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Seizure detection using intracranial electroencephalography (iEEG) contributes to improved treatment of patients with refractory epilepsy. For that purpose, a feature of iEEG to characterize the ictal state with high specificity and sensitivity is necessary. We evaluated the use of phase-amplitude coupling (PAC) of iEEG signals over a period of 24 h to detect the ictal and interictal states. PAC was estimated by using a synchronisation index (SI) for iEEG signals from seven patients with refractory temporal lobe epilepsy. iEEG signals of the ictal state was characterised by a strong PAC between the phase of β and the amplitude of high γ. Furthermore, using SI values, the ictal state was successfully detected with significantly higher accuracy than by using the amplitude of high γ alone. In conclusion, PAC accurately distinguished the ictal state from the interictal state.

Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes. Although several studies have already succeeded in the inference of computer cursor trajectories and finger flexions using human ECoG signals, precise three-dimensional (3D) trajectory reconstruction for a human limb from ECoG has not yet been achieved. In this study, we predicted 3D arm trajectories in time series from ECoG signals in humans using a novel preprocessing method and a sparse linear regression. Average Pearson's correlation coefficients and normalized root-mean-square errors between predicted and actual trajectories were 0.44~0.73 and 0.18~0.42, respectively, confirming the feasibility of predicting 3D arm trajectories from ECoG. We foresee this method contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

Brain Machine Interface (BMI) is a system that assumes user's intention by analyzing user's brain activities and control devices with the assumed intention. It is considered as one of prospective tools to enhance paralyzed patients' quality of life. In our group, we especially focus on ECoG (electro-corti-gram)-BMI, which requires surgery to place electrodes on the cortex. We try to implant all the devices within the patient's head and abdomen and to transmit the data and power wirelessly. Our device consists of 5 parts: (1) High-density multi-electrodes with a 3D shaped sheet fitting to the individual brain surface to effectively record the ECoG signals; (2) A small circuit board with two integrated circuit chips functioning 128 [ch] analogue amplifiers and A/D converters for ECoG signals; (3) A Wifi data communication & control circuit with the target PC; (4) A non-contact power supply transmitting electrical power minimum 400[mW] to the device 20[mm] away. We developed those devices, integrated them, and, investigated the performance.

Objective: Paralyzed patients may benefit from restoration of movement afforded by prosthetics controlled by electrocorticography (ECoG). Although ECoG shows promising results in human volunteers, it is unclear whether ECoG signals recorded from chronically paralyzed patients provide sufficient motor information, and if they do, whether they can be applied to control a prosthetic., Methods: We recorded ECoG signals from sensorimotor cortices of 12 patients while they executed or attempted to execute 3 to 5 simple hand and elbow movements. Sensorimotor function was severely impaired in 3 patients due to peripheral nervous system lesion or amputation, moderately impaired due to central nervous system lesions sparing the cortex in 4 patients, and normal in 5 patients. Time frequency and decoding analyses were performed with the patients' ECoG signals., Results: In all patients, the high gamma power (80-150 Hz) of the ECoG signals during movements was clearly responsive to movement types and provided the best information for classifying different movement types. The classification performance was significantly better than chance in all patients, although differences between ECoG power modulations during different movement types were significantly less in patients with severely impaired motor function. In the impaired patients, cortical representations tended to overlap each other. Finally, using the classification method in real time, a moderately impaired patient and 3 nonparalyzed patients successfully controlled a prosthetic arm., Interpretation: ECoG signals appear useful for prosthetic arm control and may provide clinically feasible motor restoration for patients with paralysis but no injury of the sensorimotor cortex., (Copyright © 2011 American Neurological Association.)

Object: A brain-machine interface (BMI) offers patients with severe motor disabilities greater independence by controlling external devices such as prosthetic arms. Among the available signal sources for the BMI, electrocorticography (ECoG) provides a clinically feasible signal with long-term stability and low clinical risk. Although ECoG signals have been used to infer arm movements, no study has examined its use to control a prosthetic arm in real time. The authors present an integrated BMI system for the control of a prosthetic hand using ECoG signals in a patient who had suffered a stroke. This system used the power modulations of the ECoG signal that are characteristic during movements of the patient's hand and enabled control of the prosthetic hand with movements that mimicked the patient's hand movements., Methods: A poststroke patient with subdural electrodes placed over his sensorimotor cortex performed 3 types of simple hand movements following a sound cue (calibration period). Time-frequency analysis was performed with the ECoG signals to select 3 frequency bands (1-8, 25-40, and 80-150 Hz) that revealed characteristic power modulation during the movements. Using these selected features, 2 classifiers (decoders) were trained to predict the movement state--that is, whether the patient was moving his hand or not--and the movement type based on a linear support vector machine. The decoding accuracy was compared among the 3 frequency bands to identify the most informative features. With the trained decoders, novel ECoG signals were decoded online while the patient performed the same task without cues (free-run period). According to the results of the real-time decoding, the prosthetic hand mimicked the patient's hand movements., Results: Offline cross-validation analysis of the ECoG data measured during the calibration period revealed that the state and movement type of the patient's hand were predicted with an accuracy of 79.6% (chance 50%) and 68.3% (chance 33.3%), respectively. Using the trained decoders, the onset of the hand movement was detected within 0.37 ± 0.29 seconds of the actual movement. At the detected onset timing, the type of movement was inferred with an accuracy of 69.2%. In the free-run period, the patient's hand movements were faithfully mimicked by the prosthetic hand in real time., Conclusions: The present integrated BMI system successfully decoded the hand movements of a poststroke patient and controlled a prosthetic hand in real time. This success paves the way for the restoration of the patient's motor function using a prosthetic arm controlled by a BMI using ECoG signals.

