Peripheral electrical stimulation triggered by self-paced detection of motor intention enhances motor evoked potentials

This paper proposes the development and experimental tests of a self-paced asynchronous brain-computer interfacing (BCI) system that detects movement related cortical potentials (MRCPs) produced during motor imagination of ankle dorsiflexion and triggers peripheral electrical stimulations timed with the occurrence of MRCPs to induce corticospinal plasticity. MRCPs were detected online from EEG signals in eight healthy subjects with a true positive rate (TPR) of 67.15 ± 7.87% and false positive rate (FPR) of 22.05 ±9.07%. The excitability of the cortical projection to the target muscle (tibialis anterior) was assessed before and after the intervention through motor evoked potentials (MEP) using transcranial magnetic stimulation (TMS). The peak of the evoked potential significantly (P=0.02) increased after the BCI intervention by 53 ± 43% (relative to preintervention measure), although the spinal excitability (tested by stretch reflexes) did not change. These results demonstrate for the first time that it is possible to alter the corticospinal projections to the tibialis anterior muscle by using an asynchronous BCI system based on online motor imagination that triggered peripheral stimulation. This type of repetitive proprioceptive feedback training based on self-generated brain signal decoding may be a requirement for purposeful skill acquisition in intact humans and in the rehabilitation of persons with brain damage.     

Somatotopy of the motor cortex after long-term spinal cord injuryor amputation

Certain brain-computer interface (BCI) methods use intrinsic signals from the motor cortex to control neuroprosthetic devices. The organization of the motor pathways in those populations likely to use neuroprosthetic devices, therefore, needs to be determined; there is evidence that following disease or injury the representation of the body in the motor cortex may change. In this study, functional MRI measures of somatotopy following spinal cord injury (SCI) showed evidence of changes in limb representations in the motor cortex. Subjects with chronic SCI had unusual cortical patterns of activity when attempting to move limbs below their injury; amputees showed a more normal somatotopy. The functional reorganization may affect optimal implanted electrode placements for invasive BCI methods for these different populations     

The impact of loss of control on movement BCIs

Brain-computer interfaces (BCIs) are known to suffer from spontaneous changes in the brain activity. If changes in the mental state of the user are reflected in the brain signals used for control, the behavior of a BCI is directly influenced by these states. We investigate the influence of a state of loss of control in a variant of Pacman on the performance of BCIs based on motor control. To study the effect a temporal loss of control has on the BCI performance, BCI classifiers were trained on electroencephalography (EEG) recorded during the normal control condition, and the classification performance on segments of EEG from the normal and loss of control condition was compared. Classifiers based on event-related desynchronization unexpectedly performed significantly better during the loss of control condition; for the event-related potential classifiers there was no significant difference in performance.     

Linear classification of low-resolution EEG patterns produced by imagined hand movements.

Electroencephalograph (EEG)-based brain-computer interfaces (BCI's) require on-line detection of mental states from spontaneous EEG signals. In this framework, surface Laplacian (SL) transformation of EEG signals has proved to improve the recognition scores of imagined motor activity. The results we obtained in the first year of an European project named adaptive brain interfaces (ABI) suggest that: 1) the detection of mental imagined activity can be obtained by using the signal space projection (SSP) method as a classifier and 2) a particular type of electrodes can be used in such a BCI device, reconciling the benefits of SL waveforms and the need for the use of few electrodes. Recognition of mental activity was attempted on both raw and SL-transformed EEG data from five healthy people performing two mental tasks, namely imagined right and left hand movements.     

Cortical Imaging of Event-Related (de)Synchronization During Online Control of Brain-Computer Interface Using Minimum-Norm Estimates in Frequency Domain

It is of wide interest to study the brain activity that correlates to the control of brain–computer interface (BCI). In the present study, we have developed an approach to image the cortical rhythmic modulation associated with motor imagery using minimum-norm estimates in the frequency domain (MNEFD). The distribution of cortical sources of mu activity during online control of BCI was obtained with the MNEFD. Contralateral decrease (event-related desynchronization) and ipsilateral increase (event-related synchronization) are localized in the sensorimotor cortex during online control of BCI in a group of human subjects. Statistical source analysis revealed that maximum correlation with movement imagination is localized in sensorimotor cortex.      

