Single-channel seizure detection with clinical confirmation of seizure locations using CHB-MIT dataset.

Introduction: Long-term electroencephalography (EEG) monitoring is advised to patients with refractory epilepsy who have a failure of anti-seizure medication and therapy. However, its real-life application is limited mainly due to the use of multiple EEG channels. We proposed a patient-specific deep learning-based single-channel seizure detection approach using the long-term scalp EEG recordings of the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, in conjunction with neurologists' confirmation of spatial seizure characteristics of individual patients. Methods: We constructed 18-, 4-, and single-channel seizure detectors for 13 patients. Neurologists selected a specific channel among four channels, two close to the behind-the-ear and two at the forehead for each patient, after reviewing the patient's distinctive seizure locations with seizure re-annotation. Results: Our multi- and single-channel detectors achieved an average sensitivity of 97.05-100%, false alarm rate of 0.22-0.40/h, and latency of 2.1-3.4 s for identification of seizures in continuous EEG recordings. The results demonstrated that seizure detection performance of our single-channel approach was comparable to that of our multi-channel ones. Discussion: We suggest that our single-channel approach in conjunction with clinical designation of the most prominent seizure locations has a high potential for wearable seizure detection on long-term EEG recordings for patients with refractory epilepsy.

Deep learning-based automated detection and multiclass classification of focal interictal epileptiform discharges in scalp electroencephalograms.

Detection and spatial distribution analyses of interictal epileptiform discharges (IEDs) are important for diagnosing, classifying, and treating focal epilepsy. This study proposes deep learning-based models to detect focal IEDs in electroencephalography (EEG) recordings of the frontal, temporal, and occipital scalp regions. This study included 38 patients with frontal (n = 15), temporal (n = 13), and occipital (n = 10) IEDs and 232 controls without IEDs from a single tertiary center. All the EEG recordings were segmented into 1.5-s epochs and fed into 1- or 2-dimensional convolutional neural networks to construct binary classification models to detect IEDs in each focal region and multiclass classification models to categorize IEDs into frontal, temporal, and occipital regions. The binary classification models exhibited accuracies of 79.3-86.4%, 93.3-94.2%, and 95.5-97.2% for frontal, temporal, and occipital IEDs, respectively. The three- and four-class models exhibited accuracies of 87.0-88.7% and 74.6-74.9%, respectively, with temporal, occipital, and non-IEDs F1-scores of 89.9-92.3%, 84.9-90.6%, and 84.3-86.0%; and 86.6-86.7%, 86.8-87.2%, and 67.8-69.2% for the three- and four-class (frontal, 50.3-58.2%) models, respectively. The deep learning-based models could help enhance EEG interpretation. Although they performed well, the resolution of region-specific focal IED misinterpretations and further model improvement are needed.

Deep Learning-Based Detection of Epileptiform Discharges for Self-Limited Epilepsy With Centrotemporal Spikes.

Centrotemporal spike-waves (CTSWs) are typical interictal epileptiform discharges (IEDs) observed in centrotemporal regions in self-limited epilepsy with centrotemporal spikes (SLECTS). This study aims to develop a deep learning-based approach for automated detection of CTSWs in scalp electroencephalography (EEG) recordings of patients with SLECTS. To lower the substantial burden of IED annotation on clinicians, we simplified it by limiting IEDs to CTSWs because electroencephalographic patterns of CTSWs are known to be highly consistent. Two neurologists annotated 1672 CTSWs of 20 patients with SLECTS. Thereafter, we performed a two-level CTSW detection procedure: epoch-level and EEG-level. In the epoch-level detection, we constructed convolutional neural network-based classification models for CTSW and non-CTSW binary classification using the recordings of 20 patients and 20 controls. We then set the thresholds of the classification models for 100% specificity. In the EEG-level detection, we applied the threshold-adjusted classification models to the recordings of 50 patients and 50 controls that were not used in the epoch-level detection to distinguish between CTSW-positive (with one or more CTSWs) and CTSW-negative (with no CTSW) recordings based on the detection of CTSW presence. We obtained an average sensitivity, specificity, and accuracy of 99.8%, 98.4%, and 99.1%, respectively, with an average false detection rate of 0.19/hr for the controls. Our approach showed high detectability for CTSWs despite the simplified annotation process. We expect that the proposed CTSW detectors have potential clinical usefulness for efficiently reading EEGs and diagnosing SLECTS, and can significantly reduce the burden of IED annotation on clinicians.

