Passive functional mapping of receptive language areas using electrocorticographic signals.

OBJECTIVE: To validate the use of passive functional mapping using electrocorticographic (ECoG) broadband gamma signals for identifying receptive language cortex. METHODS: We mapped language function in 23 patients using ECoG and using electrical cortical stimulation (ECS) in a subset of 15 subjects. RESULTS: The qualitative comparison between cortical sites identified by ECoG and ECS show a high concordance. A quantitative comparison indicates a high level of sensitivity (95%) and a lower level of specificity (59%). Detailed analysis reveals that 82% of all cortical sites identified by ECoG were within one contact of a site identified by ECS. CONCLUSIONS: These results show that passive functional mapping reliably localizes receptive language areas, and that there is a substantial concordance between the ECoG- and ECS-based methods. They also point to a more refined understanding of the differences between ECoG- and ECS-based mappings. This refined understanding helps to clarify the instances in which the two methods disagree and can explain why neurosurgical practice has established the concept of a "safety margin." SIGNIFICANCE: Passive functional mapping using ECoG signals provides a fast, robust, and reliable method for identifying receptive language areas without many of the risks and limitations associated with ECS.

Real-time detection and discrimination of visual perception using electrocorticographic signals.

OBJECTIVE: Several neuroimaging studies have demonstrated that the ventral temporal cortex contains specialized regions that process visual stimuli. This study investigated the spatial and temporal dynamics of electrocorticographic (ECoG) responses to different types and colors of visual stimulation that were presented to four human participants, and demonstrated a real-time decoder that detects and discriminates responses to untrained natural images. APPROACH: ECoG signals from the participants were recorded while they were shown colored and greyscale versions of seven types of visual stimuli (images of faces, objects, bodies, line drawings, digits, and kanji and hiragana characters), resulting in 14 classes for discrimination (experiment I). Additionally, a real-time system asynchronously classified ECoG responses to faces, kanji and black screens presented via a monitor (experiment II), or to natural scenes (i.e. the face of an experimenter, natural images of faces and kanji, and a mirror) (experiment III). Outcome measures in all experiments included the discrimination performance across types based on broadband γ activity. MAIN RESULTS: Experiment I demonstrated an offline classification accuracy of 72.9% when discriminating among the seven types (without color separation). Further discrimination of grey versus colored images reached an accuracy of 67.1%. Discriminating all colors and types (14 classes) yielded an accuracy of 52.1%. In experiment II and III, the real-time decoder correctly detected 73.7% responses to face, kanji and black computer stimuli and 74.8% responses to presented natural scenes. SIGNIFICANCE: Seven different types and their color information (either grey or color) could be detected and discriminated using broadband γ activity. Discrimination performance maximized for combined spatial-temporal information. The discrimination of stimulus color information provided the first ECoG-based evidence for color-related population-level cortical broadband γ responses in humans. Stimulus categories can be detected by their ECoG responses in real time within 500 ms with respect to stimulus onset.

Passive functional mapping guides electrical cortical stimulation for efficient determination of eloquent cortex in epilepsy patients.

Electrical cortical stimulation (ECS) is often used in presurgical evaluation procedures for patients suffering from pharmacoresistant epilepsy. Real-time functional mapping (RTFM) is an alternative brain mapping methodology that can accompany traditional functional mapping approaches like ECS. In this paper, we present a combined RTFM/ECS system that aims to exploit the common ground and the advantages of the two procedures for improved time/effort effectiveness, patients' experience and safety. Using the RTFM and ECS data from four patients who suffer epilepsy, we demonstrate that the RTFM-guided ECS procedure hypothetically reduces the number of electrical stimulations necessary for eloquent cortex detection by 40%.

Heterogeneous spatial representation by different subpopulations of neurons in the subiculum.

