An Electrocorticographic Brain Interface in an Individual with Tetraplegia

Brain-computer interface (BCI) technology aims to help individuals with disability to control assistive devices and reanimate paralyzed limbs. Our study investigated the feasibility of an electrocorticography (ECoG)-based BCI system in an individual with tetraplegia caused by C4 level spinal cord injury. ECoG signals were recorded with a high-density 32-electrode grid over the hand and arm area of the left sensorimotor cortex. The participant was able to voluntarily activate his sensorimotor cortex using attempted movements, with distinct cortical activity patterns for different segments of the upper limb. Using only brain activity, the participant achieved robust control of 3D cursor movement. The ECoG grid was explanted 28 days post-implantation with no adverse effect. This study demonstrates that ECoG signals recorded from the sensorimotor cortex can be used for real-time device control in paralyzed individuals.     

Restoration of whole body movement: toward a noninvasive brain-machine interface system

This article highlights recent advances in the design of noninvasive neural interfaces based on the scalp electroencephalogram (EEG). The simplest of physical tasks, such as turning the page to read this article, requires an intense burst of brain activity. It happens in milliseconds and requires little conscious thought. But for amputees and stroke victims with diminished motor-sensory skills, this process can be difficult or impossible. Our team at the University of Maryland, in conjunction with the Johns Hopkins Applied Physics Laboratory (APL) and the University of Maryland School of Medicine, hopes to offer these people newfound mobility and dexterity. In separate research thrusts, were using data gleaned from scalp EEG to develop reliable brainmachine interface (BMI) systems that could soon control modern devices such as prosthetic limbs or powered robotic exoskeletons.     

Coarse Electrocorticographic Decoding of Ipsilateral Reach in Patients with Brain Lesions

In patients with unilateral upper limb paralysis from strokes and other brain lesions, strategies for functional recovery may eventually include brain-machine interfaces (BMIs) using control signals from residual sensorimotor systems in the damaged hemisphere. When voluntary movements of the contralateral limb are not possible due to brain pathology, initial training of such a BMI may require use of the unaffected ipsilateral limb. We conducted an offline investigation of the feasibility of decoding ipsilateral upper limb movements from electrocorticographic (ECoG) recordings in three patients with different lesions of sensorimotor systems associated with upper limb control. We found that the first principal component (PC) of unconstrained, naturalistic reaching movements of the upper limb could be decoded from ipsilateral ECoG using a linear model. ECoG signal features yielding the best decoding accuracy were different across subjects. Performance saturated with very few input features. Decoding performances of 0.77, 0.73, and 0.66 (median Pearson''s r between the predicted and actual first PC of movement using nine signal features) were achieved in the three subjects. The performance achieved here with small numbers of electrodes and computationally simple decoding algorithms suggests that it may be possible to control a BMI using ECoG recorded from damaged sensorimotor brain systems.     

Prediction of Three-Dimensional Arm Trajectories Based on ECoG Signals Recorded from Human Sensorimotor Cortex

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.     

Decoding Individual Finger Movements from One Hand Using Human EEG Signals

Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.     

EEGgui: a program used to detect electroencephalogram anomalies after traumatic brain injury

Background

Identifying and quantifying pathological changes in brain electrical activity is important for investigations of brain injury and neurological disease. An example is the development of epilepsy, a secondary consequence of traumatic brain injury. While certain epileptiform events can be identified visually from electroencephalographic (EEG) or electrocorticographic (ECoG) records, quantification of these pathological events has proved to be more difficult. In this study we developed MATLAB-based software that would assist detection of pathological brain electrical activity following traumatic brain injury (TBI) and present our MATLAB code used for the analysis of the ECoG.

Methods

Software was developed using MATLAB(?) and features of the open access EEGLAB. EEGgui is a graphical user interface in the MATLAB programming platform that allows scientists who are not proficient in computer programming to perform a number of elaborate analyses on ECoG signals. The different analyses include Power Spectral Density (PSD), Short Time Fourier analysis and Spectral Entropy (SE). ECoG records used for demonstration of this software were derived from rats that had undergone traumatic brain injury one year earlier.