Variations of Resting-State EEG-Based Functional Networks in Brain Maturation From Early Childhood to Adolescence.

BACKGROUND AND PURPOSE: Alterations in human brain functional networks with maturation have been explored extensively in numerous electroencephalography (EEG) and functional magnetic resonance imaging studies. It is known that the age-related changes in the functional networks occurring prior to adulthood deviate from ordinary trajectories of network-based brain maturation across the adult lifespan. METHODS: This study investigated the longitudinal evolution of resting-state EEG-based functional networks from early childhood to adolescence among 212 pediatric patients (age 12.2±3.5 years, range 4.4-17.9) in 6 frequency bands using 8 types of functional connectivity measures in the amplitude, frequency, and phase domains. RESULTS: Electrophysiological aspects of network-based pediatric brain maturation were characterized by increases in both functional segregation and integration up to middle adolescence. EEG oscillations in the upper alpha band reflected the age-related increases in mean node strengths and mean clustering coefficients and a decrease in the characteristic path lengths better than did those in the other frequency bands, especially for the phase-domain functional connectivity. The frequency-band-specific age-related changes in the global network metrics were influenced more by volume-conduction effects than by the domain specificity of the functional connectivity measures. CONCLUSIONS: We believe that this is the first study to reveal EEG-based functional network properties during preadult brain maturation based on various functional connectivity measures. The findings potentially have clinical applications in the diagnosis and treatment of age-related brain disorders.

Big data analysis and artificial intelligence in epilepsy - common data model analysis and machine learning-based seizure detection and forecasting.

There has been significant interest in big data analysis and artificial intelligence (AI) in medicine. Ever-increasing medical data and advanced computing power have enabled the number of big data analyses and AI studies to increase rapidly. Here we briefly introduce epilepsy, big data, and AI and review big data analysis using a common data model. Studies in which AI has been actively applied, such as those of electroencephalography epileptiform discharge detection, seizure detection, and forecasting, will be reviewed. We will also provide practical suggestions for pediatricians to understand and interpret big data analysis and AI research and work together with technical expertise.

Deep Convolutional Neural Network Based Interictal-Preictal Electroencephalography Prediction: Application to Focal Cortical Dysplasia Type-II.

We aimed to differentiate between the interictal and preictal states in epilepsy patients with focal cortical dysplasia (FCD) type-II using deep learning-based classifiers based on intracranial electroencephalography (EEG). We also investigated the practical conditions for high interictal-preictal discriminability in terms of spatiotemporal EEG characteristics and data size efficiency. Intracranial EEG recordings of nine epilepsy patients with FCD type-II (four female, five male; mean age: 10.7 years) were analyzed. Seizure onset and channel ranking were annotated by two epileptologists. We performed three consecutive interictal-preictal classification steps by varying the preictal length, number of electrodes, and sampling frequency with convolutional neural networks (CNN) using 30 s time-frequency data matrices. Classification performances were evaluated based on accuracy, F1 score, precision, and recall with respect to the above-mentioned three parameters. We found that (1) a 5 min preictal length provided the best classification performance, showing a remarkable enhancement of >13% on average compared to that with the 120 min preictal length; (2) four electrodes provided considerably high classification performance with a decrease of only approximately 1% on average compared to that with all channels; and (3) there was minimal performance change when quadrupling the sampling frequency from 128 Hz. Patient-specific performance variations were noticeable with respect to the preictal length, and three patients showed above-average performance enhancements of >28%. However, performance enhancements were low with respect to both the number of electrodes and sampling frequencies, and some patients showed at most 1-2% performance change. CNN-based classifiers from intracranial EEG recordings using a small number of electrodes and efficient sampling frequency are feasible for predicting the interictal-preictal state transition preceding seizures in epilepsy patients with FCD type-II. Preictal lengths affect the predictability in a patient-specific manner; therefore, pre-examinations for optimal preictal length will be helpful in seizure prediction.

Change of centrotemporal spikes from onset to remission in self-limited epilepsy with centrotemporal spikes (SLECTS).