The subiculum is a pivotal structure located in the hippocampal formation that receives inputs from grid and place cells and that mediates the output from the hippocampus to cortical and sub-cortical areas. Previous studies have demonstrated the existence of boundary vector cells (BVC) in the subiculum, as well as exceptional stability during recordings conducted in the dark, suggesting that the subiculum is involved in the coding of allocentric cues and also in path integration. In order to better understand the role of the subiculum in spatial processing and the coding of external cues, we recorded subicular units in freely moving rats while performing two experiments: the "size experiment" in which we modified the arena size, and the "barrier experiment" in which we inserted new barriers in a familiar open field thus dividing the enclosure into four comparable sub-chambers. We hypothesized that if physical boundaries were deterministic of the firing of subicular units a strong spatial replication pattern would be found in most spatially modulated units. In contrast, our results demonstrate heterogeneous space coding by different cell types: place cells, barrier-related units and BVC. We also found units characterized by narrow spike waveforms, most likely belonging to axonal recordings, that showed grid-like patterns. Our data indicate that the subiculum codes space in a flexible manner, and that it is involved in the processing of allocentric information, external cues and path integration, thus broadly supporting spatial navigation.

recoveriX: a new BCI-based technology for persons with stroke.

Brain-computer interface (BCI) systems have been used primarily to provide communication for persons with severe movement disabilities. This paper presents a new system that extends BCI technology to a new patient group: persons diagnosed with stroke. This system, called recoveriX, is designed to detect changes in motor imagery in real-time to help monitor compliance and provide closed-loop feedback during therapy. We describe recoveriX and present initial results from one patient.

Distinction of individual finger responses in somatosensory cortex using ECoG high-gamma activation mapping.

This study demonstrates the feasibility of high-gamma activity mapping for localization of somatosensory finger areas in the human brain. Identification of functional brain regions is important in surgical planning, such as for resections of epileptic foci or brain tumors. The mapping procedure is done using electrocorticography (ECoG), an invasive technique in which electrical brain signals are acquired from the cortical surface. Two epilepsy patients with implanted electrode grids participated in the study. Data were collected during a vibrotactile finger stimulation paradigm and showed significant cortical activation (p <; 0.001) in the high-gamma range over the contralateral somatosensory cortex. The results are consistent with previous studies that used fMRI in test subjects without implanted electrodes. Therefore, the results suggest that localizing the cortical representations of the fingers in clinical practice using ECoG is feasible, even without the patient's active participation.

Online control of a humanoid robot through hand movement imagination using CSP and ECoG based features.

Intention recognition through decoding brain activity could lead to a powerful and independent Brain-Computer-Interface (BCI) allowing for intuitive control of devices like robots. A common strategy for realizing such a system is the motor imagery (MI) BCI using electroencephalography (EEG). Changing to invasive recordings like electrocorticography (ECoG) allows extracting very robust features and easy introduction of an idle state, which might simplify the mental task and allow the subject to focus on the environment. Especially for multi-channel recordings like ECoG, common spatial patterns (CSP) provide a powerful tool for feature optimization and dimensionality reduction. This work focuses on an invasive and independent MI BCI that allows triggering from an idle state, and therefore facilitates tele-operation of a humanoid robot. The task was to lift a can with the robot's hand. One subject participated and reached 95.4 % mean online accuracy after six runs of 40 trials. To our knowledge, this is the first online experiment with a MI BCI using CSPs from ECoG signals.

Single trial detection of hand poses in human ECoG using CSP based feature extraction.

Decoding brain activity of corresponding highlevel tasks may lead to an independent and intuitively controlled Brain-Computer Interface (BCI). Most of today's BCI research focuses on analyzing the electroencephalogram (EEG) which provides only limited spatial and temporal resolution. Derived electrocorticographic (ECoG) signals allow the investigation of spatially highly focused task-related activation within the high-gamma frequency band, making the discrimination of individual finger movements or complex grasping tasks possible. Common spatial patterns (CSP) are commonly used for BCI systems and provide a powerful tool for feature optimization and dimensionality reduction. This work focused on the discrimination of (i) three complex hand movements, as well as (ii) hand movement and idle state. Two subjects S1 and S2 performed single `open', `peace' and `fist' hand poses in multiple trials. Signals in the high-gamma frequency range between 100 and 500 Hz were spatially filtered based on a CSP algorithm for (i) and (ii). Additionally, a manual feature selection approach was tested for (i). A multi-class linear discriminant analysis (LDA) showed for (i) an error rate of 13.89 % / 7.22 % and 18.42 % / 1.17 % for S1 and S2 using manually / CSP selected features, where for (ii) a two class LDA lead to a classification error of 13.39 % and 2.33 % for S1 and S2, respectively.

Rapid and low-invasive functional brain mapping by realtime visualization of high gamma activity for awake craniotomy.