Results

The software provided in this report provides a graphical user interface for displaying ECoG activity and calculating normalized power density using fast fourier transform of the major brain wave frequencies (Delta, Theta, Alpha, Beta1, Beta2 and Gamma). The software further detects events in which power density for these frequency bands exceeds normal ECoG by more than 4 standard deviations. We found that epileptic events could be identified and distinguished from a variety of ECoG phenomena associated with normal changes in behavior. We further found that analysis of spectral entropy was less effective in distinguishing epileptic from normal changes in ECoG activity.

Conclusion

The software presented here was a successful modification of EEGLAB in the Matlab environment that allows detection of epileptiform ECoG signals in animals after TBI. The code allows import of large EEG or ECoG data records as standard text files and uses fast fourier transform as a basis for detection of abnormal events. The software can also be used to monitor injury-induced changes in spectral entropy if required. We hope that the software will be useful for other investigators in the field of traumatic brain injury and will stimulate future advances of quantitative analysis of brain electrical activity after neurological injury or disease.

     

Prediction of Hand Trajectory from Electrocorticography Signals in Primary Motor Cortex

Due to their potential as a control modality in brain-machine interfaces, electrocorticography (ECoG) has received much focus in recent years. Studies using ECoG have come out with success in such endeavors as classification of arm movements and natural grasp types, regression of arm trajectories in two and three dimensions, estimation of muscle activity time series and so on. However, there still remains considerable work to be done before a high performance ECoG-based neural prosthetic can be realized. In this study, we proposed an algorithm to decode hand trajectory from 15 and 32 channel ECoG signals recorded from primary motor cortex (M1) in two primates. To determine the most effective areas for prediction, we applied two electrode selection methods, one based on position relative to the central sulcus (CS) and another based on the electrodes'' individual prediction performance. The best coefficients of determination for decoding hand trajectory in the two monkeys were 0.4815±0.0167 and 0.7780±0.0164. Performance results from individual ECoG electrodes showed that those with higher performance were concentrated at the lateral areas and areas close to the CS. The results of prediction according with different numbers of electrodes based on proposed methods were also shown and discussed. These results also suggest that superior decoding performance can be achieved from a group of effective ECoG signals rather than an entire ECoG array.     

Resolving the dynamics of EEG generators by multichannel recordings

The voltage recorded over the cortex (ECoG) or over the scalp (EEG) is generated by currents derived from many sources called “generators”. Different patterns and amplitudes are observed in aroused, sleepy, epileptic or other brain states. Differences in amplitude are generally attributed to differences in synchrony among generators. The degree of EEG synchrony is measured by the correlation between electrodes placed over different cortical regions. We present a new way to quantitatively assess the degree of synchronization of these generators via multichannel recordings. We illustrate how situations where there are several groups of generators with different inter-group and intra-group synchronies can be analyzed. Finally, we present a way to identify the organization of groups exhibiting topographic organization. Although the model presented here is highly simplified, several methods are based on averaging activity over increasingly larger areas. These types of measurements may be applied as well to EEG and ECoG recordings.     

A review on directional information in neural signals for brain-machine interfaces

Brain-machine interfaces (BMIs) can be characterized by the technique used to measure brain activity and by the way different brain signals are translated into commands that control an effector. We give an overview of different approaches and focus on a particular BMI approach: the movement of an artificial effector (e.g. arm prosthesis to the right) by those motor cortical signals that control the equivalent movement of a corresponding body part (e.g. arm movement to the right). This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single-units. Here, we review recent findings showing that analog neuronal population signals, ranging from intracortical local field potentials over epicortical ECoG to non-invasive EEG and MEG, can also be used to decode movement direction and continuous movement trajectories. Therefore, these signals might provide additional or alternative control for this BMI approach, with possible advantages due to reduced invasiveness.     

Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), magnetoencephalography or functional magnetic-resonance imaging. These techniques have either inherently low temporal or low spatial resolution, and suffer from low signal-to-noise ratio and/or poor high-frequency sensitivity. Thus, they are suboptimal for exploring the short-lived spatio-temporal dynamics of many of the underlying brain processes. In contrast, the invasive technique of electrocorticography (ECoG) provides brain signals that have an exceptionally high signal-to-noise ratio, less susceptibility to artifacts than EEG, and a high spatial and temporal resolution (i.e., <1 cm/<1 millisecond, respectively). ECoG involves measurement of electrical brain signals using electrodes that are implanted subdurally on the surface of the brain. Recent studies have shown that ECoG amplitudes in certain frequency bands carry substantial information about task-related activity, such as motor execution and planning¹, auditory processing² and visual-spatial attention³. Most of this information is captured in the high gamma range (around 70-110 Hz). Thus, gamma activity has been proposed as a robust and general indicator of local cortical function^1-5. ECoG can also reveal functional connectivity and resolve finer task-related spatial-temporal dynamics, thereby advancing our understanding of large-scale cortical processes. It has especially proven useful for advancing brain-computer interfacing (BCI) technology for decoding a user''s intentions to enhance or improve communication⁶ and control⁷. Nevertheless, human ECoG data are often hard to obtain because of the risks and limitations of the invasive procedures involved, and the need to record within the constraints of clinical settings. Still, clinical monitoring to localize epileptic foci offers a unique and valuable opportunity to collect human ECoG data. We describe our methods for collecting recording ECoG, and demonstrate how to use these signals for important real-time applications such as clinical mapping and brain-computer interfacing. Our example uses the BCI2000 software platform^8,9 and the SIGFRIED¹⁰ method, an application for real-time mapping of brain functions. This procedure yields information that clinicians can subsequently use to guide the complex and laborious process of functional mapping by electrical stimulation.

Prerequisites and Planning:

Patients with drug-resistant partial epilepsy may be candidates for resective surgery of an epileptic focus to minimize the frequency of seizures. Prior to resection, the patients undergo monitoring using subdural electrodes for two purposes: first, to localize the epileptic focus, and second, to identify nearby critical brain areas (i.e., eloquent cortex) where resection could result in long-term functional deficits. To implant electrodes, a craniotomy is performed to open the skull. Then, electrode grids and/or strips are placed on the cortex, usually beneath the dura. A typical grid has a set of 8 x 8 platinum-iridium electrodes of 4 mm diameter (2.3 mm exposed surface) embedded in silicon with an inter-electrode distance of 1cm. A strip typically contains 4 or 6 such electrodes in a single line. The locations for these grids/strips are planned by a team of neurologists and neurosurgeons, and are based on previous EEG monitoring, on a structural MRI of the patient''s brain, and on relevant factors of the patient''s history. Continuous recording over a period of 5-12 days serves to localize epileptic foci, and electrical stimulation via the implanted electrodes allows clinicians to map eloquent cortex. At the end of the monitoring period, explantation of the electrodes and therapeutic resection are performed together in one procedure.In addition to its primary clinical purpose, invasive monitoring also provides a unique opportunity to acquire human ECoG data for neuroscientific research. The decision to include a prospective patient in the research is based on the planned location of their electrodes, on the patient''s performance scores on neuropsychological assessments, and on their informed consent, which is predicated on their understanding that participation in research is optional and is not related to their treatment. As with all research involving human subjects, the research protocol must be approved by the hospital''s institutional review board. The decision to perform individual experimental tasks is made day-by-day, and is contingent on the patient''s endurance and willingness to participate. Some or all of the experiments may be prevented by problems with the clinical state of the patient, such as post-operative facial swelling, temporary aphasia, frequent seizures, post-ictal fatigue and confusion, and more general pain or discomfort.At the Epilepsy Monitoring Unit at Albany Medical Center in Albany, New York, clinical monitoring is implemented around the clock using a 192-channel Nihon-Kohden Neurofax monitoring system. Research recordings are made in collaboration with the Wadsworth Center of the New York State Department of Health in Albany. Signals from the ECoG electrodes are fed simultaneously to the research and the clinical systems via splitter connectors. To ensure that the clinical and research systems do not interfere with each other, the two systems typically use separate grounds. In fact, an epidural strip of electrodes is sometimes implanted to provide a ground for the clinical system. Whether research or clinical recording system, the grounding electrode is chosen to be distant from the predicted epileptic focus and from cortical areas of interest for the research. Our research system consists of eight synchronized 16-channel g.USBamp amplifier/digitizer units (g.tec, Graz, Austria). These were chosen because they are safety-rated and FDA-approved for invasive recordings, they have a very low noise-floor in the high-frequency range in which the signals of interest are found, and they come with an SDK that allows them to be integrated with custom-written research software. In order to capture the high-gamma signal accurately, we acquire signals at 1200Hz sampling rate-considerably higher than that of the typical EEG experiment or that of many clinical monitoring systems. A built-in low-pass filter automatically prevents aliasing of signals higher than the digitizer can capture. The patient''s eye gaze is tracked using a monitor with a built-in Tobii T-60 eye-tracking system (Tobii Tech., Stockholm, Sweden). Additional accessories such as joystick, bluetooth Wiimote (Nintendo Co.), data-glove (5^th Dimension Technologies), keyboard, microphone, headphones, or video camera are connected depending on the requirements of the particular experiment.Data collection, stimulus presentation, synchronization with the different input/output accessories, and real-time analysis and visualization are accomplished using our BCI2000 software^8,9. BCI2000 is a freely available general-purpose software system for real-time biosignal data acquisition, processing and feedback. It includes an array of pre-built modules that can be flexibly configured for many different purposes, and that can be extended by researchers'' own code in C++, MATLAB or Python. BCI2000 consists of four modules that communicate with each other via a network-capable protocol: a Source module that handles the acquisition of brain signals from one of 19 different hardware systems from different manufacturers; a Signal Processing module that extracts relevant ECoG features and translates them into output signals; an Application module that delivers stimuli and feedback to the subject; and the Operator module that provides a graphical interface to the investigator.A number of different experiments may be conducted with any given patient. The priority of experiments will be determined by the location of the particular patient''s electrodes. However, we usually begin our experimentation using the SIGFRIED (SIGnal modeling For Realtime Identification and Event Detection) mapping method, which detects and displays significant task-related activity in real time. The resulting functional map allows us to further tailor subsequent experimental protocols and may also prove as a useful starting point for traditional mapping by electrocortical stimulation (ECS).Although ECS mapping remains the gold standard for predicting the clinical outcome of resection, the process of ECS mapping is time consuming and also has other problems, such as after-discharges or seizures. Thus, a passive functional mapping technique may prove valuable in providing an initial estimate of the locus of eloquent cortex, which may then be confirmed and refined by ECS. The results from our passive SIGFRIED mapping technique have been shown to exhibit substantial concurrence with the results derived using ECS mapping¹⁰.The protocol described in this paper establishes a general methodology for gathering human ECoG data, before proceeding to illustrate how experiments can be initiated using the BCI2000 software platform. Finally, as a specific example, we describe how to perform passive functional mapping using the BCI2000-based SIGFRIED system.     