OBJECTIVE: To reveal the changes of centrotemporal spikes that occur during the disease course of self-limited epilepsy with centrotemporal spikes (SLECTS). METHOD: We retrospectively reviewed the serial EEGs of 63 patients with SLECTS from initial diagnosis to remission. There were 32 patients who did not undergo treatment and 31 patients who underwent treatment with oxcarbazepine (OXC). The change of occurrence or abundance, voltage, and location of centrotemporal spikes of serial EEGs were analyzed and compared between the two groups. Clinical seizure evidenced and reported was counted. The time gap between seizure remission and EEG remission was measured in the two groups. RESULT: Changes of occurrence or abundance of the centrotemporal spikes were either abrupt (sudden disappearance of the frequent spikes on following EEG) or gradual (decline in number over 2 or more serial EEGs). Pattern of spike disappearance was not significantly different between the medication naïve group and OXC treated group. The spike voltage or the location of centrotemporal spikes did not change during the disease course in most cases. Delay between seizure remission and EEG normalization was 3.34 ± 1.75 (mean ± standard deviation, range: 0.77-7.97) years in untreated patients and 3.03 ± 1.41 (0.95-6.61) years in OXC-treated group. CONCLUSION: Pattern of spike disappearance in SLECTS was either abrupt or gradual. Treatment with OXC had no effect in the disappearance pattern. Precise data regarding the pattern of disappearance and delay between seizure remission and EEG normalization can help to understand the evolution of spike in SLECTS and to predict the timing of normalization of EEG after seizure remission.

Electroencephalographic Resting-State Functional Connectivity of Benign Epilepsy with Centrotemporal Spikes.

BACKGROUND AND PURPOSE: We aimed to reveal resting-state functional connectivity characteristics based on the spike-free waking electroencephalogram (EEG) of benign epilepsy with centrotemporal spikes (BECTS) patients, which usually appears normal in routine visual inspection. METHODS: Thirty BECTS patients and 30 disease-free and age- and sex-matched controls were included. Eight-second EEG epochs without artifacts were sampled and then bandpass filtered into the delta, theta, lower alpha, upper alpha, and beta bands to construct the association matrix. The weighted phase lag index (wPLI) was used as an association measure for EEG signals. The band-specific connectivity, which was represented as a matrix of wPLI values of all edges, was compared for analyzing the connectivity itself. The global wPLI, characteristic path length (CPL), and mean clustering coefficient were compared. RESULTS: The resting-state functional connectivity itself and the network topology differed in the BECTS patients. For the lower-alpha-band and beta-band connectivity, edges that showed significant differences had consistently lower wPLI values compared to the disease-free controls. The global wPLI value was significantly lower for BECTS patients than for the controls in lower-alpha-band connectivity (mean±SD; 0.241±0.034 vs. 0.276±0.054, p=0.024), while the CPL was significantly longer for BECTS in the same frequency band (mean±SD; 4.379±0.574 vs. 3.904±0.695, p=0.04). The resting-state functional connectivity of BECTS showed decreased connectivity, integration, and efficiency compared to controls. CONCLUSIONS: The connectivity differed significantly between BECTS patients and disease-free controls. In BECTS, global connectivity was significantly decreased and the resting-state functional connectivity showed lower efficiency in the lower alpha band.

Decoding pressure stimulation locations on the fingers from human neural activation patterns.

In this functional MRI study, we investigated how the human brain activity represents tactile location information evoked by pressure stimulation on fingers. Using the searchlight multivoxel pattern analysis, we looked for local activity patterns that could be decoded into one of four stimulated finger locations. The supramarginal gyrus (SMG) and the thalamus were found to contain distinct multivoxel patterns corresponding to individual stimulated locations. In contrast, the univariate general linear model analysis contrasting stimulation against resting phases for each finger identified activations mainly in the primary somatosensory cortex (S1), but not in SMG or in thalamus. Our results indicate that S1 might be involved in the detection of the presence of pressure stimuli, whereas the SMG and the thalamus might play a role in identifying which finger is stimulated. This finding may provide additional evidence for hierarchical information processing in the human somatosensory areas.

Neural Categorization of Vibrotactile Frequency in Flutter and Vibration Stimulations: An fMRI Study.

As the use of wearable haptic devices with vibrating alert features is commonplace, an understanding of the perceptual categorization of vibrotactile frequencies has become important. This understanding can be substantially enhanced by unveiling how neural activity represents vibrotactile frequency information. Using functional magnetic resonance imaging (fMRI), this study investigated categorical clustering patterns of the frequency-dependent neural activity evoked by vibrotactile stimuli with gradually changing frequencies from 20 to 200 Hz. First, a searchlight multi-voxel pattern analysis (MVPA) was used to find brain regions exhibiting neural activities associated with frequency information. We found that the contralateral postcentral gyrus (S1) and the supramarginal gyrus (SMG) carried frequency-dependent information. Next, we applied multidimensional scaling (MDS) to find low-dimensional neural representations of different frequencies obtained from the multi-voxel activity patterns within these regions. The clustering analysis on the MDS results showed that neural activity patterns of 20-100 Hz and 120-200 Hz were divided into two distinct groups. Interestingly, this neural grouping conformed to the perceptual frequency categories found in the previous behavioral studies. Our findings therefore suggest that neural activity patterns in the somatosensory cortical regions may provide a neural basis for the perceptual categorization of vibrotactile frequency.