Electrocorticography of the primary motor cortex (M1) is a promising tool for controlling a brain-computer interface (BCI). Electrocorticograms (ECoG) of the human M1 within the central sulcus (intrasulcal ECoG) have been rarely examined. In order to evaluate the usefulness of intrasulcal ECoG for BCI, we examined patients with subdural electrodes placed temporarily inside the central sulcus and over the sensorimotor cortex (gyral ECoG). Five patients were asked to perform or imagine two or three classes of simple upper limb movements. Univariate statistical analysis of the results revealed that the intrasulcal ECoG on M1 showed significant variability across movement classes. A support vector machine was used for classification of single-trial ECoG signals to infer movement class (neural decoding). The movement classes were predicted with 80-90% accuracy (chance level: 33% or 50%). To reveal the relative importance of anatomical areas for neural decoding, the decoding performance was compared between gyral and intrasulcal ECoGs. The intrasulcal ECoG on the motor bank showed higher performance than the equally-sized gyral ECoG or the intrasulcal ECoG on the sensory bank. Analysis using a short time window revealed that movement class could be decoded even before movement onset. These results suggest the usefulness of intrasulcal ECoG on M1 to infer upper limb movements and present a promising application for a practical BCI system.

Using equivalent current dipole (ECD) analysis of magnetoencephalography (MEG), we examined the locations of interictal spikes for 5 years in an epileptic patient who had experienced colored elementary visual auras that gradually disappeared with medication. During the period that the patient frequently experienced colored elementary visual auras, six of nine spikes occurred in the right fusiform and inferior temporal gyri. As the frequency of the visual auras decreased, the spikes were widely distributed in the right hemisphere, but were not localized to these areas. After the visual auras ceased, six of seven spikes occurred in the right transverse gyrus of Heschl. These results suggest that colored elementary visual auras of epileptic-positive symptoms originate in the fusiform and inferior temporal gyri and that these regions are involved in human color processing. This is the first MEG study suggesting that the fusiform gyrus and the inferior temporal cortex are related to human color processing.

Objective: Swallowing is a unique movement due to the indispensable orchestration of voluntary and involuntary movements. The transition from voluntary to involuntary swallowing is executed within milliseconds. We hypothesized that the underlying neural mechanism of swallowing would be revealed by high‐frequency cortical activities. Methods: Eight epileptic participants fitted with intracranial electrodes over the orofacial cortex were asked to swallow a water bolus and cortical oscillatory changes, including the high γ band (75–150 Hz) and β band (13–30 Hz), were investigated at the time of mouth opening, water injection, and swallowing. Results: Increases in high γ power associated with mouth opening were observed in the ventrolateral prefrontal cortex (VLPFC) with water injection in the lateral central sulcus and with swallowing in the region along the Sylvian fissure. Mouth opening induced a decrease in β power, which continued until the completion of swallowing. The high γ burst of activity was focal and specific to swallowing; however, the β activities were extensive and not specific to swallowing. In the interim between voluntary and involuntary swallowing, swallowing‐related high γ power achieved its peak, and subsequently, the power decreased. Interpretation: We demonstrated three distinct activities related to mouth opening, water injection, and swallowing induced at different timings using high γ activities. The peak of high γ power related to swallowing suggests that during voluntary swallowing phases, the cortex is the main driving force for swallowing as opposed to the brain stem. [ABSTRACT FROM AUTHOR]