Work toward real-time control of a cortical neural prothesis.

Implantable devices that interact directly with the human nervous system have been gaining acceptance in the field of medicine since the 1960's. More recently, as is noted by the FDA approval of a deep brain stimulator for movement disorders, interest has shifted toward direct communication with the central nervous system (CNS). Deep brain stimulation (DBS) can have a remarkable effect on the lives of those with certain types of disabilities such as Parkinson's disease, Essential Tremor, and dystonia. To correct for many of the motor impairments not treatable by DBS (e.g. quadriplegia), it would be desirable to extract from the CNS a control signal for movement. A direct interface with motor cortical neurons could provide an optimal signal for restoring movement. In order to accomplish this, a real-time conversion of simultaneously recorded neural activity to an online command for movement is required. A system has been established to isolate the cellular activity of a group of motor neurons and interpret their movement-related information with a minimal delay. The real-time interpretation of cortical activity on a millisecond time scale provides an integral first step in the development of a direct brain-computer interface (BCI).     

The use of EEG modifications due to motor imagery for brain-computer interfaces

The opening of a communication channel between brain and computer [brain-computer interface (BCI)] is possible by using changes in electroencephalogram (EEG) power spectra related to the imagination of movements. In this paper, we present results obtained by recording EEG during an upper limb motor imagery task in a total of 18 subjects by using low-resolution surface Laplacian, different linear and quadratic classifiers, as well as a variable number of scalp electrodes, from 2 to 26. The results (variable correct classification rate of mental imagery between 75% and 95%) suggest that it is possible to recognize quite reliably ongoing mental movement imagery for BCI applications.     

The Neurochip BCI: towards a neural prosthesis for upper limb function.

The Neurochip BCI is an autonomously operating interface between an implanted computer chip and recording and stimulating electrodes in the nervous system. By converting neural activity recorded in one brain area into electrical stimuli delivered to another site, the Neurochip BCI could form the basis for a simple, direct neural prosthetic. In tests with normal, unrestrained monkeys, the Neurochip continuously recorded activity of single neurons in primary motor cortex for several weeks at a time. Cortical activity was correlated with simultaneously-recorded electromyogram (EMG) activity from arm muscles during free behavior. In separate experiments with anesthetized monkeys, we found that microstimulation of the cervical spinal cord evoked movements of the arm and hand, often involving multiple muscles synergies. These observations suggest that spinal microstimulation controlled by cortical neurons could help compensate for damaged corticospinal projections.     

ECoG factors underlying multimodal control of a brain-computer interface.

Most current brain-computer interface (BCI) systems for humans use electroencephalographic activity recorded from the scalp, and may be limited in many ways. Electrocorticography (ECoG) is believed to be a minimally-invasive alternative to electroencephalogram (EEG) for BCI systems, yielding superior signal characteristics that could allow rapid user training and faster communication rates. In addition, our preliminary results suggest that brain regions other than the sensorimotor cortex, such as auditory cortex, may be trained to control a BCI system using similar methods as those used to train motor regions of the brain. This could prove to be vital for users who have neurological disease, head trauma, or other conditions precluding the use of sensorimotor cortex for BCI control.     

Estimation of the cortical connectivity patterns during the intention of limb movements.

The problem of the definition and evaluation of brain connectivity has become a central one in neuroscience during recent years as a way to understand the organization and interaction of cortical areas during the execution of cognitive or motor tasks. In this article, we propose the use of the directed transfer function (DTF) method on cortical signals estimated from high-resolution electroencephalography (EEG) recordings. An application of the proposed technique to the estimation of the cortical connectivity pattern in normal subjects and in one spinal-cord-injured patient is also provided.     

Utilizing gamma band to improve mental task based brain-computer interface design.

A common method for designing brain-computer Interface (BCI) is to use electroencephalogram (EEG) signals extracted during mental tasks. In these BCI designs, features from EEG such as power and asymmetry ratios from delta, theta, alpha, and beta bands have been used in classifying different mental tasks. In this paper, the performance of the mental task based BCI design is improved by using spectral power and asymmetry ratios from gamma (24-37 Hz) band in addition to the lower frequency bands. In the experimental study, EEG signals extracted during five mental tasks from four subjects were used. Elman neural network (ENN) trained by the resilient backpropagation algorithm was used to classify the power and asymmetry ratios from EEG into different combinations of two mental tasks. The results indicated that ((1) the classification performance and training time of the BCI design were improved through the use of additional gamma band features; (2) classification performances were nearly invariant to the number of ENN hidden units or feature extraction method.     