Adaptation of cortical activity to sustained pressure stimulation on the fingertip.

BACKGROUND: Tactile adaptation is a phenomenon of the sensory system that results in temporal desensitization after an exposure to sustained or repetitive tactile stimuli. Previous studies reported psychophysical and physiological adaptation where perceived intensity and mechanoreceptive afferent signals exponentially decreased during tactile adaptation. Along with these studies, we hypothesized that somatosensory cortical activity in the human brain also exponentially decreased during tactile adaptation. The present neuroimaging study specifically investigated temporal changes in the human cortical responses to sustained pressure stimuli mediated by slow-adapting type I afferents. METHODS: We applied pressure stimulation for up to 15 s to the right index fingertip in 21 healthy participants and acquired functional magnetic resonance imaging (fMRI) data using a 3T MRI system. We analyzed cortical responses in terms of the degrees of cortical activation and inter-regional connectivity during sustained pressure stimulation. RESULTS: Our results revealed that the degrees of activation in the contralateral primary and secondary somatosensory cortices exponentially decreased over time and that intra- and inter-hemispheric inter-regional functional connectivity over the regions associated with tactile perception also linearly decreased or increased over time, during pressure stimulation. CONCLUSION: These results indicate that cortical activity dynamically adapts to sustained pressure stimulation mediated by SA-I afferents, involving changes in the degrees of activation on the cortical regions for tactile perception as well as in inter-regional functional connectivity among them. We speculate that these adaptive cortical activity may represent an efficient cortical processing of tactile information.

Decoding Accuracy in Supplementary Motor Cortex Correlates with Perceptual Sensitivity to Tactile Roughness.

Perceptual sensitivity to tactile roughness varies across individuals for the same degree of roughness. A number of neurophysiological studies have investigated the neural substrates of tactile roughness perception, but the neural processing underlying the strong individual differences in perceptual roughness sensitivity remains unknown. In this study, we explored the human brain activation patterns associated with the behavioral discriminability of surface texture roughness using functional magnetic resonance imaging (fMRI). First, a whole-brain searchlight multi-voxel pattern analysis (MVPA) was used to find brain regions from which we could decode roughness information. The searchlight MVPA revealed four brain regions showing significant decoding results: the supplementary motor area (SMA), contralateral postcentral gyrus (S1), and superior portion of the bilateral temporal pole (STP). Next, we evaluated the behavioral roughness discrimination sensitivity of each individual using the just-noticeable difference (JND) and correlated this with the decoding accuracy in each of the four regions. We found that only the SMA showed a significant correlation between neuronal decoding accuracy and JND across individuals; Participants with a smaller JND (i.e., better discrimination ability) exhibited higher decoding accuracy from their voxel response patterns in the SMA. Our findings suggest that multivariate voxel response patterns presented in the SMA represent individual perceptual sensitivity to tactile roughness and people with greater perceptual sensitivity to tactile roughness are likely to have more distinct neural representations of different roughness levels in their SMA.

An efficient detection of epileptic seizure by differentiation and spectral analysis of electroencephalograms.

Epilepsy is a critical neurological disorder resulting from abnormal hyper-excitability of neurons in the brain. Studies have shown that epilepsy can be detected in electroencephalography (EEG) recordings of patients suffering from seizures. The performance of EEG-based epileptic seizure detection relies largely on how well one can extract features from an EEG that characterize seizure activity. Conventional feature extraction methods using time-series analysis, spectral analysis and nonlinear dynamic analysis have advanced in recent years to improve detection. The computational complexity has also increased to obtain a higher detection rate. This study aimed to develop an efficient feature extraction method based on Hjorth's mobility to reduce computational complexity while maintaining high detection accuracy. A new feature extraction method was proposed by computing the spectral power of Hjorth's mobility components, which were effectively estimated by differentiating EEG signals in real-time. Using EEG data in five epileptic patients, this method resulted in a detection rate of 99.46% between interictal and epileptic EEG signals and 99.78% between normal and epileptic EEG signals, which is comparable to most advanced nonlinear methods. These results suggest that the spectral features of Hjorth's mobility components in EEG signals can represent seizure activity and may pave the way for developing a fast and reliable epileptic seizure detection method.