David Hubel and Torsten Wiesel

While attending medical school at McGill, David Hubel developed an interest in the nervous system during the summers he spent at the Montreal Neurological Institute. After heading to the United States in 1954 for a Neurology year at Johns Hopkins, he was drafted by the army and was assigned to the Neuropsychiatry Division at the Walter Reed Hospital, where he began his career in research and did his first recordings from the visual cortex of sleeping and awake cats. In 1958, he moved to the lab of Stephen Kuffler at Johns Hopkins, where he began a long and fruitful collaboration with Torsten Wiesel. Born in Sweden, Torsten Wiesel began his scientific career at the Karolinska Institute, where he received his medical degree in 1954. After spending a year in Carl Gustaf Bernhard's laboratory doing basic neurophysiological research, he moved to the United States to be a postdoctoral fellow with Stephen Kuffler. It was at Johns Hopkins where he met David Hubel in 1958, and they began working together on exploring the receptive field properties of neurons in the visual cortex. Their collaboration continued until the late seventies. Hubel and Wiesel's work provided fundamental insight into information processing in the visual system and laid the foundation for the field of visual neuroscience. They have had many achievements, including--but not limited to--the discovery of orientation selectivity in visual cortex neurons and the characterization of the columnar organization of visual cortex through their discovery of orientation columns and ocular-dominance columns. Their work earned them the Nobel Prize for Physiology or Medicine in 1981, which they shared with Roger Sperry.     

