.

Training in Use of Brain-Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements.

Objective: Brain-machine interfaces (BMIs) are useful for inducing plastic changes in cortical representation. A BMI first decodes hand movements using cortical signals and then converts the decoded information into movements of a robotic hand. By using the BMI robotic hand, the cortical representation decoded by the BMI is modulated to improve decoding accuracy. We developed a BMI based on real-time magnetoencephalography (MEG) signals to control a robotic hand using decoded hand movements. Subjects were trained to use the BMI robotic hand freely for 10 min to evaluate plastic changes in the cortical representation due to the training.Method: We trained nine young healthy subjects with normal motor function. In open-loop conditions, they were instructed to grasp or open their right hands during MEG recording. Time-averaged MEG signals were then used to train a real decoder to control the robotic arm in real time. Then, subjects were instructed to control the BMI-controlled robotic hand by moving their right hands for 10 min while watching the robot's movement. During this closed-loop session, subjects tried to improve their ability to control the robot. Finally, subjects performed the same offline task to compare cortical activities related to the hand movements. As a control, we used a random decoder trained by the MEG signals with shuffled movement labels. We performed the same experiments with the random decoder as a crossover trial. To evaluate the cortical representation, cortical currents were estimated using a source localization technique. Hand movements were also decoded by a support vector machine using the MEG signals during the offline task. The classification accuracy of the movements was compared among offline tasks.Results: During the BMI training with the real decoder, the subjects succeeded in improving their accuracy in controlling the BMI robotic hand with correct rates of 0.28 ± 0.13 to 0.50 ± 0.11 (p = 0.017, n = 8, paired Student's t-test). Moreover, the classification accuracy of hand movements during the offline task was significantly increased after BMI training with the real decoder from 62.7 ± 6.5 to 70.0 ± 11.1% (p = 0.022, n = 8, t(7) = 2.93, paired Student's t-test), whereas accuracy did not significantly change after BMI training with the random decoder from 63.0 ± 8.8 to 66.4 ± 9.0% (p = 0.225, n = 8, t(7) = 1.33).Conclusion: BMI training is a useful tool to train the cortical activity necessary for BMI control and to induce some plastic changes in the activity.

Relationships Between Behavioral And Single-Trial Target Detection Performance With Magnetoencephalography.

Target detection during serial visual presentation tasks is an active research topic in the brain-computer interface (BCI) community as this type of paradigm allows to take advantage of event-related potentials (ERPs) through electroencephalography (EEG) recordings to enhance the accuracy of target detection. The detection of brain evoked responses at the single-trial level remains a challenging task and can be exploited in various applications. Typical non-invasive BCIs based on event-related brain responses use EEG. In clinical settings, brain signals recorded with magnetoencephalography (MEG) can be advantageously used thanks to their high spatial and temporal resolution. In this study, we address the problem of the relationships between behavioral performance and single-trial detection by considering a task with different levels of difficulty. We consider images of faces with six different facial expressions (anger, disgust, fear, neutrality, sadness, and happiness). We consider MEG signals recorded on ten healthy participants in six sessions where targets were one of the six types of facial expressions in each session. The results support the conclusion that a high performance can be obtained at the single-trial level $( {AUC }= 0 . 903 \pm 0 .045)$, and that the performance is correlated with the behavioral performance (reaction time and hit rate).