Intra- and inter-hemispheric effective connectivity in the human somatosensory cortex during pressure stimulation.

BACKGROUND: Slow-adapting type I (SA-I) afferents deliver sensory signals to the somatosensory cortex during low-frequency (or static) mechanical stimulation. It has been reported that the somatosensory projection from SA-I afferents is effective and reliable for object grasping and manipulation. Despite a large number of neuroimaging studies on cortical activation responding to tactile stimuli mediated by SA-I afferents, how sensory information of such tactile stimuli flows over the somatosensory cortex remains poorly understood. In this study, we investigated tactile information processing of pressure stimuli between the primary (SI) and secondary (SII) somatosensory cortices by measuring effective connectivity using dynamic causal modeling (DCM). We applied pressure stimuli for 3 s to the right index fingertip of healthy participants and acquired functional magnetic resonance imaging (fMRI) data using a 3T MRI system. RESULTS: DCM analysis revealed intra-hemispheric effective connectivity between the contralateral SI (cSI) and SII (cSII) characterized by both parallel (signal inputs to both cSI and cSII) and serial (signal transmission from cSI to cSII) pathways during pressure stimulation. DCM analysis also revealed inter-hemispheric effective connectivity among cSI, cSII, and the ipsilateral SII (iSII) characterized by serial (from cSI to cSII) and SII-level (from cSII to iSII) pathways during pressure stimulation. CONCLUSIONS: Our results support a hierarchical somatosensory network that underlies processing of low-frequency tactile information. The network consists of parallel inputs to both cSI and cSII (intra-hemispheric), followed by serial pathways from cSI to cSII (intra-hemispheric) and from cSII to iSII (inter-hemispheric). Importantly, our results suggest that both serial and parallel processing take place in tactile information processing of static mechanical stimuli as well as highlighting the contribution of callosal transfer to bilateral neuronal interactions in SII.

Development of a simple pressure and heat stimulator for intra- and interdigit functional magnetic resonance imaging.

For this study, we developed a simple pressure and heat stimulator that can quantitatively control pressure and provide heat stimulation to intra- and interdigit areas. The developed stimulator consists of a control unit, drive units, and tactors. The control unit controls the stimulation parameters, such as stimulation types, intensity, time, and channel, and transmits a created signal of stimulation to the drive units. The drive units operate pressure and heat tactors in response to commands from the control unit. The pressure and heat tactors can display various stimulation intensities quantitatively, apply stimulation continuously, and adjust the stimulation areas. Additionally, they can easily be attached to and detached from the digits. The developed pressure and heat stimulator is small in total size, easy to install, and inexpensive to manufacture. The new stimulator operated stably in a magnetic resonance imaging (MRI) environment without affecting the obtained images. A preliminary functional magnetic resonance imaging (fMRI) experiment confirmed that differences in activation of somatosensory areas were induced from the pressure and heat stimulation. The developed pressure and heat stimulator is expected to be utilized for future intra- and interdigit fMRI studies on pressure and heat stimulation.

Distributed functions of detection and discrimination of vibrotactile stimuli in the hierarchical human somatosensory system.

According to the hierarchical view of human somatosensory network, somatic sensory information is relayed from the thalamus to primary somatosensory cortex (S1), and then distributed to adjacent cortical regions to perform further perceptual and cognitive functions. Although a number of neuroimaging studies have examined neuronal activity correlated with tactile stimuli, comparatively less attention has been devoted toward understanding how vibrotactile stimulus information is processed in the hierarchical somatosensory cortical network. To explore the hierarchical perspective of tactile information processing, we studied two cases: (a) discrimination between the locations of finger stimulation; and (b) detection of stimulation against no stimulation on individual fingers, using both standard general linear model (GLM) and searchlight multi-voxel pattern analysis (MVPA) techniques. These two cases were studied on the same data set resulting from a passive vibrotactile stimulation experiment. Our results showed that vibrotactile stimulus locations on fingers could be discriminated from measurements of human functional magnetic resonance imaging (fMRI). In particular, it was in case (a) we observed activity in contralateral posterior parietal cortex (PPC) and supramarginal gyrus (SMG) but not in S1, while in case; (b) we found significant cortical activations in S1 but not in PPC and SMG. These discrepant observations suggest the functional specialization with regard to vibrotactile stimulus locations, especially, the hierarchical information processing in the human somatosensory cortical areas. Our findings moreover support the general understanding that S1 is the main sensory receptive area for the sense of touch, and adjacent cortical regions (i.e., PPC and SMG) are in charge of a higher level of processing and may thus contribute most for the successful classification between stimulated finger locations.