For neurosurgery with an awake craniotomy, the critical issue is to set aside enough time to identify eloquent cortices by electrocortical stimulation (ECS). High gamma activity (HGA) ranging between 80 and 120 Hz on electrocorticogram (ECoG) is assumed to reflect localized cortical processing. In this report, we used realtime HGA mapping and functional magnetic resonance imaging (fMRI) for rapid and reliable identification of motor and language functions. Three patients with intra-axial tumors in their dominant hemisphere underwent preoperative fMRI and lesion resection with an awake craniotomy. All patients showed significant fMRI activation evoked by motor and language tasks. After the craniotomy, we recorded ECoG activity by placing subdural grids directly on the exposed brain surface. Each patient performed motor and language tasks and demonstrated realtime HGA dynamics in hand motor areas and parts of the inferior frontal gyrus. Sensitivity and specificity of HGA mapping were 100% compared to ECS mapping in the frontal lobe, which suggested HGA mapping precisely indicated eloquent cortices. The investigation times of HGA mapping was significantly shorter than that of ECS mapping. Specificities of the motor and language-fMRI, however, did not reach 85%. The results of HGA mapping was mostly consistent with those of ECS mapping, although fMRI tended to overestimate functional areas. This novel technique enables rapid and accurate functional mapping.

Performance of a tactile P300 speller for healthy people and severely disabled patients.

P300 based Brain-Computer Interfaces (BCIs) for communication are well known since many years. Most of them use visual stimuli to elicit evoked potentials because it is easy to integrate a high number of different classes into the paradigm. Nevertheless, a BCI that depends on visual stimuli is sometimes not feasible due to the presence of visual impairment in patients with severe brain injuries. In this case, it could be possible to use auditory or somatosensory stimulation. In this publication a vibrotactile P300 based BCI is introduced. Two different approaches were tested: a first approach using two stimulators and a second one that utilizes three stimulators for emitting the stimuli. The two paradigms were tested on 16 users: A group of ten healthy users and a second group comprising of 6 patients suffering Locked-In Syndrome. The control accuracy was calculated for both groups and both approaches, proving the feasibility of the device, not only for healthy people but also in severely disabled patients. In a second step we evaluated the influence of the number of stimuli on the accuracy. It was shown that in many cases the maximum accuracy was already reached with a small number of stimuli, this could be used in future tests to speed up the Information transfer rate.

A BCI using VEP for continuous control of a mobile robot.

A brain-computer interface (BCI) translates brain activity into commands to control devices or software. Common approaches are based on visual evoked potentials (VEP), extracted from the electroencephalogram (EEG) during visual stimulation. High information transfer rates (ITR) can be achieved using (i) steady-state VEP (SSVEP) or (ii) code-modulated VEP (c-VEP). This study investigates how applicable such systems are for continuous control of robotic devices and which method performs best. Eleven healthy subjects steered a robot along a track using four BCI controls on a computer screen in combination with feedback video of the movement. The average time to complete the tasks was (i) 573.43 s and (ii) 222.57 s. In a second non-continuous trial-based validation run the maximum achievable online classification accuracy over all subjects was (i) 91.36 % and (ii) 98.18 %. This results show that the c-VEP fits the needs of a continuous system better than the SSVEP implementation.

A motor imagery based brain-computer interface for stroke rehabilitation.

Brain-Computer Interfaces (BCIs) have been used to assist people with impairments since many years. In most of these applications the BCI is intended to substitute functions the user is no longer able to perform without help. For example BCIs could be used for communication and for control of devices like robotic arms, wheelchairs or also orthoses and prostheses. Another approach is not to replace the motor function itself by controlling a BCI, but to utilize a BCI for rehabilitation that enables the user to restore normal or "more normal" motor function. Motor imagery (MI) itself is a common strategy for motor rehabilitation in stroke patients. The idea of this paper is it to assist the MI by presenting online feedback about the imagination to the user. A BCI is presented that classifies MI of the left hand versus the right hand. Feedback is given to the user with two different strategies. One time by an abstract bar feedback, and the second time by a 3-D virtual reality environment: The left and right hand of an avatar in the 1st person's perspective in presented to him/her. If a motor imagery is detected, the according hand of the avatar moves. Preliminary tests were done on three healthy subjects. Offline analysis was then performed to (1) demonstrate the feasibility of the new, immersive, 3-D feedback strategy, (2) to compare it with the quite common bar feedback strategy and (3) to optimize the classification algorithm that detects the MI.