基于视听交互刺激的认知机理与脑机接口范式研究进展

与人类视听觉感知密切相关的图像、语音和文本(语言)信息在社会经济发展与国家安全保障等领域中扮演着重要角色,脑机接口(BCI)是无需外围神经肌肉便可操控外部设备、使"思想"直接变成"行动"的创新技术,基于视、听觉刺激的BCI系统具有极其广阔应用前景。目前认知心理学研究普遍认为视听交互刺激模式可产生比视或听单模态刺激更强的事件相关电位信号,因而可使识别大脑思维模式的正确率更高和响应速度更快,然而BCI研究至今尚难有充分验证其优势的成果。分别从认知心理机制和BCI实验范式两方面综述了视听交互刺激的信息整合机制、视听刺激之间的协同与竞争关系、匹配度对刺激效果的影响及相关BCI实验范式的研究现状;深入分析了视听交互刺激范式未在现有BCI系统中体现其优越性的原因;最后从BCI实验范式选择、系统硬件性能改进、脑电信号处理等方面提出了改进思路并展望了基于视听交互刺激BCI范式研究未来动向。     

Electroencephalography (EEG)-based brain-computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control

This study aims to propose an effective and practical paradigm for a brain-computer interface (BCI)-based 2-D virtual wheelchair control. The paradigm was based on the multi-class discrimination of spatiotemporally distinguishable phenomenon of event-related desynchronization/synchronization (ERD/ERS) in electroencephalogram signals associated with motor execution/imagery of right/left hand movement. Comparing with traditional method using ERD only, where bilateral ERDs appear during left/right hand mental tasks, the 2-D control exhibited high accuracy within a short time, as incorporating ERS into the paradigm hypothetically enhanced the spatiotemoral feature contrast of ERS versus ERD. We also expected users to experience ease of control by including a noncontrol state. In this study, the control command was sent discretely whereas the virtual wheelchair was moving continuously. We tested five healthy subjects in a single visit with two sessions, i.e., motor execution and motor imagery. Each session included a 20 min calibration and two sets of games that were less than 30 min. Average target hit rate was as high as 98.4% with motor imagery. Every subject achieved 100% hit rate in the second set of wheelchair control games. The average time to hit a target 10 m away was about 59 s, with 39 s for the best set. The superior control performance in subjects without intensive BCI training suggested a practical wheelchair control paradigm for BCI users.     

Continuous EEG classification during motor imagery-simulation of an asynchronous BCI

Nearly all electroencephalogram (EEG)-based brain-computer interface (BCI) systems operate in a cue-paced or synchronous mode. This means that the onset of mental activity (thought) is externally-paced and the EEG has to be analyzed in predefined time windows. In the near future, BCI systems that allow the user to intend a specific mental pattern whenever she/he wishes to produce such patterns will also become important. An asynchronous BCI is characterized by continuous analyzing and classification of EEG data. Therefore, it is important to maximize the hits (true positive rate) during an intended mental task and to minimize the false positive detections in the resting or idling state. EEG data recorded during right/left motor imagery is used to simulate an asynchronous BCI. To optimize the classification results, a refractory period and a dwell time are introduced.     

A virtual reality testbed for brain-computer interface research.

Virtual reality promises to extend the realm of possible brain-computer interface (BCI) prototypes. Most of the work using electroencephalograph (EEG) signals in VR has focussed on brain-body actuated control, where biological signals from the body as well as the brain are used. We show that when subjects are allowed to move and act normally in an immersive virtual environment, cognitive evoked potential signals can still be obtained and used reliably. A single trial accuracy average of 85% for recognizing the differences between evoked potentials at red and yellow stop lights will be presented and future directions discussed.     