Evaluation of somatosensory cortical differences between flutter and vibration tactile stimuli.

In parallel with advances in haptic-based mobile computing systems, understanding of the neural processing of vibrotactile information becomes of great importance. In the human nervous system, two types of vibrotactile information, flutter and vibration, are delivered from mechanoreceptors to the somatosensory cortex through segregated neural afferents. To investigate how the somatosensory cortex differentiates flutter and vibration, we analyzed the cortical responses to vibrotactile stimuli with a wide range of frequencies. Specifically, we examined whether cortical activity changed most around 50 Hz, which is known as a boundary between flutter and vibration. We explored various measures to evaluate separability of cortical activity across frequency and found that the hypothesis margin method resulted in the greatest separability between flutter and vibration. This result suggests that flutter and vibration information may be processed by different neural processes in the somatosensory cortex.

Frequency-dependent patterns of somatosensory cortical responses to vibrotactile stimulation in humans: a fMRI study.

In the human mechanosensation system, rapidly adapting afferents project sensory signals of flutter (5-50Hz) to the contralateral primary somatosensory cortex (S1) and bilateral secondary somatosensory cortex (S2) whereas Pacinian afferents project sensory signals of vibration (50-400Hz) to bilateral S2. However, it remains largely unknown how somatosensory cortical activity changes as a function of vibrotactile frequency. This functional magnetic resonance imaging (fMRI) study investigated frequency dependency of somatosensory cortical activity in humans by applying vibrotactile stimulation with various frequencies (20-200Hz) to the index finger. We found more frequency-dependent voxels in the upper bank of the lateral sulcus (LS) of S2 than in S1 and the posterior parietal cortex of S2. Our statistical spatial clustering analysis showed that two groups of positively or negatively frequency-dependent voxels formed distinct clusters, most clearly in the LS. Using a cortical separability index, we reaffirmed that somatosensory cortical activity was most separable at 50Hz, previously known to demarcate flutter and vibration. Our results suggest that the LS (S2) may play an important role in processing vibrotactile frequency information and that the somatosensory cortex may include spatially localized neural assemblies specialized to higher or lower vibrotactile frequency.

Development of a simple MR-compatible vibrotactile stimulator using a planar-coil-type actuator.

For this study, we developed a magnetic resonance (MR)-compatible vibrotactile stimulator using a planar-coil-type actuator. The newly developed vibrotactile stimulator consists of three units: control unit, drive unit, and planar-coil-type actuator. The control unit controls frequency, intensity, time, and channel, and transfers the stimulation signals to the drive unit. The drive unit operates the planar-coil-type actuator in response to commands from the control unit. The planar-coil-type actuator, which uses a planar coil instead of conventional electric wire, generates vibrating stimulation through interaction of the current of the planar coil with the static magnetic field of the MR scanner. Even though the developed tactile stimulating system is small, simple, and inexpensive, it has a wide range of stimulation frequencies (20 ~ 400 Hz, at 40 levels) and stimulation intensities (0 ~ 7 V, at 256 levels). The stimulation intensity does not change due to frequency changes. Since the transient response time is a few microseconds, the stimulation time can be controlled on a scale of microseconds. In addition, this actuator has the advantages of providing highly repeatable stimulation, being durable, being able to assume various shapes, and having an adjustable contact area with the skin. The new stimulator operated stably in an MR environment without affecting the MR images. Using functional magnetic resonance imaging, we observed the brain activation changes resulting from stimulation frequency and intensity changes.

Correlation of fronto-central phase coupling with sensorimotor rhythm modulation.

We investigated neurophysiologic correlates of individual differences in the modulation of sensorimotor rhythms (SMRs) in the human electroencephalography (EEG) during motor imagery. The ability of modulating SMRs to different motor imageries was correlated with the strength of alpha phase synchronization across frontal and central sensorimotor areas. The results suggest that fronto-central coupling may elucidate individual variations in SMR modulation that is essential for using SMR-based brain-computer interfaces.