Prototype of an auto-calibrating, context-aware, hybrid brain-computer interface.

We present the prototype of a context-aware framework that allows users to control smart home devices and to access internet services via a Hybrid BCI system of an auto-calibrating sensorimotor rhythm (SMR) based BCI and another assistive device (Integra Mouse mouth joystick). While there is extensive literature that describes the merit of Hybrid BCIs, auto-calibrating and co-adaptive ERD BCI training paradigms, specialized BCI user interfaces, context-awareness and smart home control, there is up to now, no system that includes all these concepts in one integrated easy-to-use framework that can truly benefit individuals with severe functional disabilities by increasing independence and social inclusion. Here we integrate all these technologies in a prototype framework that does not require expert knowledge or excess time for calibration. In a first pilot-study, 3 healthy volunteers successfully operated the system using input signals from an ERD BCI and an Integra Mouse and reached average positive predictive values (PPV) of 72 and 98% respectively. Based on what we learned here we are planning to improve the system for a test with a larger number of healthy volunteers so we can soon bring the system to benefit individuals with severe functional disability.

Current trends in hardware and software for brain-computer interfaces (BCIs).

A brain-computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the development of certification, dissemination and reimbursement procedures.

Behavioral rehabilitation of the eye closure reflex in senescent rats using a real-time biosignal acquisition system.

In this paper the replacement of a lost learning function of rats through a computer-based real-time recording and feedback system is shown. In an experiment two recording electrodes and one stimulation electrode were implanted in an anesthetized rat. During a classical-conditioning paradigm, which includes tone and airpuff stimulation, biosignals were recorded and the stimulation events detected. A computational model of the cerebellum acquired the association between the stimuli and gave feedback to the brain of the rat using deep brain stimulation in order to close the eyelid of the rat. The study shows that replacement of a lost brain function using a direct bidirectional interface to the brain is realizable and can inspire future research for brain rehabilitation.

Exploring virtual environments with an EEG-based BCI through motor imagery.

In this paper, we describe the possibility of navigating in a virtual environment using the output signal of an EEG-based Brain-Computer Interface (BCI). The graphical capabilities of virtual reality (VR) should help to create new BCI-paradigms and improve feedback presentation. The objective of this combination is to enhance the subject's learning process of gaining control of the BCI. In this study, the participant had to imagine left or right hand movements while exploring a virtual conference room. By imaging a left hand movement the subject turned virtually to the left inside the room and with right hand imagery to the right. In fact, three trained subjects reached 80% to 100% BCI classification accuracy in the course of the experimental sessions. All subjects were able to achieve a rotation in the VR to the left or right by approximately 45 degrees during one trial.

Laboratory PC and Mobile Pocket PC Brain-Computer Interface Architectures.

EEG-based brain-computer interface (BCI) systems convert brain activity into control signals and have been developed for people with severe disabilities to improve their quality of life. A BCI system has to satisfy different demands depending on the application area. A laboratory PC based system allows the flexible design of multiple/single channel feature extraction, classification methods and experimental paradigms. The key advantage of a Pocket PC based BCI approach is its small dimension and battery supply. Hence a mobile BCI system e.g. mounted on a wheelchair can be realized. This study compares and discusses thoroughly the two mentioned approaches.

Correlation Changes of EEG and ECG After Fast Cable CAR Ascents.

In the Eastern Alps in Europe, the Dachstein. massif with a height of almost 3000 m is an ideal location for investigating the effects of changes in altitude on the human body. Within a few minutes, a cable car facilitates an ascent from 1702 m to 2700 m above sea level, where the partial pressure of oxygen is about 550 mmHg (as compared to 760 mmHg at sea level). In this study ten healthy subjects performed a reaction time task at 990 m and 2700 m in altitude. The subjects were instructed to perform a right hand index finger movement as fast as possible after a green light flashed (repeated 50 times). The corresponding electrocardiogram (ECG) and the electroencephalogram (EEG) were recorded. From the ECG heart rate and heart rate variability measures in the time and frequency domain were calculated. An event-related desynchronization/synchronization (ERD/ERS) analysis was performed with the EEG data. Finally, the EEG activity and the ECG parameters were correlated.