Accessory hardware for neuromuscular measurements during functional MRI experiments

Functional magnetic resonance imaging (fMRI) is increasingly being used for human sensorimotor function research. Few studies, however, have been able to acquire peripheral neuromuscular data (e.g. joint force and electromyograms [EMG]) online with fMRI measurements. The lack of muscle output information hinders interpretation of fMRI data and prevents investigators from designing more sophisticated experiments. We developed a data-acquisition system that can record force and EMG data simultaneously with fMRI signals. This system included three major components: a hydraulic, pressure transducer-based force measurement device, a well-shielded EMG-recording apparatus, and a visual feedback setup. The three components were integrated with a laptop computer equipped with data acquisition hardware and software. System evaluation experiments demonstrated that no significant mutual interference occurred between the MRI environment and the force-EMG data-acquisition system, i.e. the system can record relatively noise-free force and EMG signals while maintaining the quality of fMRI data. The system has enabled us to study human motor control function involving motor tasks such as handgrip and finger pinch that require precision control of force and EMG. This accessory equipment can facilitate fMRI investigations of human sensorimotor function.     

脑电 BCI 系统的软硬件开发平台发展现状

脑-机接口系统(brain-computer interface,BCI)是一种将大脑活动信息直接转换为人工输出的系统,允许用户通过思维 活动直接控制外部设备。 脑电图技术(electroencephalogram,EEG)可以实时获取大脑活动产生的神经生理电信息,具有无创、低 廉、高时间分辨率等优点,是 BCI 获取大脑活动信息的主流方式之一。 脑电 BCI 系统具有脑电信号采集、处理和输出结果的功 能,能够诱发特征脑电,并控制外部设备,在康复、医疗诊断和神经科学研究等领域具有巨大的应用价值。 随着脑电 BCI 系统应 用需求不断增加,确保其快速高效地部署和应用的技术越来越重要。 结合近些年脑电 BCI 系统研究和应用,综合论述目前用于 开发脑电采集和编解码的硬件和软件平台的技术,分析归纳其当前现状与未来趋势,以促进开发脑电 BCI 系统软硬件平台的有 效发展。     

Accessory hardware for neuromuscular measurements during functional MRI experiments.

Functional magnetic resonance imaging (fMRI) is increasingly being used for human sensorimotor function research. Few studies, however, have been able to acquire peripheral neuromuscular data (e.g. joint force and electromyograms [EMG]) online with fMRI measurements. The lack of muscle output information hinders interpretation of fMRI data and prevents investigators from designing more sophisticated experiments. We developed a data-acquisition system that can record force and EMG data simultaneously with fMRI signals. This system included three major components: a hydraulic, pressure transducer-based force measurement device, a well-shielded EMG-recording apparatus, and a visual feedback setup. The three components were integrated with a laptop computer equipped with data acquisition hardware and software. System evaluation experiments demonstrated that no significant mutual interference occurred between the MRI environment and the force-EMG data-acquisition system, i.e. the system can record relatively noise-free force and EMG signals while maintaining the quality of fMRI data. The system has enabled us to study human motor control function involving motor tasks such as handgrip and finger pinch that require precision control of force and EMG. This accessory equipment can facilitate fMRI investigations of human sensorimotor function.     

The Wadsworth BCI Research and Development Program: at home with BCI.

The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive, electroencephalography (EEG)-based BCI methods. We have shown that people, including those with severe motor disabilities, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one or two dimensions. We have also improved P300-based BCI operation. We are now translating this laboratory-proven BCI technology into a system that can be used by severely disabled people in their homes with minimal ongoing technical oversight. To accomplish this, we have: improved our general-purpose BCI software (BCI2000); improved online adaptation and feature translation for SMR-based BCI operation; improved the accuracy and bandwidth of P300-based BCI operation; reduced the complexity of system hardware and software and begun to evaluate home system use in appropriate users. These developments have resulted in prototype systems for every day use in people's homes.     

Graz-BCI: state of the art and clinical applications

The Graz-brain-computer interface (BCI) is a cue-based system using the imagery of motor action as the appropriate mental task. Relevant clinical applications of BCI-based systems for control of a virtual keyboard device and operations of a hand orthosis are reported. Additionally, it is demonstrated how information transfer rates of 17 b/min can be acquired by real time classification of oscillatory activity.