Definitions of state variables and state space for brain–computer interface

The hypothesis is proposed that the central dynamics of the action–perception cycle has five steps: emergence from an existing macroscopic brain state of a pattern that predicts a future goal state; selection of a mesoscopic frame for action control; execution of a limb trajectory by microscopic spike activity; modification of microscopic cortical spike activity by sensory inputs; construction of mesoscopic perceptual patterns; and integration of a new macroscopic brain state. The basis is the circular causality between microscopic entities (neurons) and the mesoscopic and macroscopic entities (populations) self-organized by axosynaptic interactions. Self-organization of neural activity is bidirectional in all cortices. Upwardly the organization of mesoscopic percepts from microscopic spike input predominates in primary sensory areas. Downwardly the organization of spike outputs that direct specific limb movements is by mesoscopic fields constituting plans to achieve predicted goals. The mesoscopic fields in sensory and motor cortices emerge as frames within macroscopic activity. Part 1 describes the action–perception cycle and its derivative reflex arc qualitatively. Part 2 describes the perceptual limb of the arc from microscopic MSA to mesoscopic wave packets, and from these to macroscopic EEG and global ECoG fields that express experience-dependent knowledge in successive states. These macroscopic states are conceived to embed and control mesoscopic frames in premotor and motor cortices that are observed in local ECoG and LFP of frontoparietal areas. The fields sampled by ECoG and LFP are conceived as local patterns of neural activity in which trajectories of multiple spike activities (MSA) emerge that control limb movements. Mesoscopic frames are located by use of the analytic signal from the Hilbert transform after band pass filtering. The state variables in frames are measured to construct feature vectors by which to describe and classify frame patterns. Evidence is cited to justify use of linear analysis. The aim of the review is to enable researchers to conceive and identify goal-oriented states in brain activity for use as commands, in order to relegate the details of execution to adaptive control devices outside the brain. http://sulcus.berkeley.edu     

Assessing Movement Factors in Upper Limb Kinematics Decoding from EEG Signals

The past decades have seen the rapid development of upper limb kinematics decoding techniques by performing intracortical recordings of brain signals. However, the use of non-invasive approaches to perform similar decoding procedures is still in its early stages. Recent studies show that there is a correlation between electroencephalographic (EEG) signals and hand-reaching kinematic parameters. From these studies, it could be concluded that the accuracy of upper limb kinematics decoding depends, at least partially, on the characteristics of the performed movement. In this paper, we have studied upper limb movements with different speeds and trajectories in a controlled environment to analyze the influence of movement variability in the decoding performance. To that end, low frequency components of the EEG signals have been decoded with linear models to obtain the position of the volunteer’s hand during performed trajectories grasping the end effector of a planar manipulandum. The results confirm that it is possible to obtain kinematic information from low frequency EEG signals and show that decoding performance is significantly influenced by movement variability and tracking accuracy as continuous and slower movements improve the accuracy of the decoder. This is a key factor that should be taken into account in future experimental designs.     

Frequency component selection for an ECoG-based brain-computer interface.

The aim of the present study was to investigate the most significant frequency components in electrocorticogram (ECoG) recordings in order to operate a brain computer interface (BCI). For this purpose the time-frequency ERD/ERS map and the distinction sensitive learning vector quantization (DSLVQ) are applied to ECoG from three subjects, recorded during a self-paced finger movement. The results show that the ERD/ERS pattern found in ECoG generally matches the ERD/ERS pattern found in EEG recordings, but has an increased prevalence of frequency components in the beta range.     

Spectral and coherence EEG analysis of rats differing in the level of seizure readiness

The resting EEGs of several brain structures (motor and visual cortex, caudate nucleus and intralaminar thalamic nuclei) were submitted to spectral and coherence computer analyses in two rat strains. Genetically predisposed to convulsive state KM rats were shown to differ from nonpredisposed Wistar rats in EEG spectral properties. KM rats EEG pattern was characterized by increase of low frequencies (1-2 Hz) power and decrease of faster activity (5-12 Hz) power in cortical spectrograms as well as by decrease of caudate nucleus EEG absolute power. The coherence value between cortical or subcortical structures at below 4 Hz was intensified in KM rats. Reinforcement of cortical auto-oscillating properties manifested by ECoG synchronization in cortical-thalamic resonance interaction as well as weakening of striatal inhibitory system may constitute neurophysiological mechanisms of enhanced convulsive readiness. The probable role of mediator imbalance in these mechanisms is discussed.     