Brain–machine interfaces (BMIs) are promising devices that can be used as neuroprostheses by severely disabled individuals. Brain surface electroencephalograms (electrocorticograms, ECoGs) can provide input signals that can then be decoded to enable communication with others and to control intelligent prostheses and home electronics. However, conventional systems use wired ECoG recordings. Therefore, the development of wireless systems for clinical ECoG BMIs is a major goal in the field. We developed a fully implantable ECoG signal recording device for human ECoG BMI, i.e., a wireless human ECoG-based real-time BMI system (W-HERBS). In this system, three-dimensional (3D) high-density subdural multiple electrodes are fitted to the brain surface and ECoG measurement units record 128-channel (ch) ECoG signals at a sampling rate of 1 kHz. The units transfer data to the data and power management unit implanted subcutaneously in the abdomen through a subcutaneous stretchable spiral cable. The data and power management unit then communicates with a workstation outside the body and wirelessly receives 400 mW of power from an external wireless transmitter. The workstation records and analyzes the received data in the frequency domain and controls external devices based on analyses. We investigated the performance of the proposed system. We were able to use W-HERBS to detect sine waves with a 4.8-μV amplitude and a 60–200-Hz bandwidth from the ECoG BMIs. W-HERBS is the first fully implantable ECoG-based BMI system with more than 100 ch. It is capable of recording 128-ch subdural ECoG signals with sufficient input-referred noise (3 μVrms) and with an acceptable time delay (250 ms). The system contributes to the clinical application of high-performance BMIs and to experimental brain research. [ABSTRACT FROM AUTHOR]

Background: ‘Shiritori’ (capping verses) is a traditional Japanese word generation game, and is very familiar to native Japanese speakers. The shiritori task is expected to more strongly activate temporal language-related regions than conventional word generation to letters because of its characteristic way to make cue letters. Objectives: The aim of this study was to examine the cortical oscillatory changes in basic brain rhythms during silently performing a shiritori task. Methods: Using synthetic aperture magnetometry (SAM) analysis of magnetoencephalography, we estimated the tomographic distributions of the statistically significant differences of the power in the alpha and beta frequency bands between the resting and the task periods. Results: Significant event-related desynchronization (ERD) in the 8- to 25-Hz band, thought to reflect neural activation, was localized within task-related cortical regions with left-side dominance. The significant ERDs were estimated in both the frontal and temporal language-related regions encompassing Broca’s and Wernicke’s areas, although previous neuroimaging studies using word generation to letters showed neural activation predominantly in frontal regions. Conclusions: Our results show the potential of SAM analysis for reliable brain mapping of language processing, and suggest that the shiritori task might be more suitable for examining the language-related network in the brain than conventional word generation to letters. Copyright © 2006 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Abstract: In patients with Alzheimer''s disease (AD), it is sometimes challenging to identify typical findings in electroencephalography (EEG) or magnetoencephalography (MEG) such as a slowing of the posterior dominant activity or an increase in slow activity. In this MEG study, we evaluated the event-related synchronization (ERS) of alpha activity after eye closing in patients with early AD and mild cognitive impairment (MCI) who presented no slow MEG pattern. Thirteen patients with probable AD and thirteen patients with MCI, who met NINCDS-ADRDA and Petersen''s diagnostic criteria, respectively, were enrolled. We also selected fourteen age-matched normal control subjects. MEG activity was acquired during eye-open and eye-closed states. The ERS after eye closing within 8–15Hz frequency band was calculated and its cortical source was superimposed on the individual''s MRI by using the beamformer implemented in Brain Electrical Source Analysis (BESA). The Source image was converted into a standardized image, and group comparisons across patients with AD, MCI and controls were performed using BrainVoyager QX. The averaged ERS was observed dominantly in posterior regions in all three groups. Significant difference in ERS was observed only for the comparison between AD patients and controls, with AD patients showing increased ERS in frontal regions. Frontal shift of posterior alpha activity was observed clearly in AD patients using the combination of beamformer and group comparison. [Copyright &y& Elsevier]

Abstract: Abnormal focal slow wave activity on electroencephalography and magnetoencephalography (MEG) is often seen in patients with various brain pathologies and MEG is capable of localizing cortical oscillatory activity with enhanced accuracy. In addition, MEG with synthetic aperture magnetometry (SAM) can depict changes in cortical oscillatory activity tomographically. Using SAM, we recorded cortical rhythms in patients with a brain tumor and evaluated the tomographic appearance of focal slow wave activity in relation to clinical signs and symptoms. Spontaneous MEG recordings were obtained in 15 patients with brain tumors. Statistically-determined power distributions in the delta-, theta-, and alpha-frequency bands were displayed tomographically and overlaid on individual magnetic resonance images. The location, strength and volume of enhanced activity were analyzed. Delta and theta band activities were significantly more intense in the cortex adjacent to tumors and in the surrounding edematous cortical areas than in other portions of the cortex. In 13 of the 15 patients, spatial distribution of enhanced focal delta activity coincided with the area responsible for the presenting signs and symptoms. Volumetric analysis revealed that emergence of tumor-related focal delta band activity in the cortex adjacent to a tumor, or with peritumoral edema, was greater for intra-axial tumors involving subcortical fibers than for extra-axial tumors. Patients with an increased volume of enhanced delta activity exhibited poor recovery of function in the early postoperative period. It is concluded that SAM imaging of focal delta activity can reveal functional alterations in cortical activity in patients with brain tumors and is useful for assessing cortical states associated with the existing pathology. [Copyright &y& Elsevier]