Localization of brain electrical activity sources and hemodynamic activity foci during motor imagery

The sources of brain activity that make the maximum contribution to EEG patterns corresponding to motor imagery have been studied. The accuracy of their classification determines the efficiency of brain-computer interface (BCI) for controlling external technical devices directly by brain signals, without the involvement of muscle activity. Brain activity sources are identified by independent component analysis. The independent components providing the maximum BCI classification accuracy are considered relevant for the motor imagery task. The two most relevant sources exhibit clearly marked event-related desynchronization and synchronization of the μ-rhythm during the imagery of contra- and ipsilateral hand movements. These sources were localized by solving the inverse EEG problem with due consideration for individual geometry of the brain and its covers, as determined by magnetic resonance imaging. Each of the sources was shown to be localized in the 3a area of the primary somatosensory cortex corresponding to proprioceptive sensitivity of the contralateral hand. Their positions were close to the foci of BOLD activity obtained by fMRI.     

EEG Classification of Different Imaginary Movements within the Same Limb

The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises.     

A Direct Brain-to-Brain Interface in Humans

We describe the first direct brain-to-brain interface in humans and present results from experiments involving six different subjects. Our non-invasive interface, demonstrated originally in August 2013, combines electroencephalography (EEG) for recording brain signals with transcranial magnetic stimulation (TMS) for delivering information to the brain. We illustrate our method using a visuomotor task in which two humans must cooperate through direct brain-to-brain communication to achieve a desired goal in a computer game. The brain-to-brain interface detects motor imagery in EEG signals recorded from one subject (the “sender”) and transmits this information over the internet to the motor cortex region of a second subject (the “receiver”). This allows the sender to cause a desired motor response in the receiver (a press on a touchpad) via TMS. We quantify the performance of the brain-to-brain interface in terms of the amount of information transmitted as well as the accuracies attained in (1) decoding the sender’s signals, (2) generating a motor response from the receiver upon stimulation, and (3) achieving the overall goal in the cooperative visuomotor task. Our results provide evidence for a rudimentary form of direct information transmission from one human brain to another using non-invasive means.     

Graph Theoretical Analysis of BOLD Functional Connectivity during Human Sleep without EEG Monitoring

Background

Functional brain networks of human have been revealed to have small-world properties by both analyzing electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) time series.

Methods & Results

In our study, by using graph theoretical analysis, we attempted to investigate the changes of paralimbic-limbic cortex between wake and sleep states. Ten healthy young people were recruited to our experiment. Data from 2 subjects were excluded for the reason that they had not fallen asleep during the experiment. For each subject, blood oxygen level dependency (BOLD) images were acquired to analyze brain network, and peripheral pulse signals were obtained continuously to identify if the subject was in sleep periods. Results of fMRI showed that brain networks exhibited stronger small-world characteristics during sleep state as compared to wake state, which was in consistent with previous studies using EEG synchronization. Moreover, we observed that compared with wake state, paralimbic-limbic cortex had less connectivity with neocortical system and centrencephalic structure in sleep.

Conclusions

In conclusion, this is the first study, to our knowledge, has observed that small-world properties of brain functional networks altered when human sleeps without EEG synchronization. Moreover, we speculate that paralimbic-limbic cortex organization owns an efficient defense mechanism responsible for suppressing the external environment interference when humans sleep, which is consistent with the hypothesis that the paralimbic-limbic cortex may be functionally disconnected from brain regions which directly mediate their interactions with the external environment. Our findings also provide a reasonable explanation why stable sleep exhibits homeostasis which is far less susceptible to outside world.     

Dynamic construction of stimulus values in the ventromedial prefrontal cortex

Signals representing the value assigned to stimuli at the time of choice have been repeatedly observed in ventromedial prefrontal cortex (vmPFC). Yet it remains unknown how these value representations are computed from sensory and memory representations in more posterior brain regions. We used electroencephalography (EEG) while subjects evaluated appetitive and aversive food items to study how event-related responses modulated by stimulus value evolve over time. We found that value-related activity shifted from posterior to anterior, and from parietal to central to frontal sensors, across three major time windows after stimulus onset: 150-250 ms, 400-550 ms, and 700-800 ms. Exploratory localization of the EEG signal revealed a shifting network of activity moving from sensory and memory structures to areas associated with value coding, with stimulus value activity localized to vmPFC only from 400 ms onwards. Consistent with these results, functional connectivity analyses also showed a causal flow of information from temporal cortex to vmPFC. Thus, although value signals are present as early as 150 ms after stimulus onset, the value signals in vmPFC appear relatively late in the choice process, and seem to reflect the integration of incoming information from sensory and memory related regions.