A high performance MEG based BCI using single trial detection of human movement intention (original) (raw)
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Detection of movement intention from single-trial movement-related cortical potentials
Journal of Neural Engineering, 2011
Detection of movement intention from neural signals combined with assistive technologies may be used for effective neurofeedback in rehabilitation. In order to promote plasticity, a causal relation between intended actions (detected for example from the EEG) and the corresponding feedback should be established. This requires reliable detection of motor intentions. In this study, we propose a method to detect movements from EEG with limited latency. In a self-paced asynchronous BCI paradigm, the initial negative phase of the movement-related cortical potentials (MRCPs), extracted from multi-channel scalp EEG was used to detect motor execution/imagination in healthy subjects and stroke patients. For MRCP detection, it was demonstrated that a new optimized spatial filtering technique led to better accuracy than a large Laplacian spatial filter and common spatial pattern. With the optimized spatial filter, the true positive rate (TPR) for detection of movement execution in healthy subjects (n = 15) was 82.5 ± 7.8%, with latency of −66.6 ± 121 ms. Although TPR decreased with motor imagination in healthy subject (n = 10, 64.5 ± 5.33%) and with attempted movements in stroke patients (n = 5, 55.01 ± 12.01%), the results are promising for the application of this approach to provide patient-driven real-time neurofeedback.
Detection of Movement Intention from Movement-Related Cortical Potentials with Different Paradigms
Biosystems & Biorobotics, 2014
In this study, we compared the effects of two imagery paradigms typically used within the field of brain computer interfaces on the detection of movement intention from scalp electroencephalography (EEG). This issue is important in the rehabilitation area because of its direct relation with appropriately timed neurofeedback. Subjects were asked to imagine hand or foot movements using either a random or a non-random cue. Templates were constructed individually for each subject. Movement intent was detected according to the correlation between the movement related cortical potentials (MRCP) of single trials with the initial part of the template. The large Laplacian filter was used to increase the signal to noise ratio (SNR). For the random cue, the true positive rate (TPR) of detection of movement intention was 63.5±5.9% for foot movement and the corresponding detection latency was 202.8±129.5 ms before movement onset. For the non-random cue, foot movement intention was detected with TPR of 75.3±5.5% and latency of 291±169.3 ms. These results demonstrate that cue type, random or non-random, has a significant effect on the performance of MRCPbased movement intention detection algorithms.
2009
Objective: The objective of this research is to explore whether a two-dimensional BCI can be achieved by efficiently and reliably decoding single-trial magnetoencephalography (MEG) signal associated with sustaining or ceasing right and left hand movements. Methods & Design: Seven naïve subjects participated in the study. Signals were recorded from 275-channel MEG and synthetic aperture magnetometry (SAM) was employed to enhance spatial resolution/signal-to-noise ratio. The multi-class classification for four-directional control was offline evaluated from 10-fold cross-validation using direct-decision tree classifier (Direct-DTC) method and Genetic Algorithm based Mahalanobis Linear Distance method (GA-MLD). Results: Beta band (15-30Hz) event-related desynchronization and event related synchronization was observed in right and left hand movement related motor areas for physical movements as well as motor imagery. The cross-validation accuracy for the proposed SAM-filtered, MEG-based two dimensional BCI was as high as 96.7±1.22% for physical movements and 88.41 % ±1.89 for motor imagery using GA-MLD and 93.34±2.13% for physical movements and 74.97± 3.24% for motor imagery using Direct-DTC. Conclusion: A reliable high performance two-dimensional BCI can be achieved from single trial detection of human movement intentions from SAM-filtered MEG signals. Significance: MEG signals associated with human natural motor behavior may provide a reliable brain-computer interface (BCI) for 2-dimensional control, which may reduce long-term training for conventional BCI methods using rhythm control. completely "locked-in", even though, their cognitive ability is intact. Brain-Computer interfaces (BCIs) are communication devices that allow for communicating intentions by analyzing mere brain activity, not involving muscles [1]. The development of BCI technology is of immense importance to patients in the 'locked-in' or semi-'locked-in' stage, where BCI can be used as a communication and rehabilitation tool. The direct brain communication or control may offer patients the only possible way to interact with external world. Different techniques have been used for decoding brain signals for reliable BCI communication. A BCI can be
Computational and Mathematical Methods in Medicine, 2015
The movement-related cortical potential (MRCP) is a low-frequency negative shift in the electroencephalography (EEG) recording that takes place about 2 seconds prior to voluntary movement production. MRCP replicates the cortical processes employed in planning and preparation of movement. In this study, we recapitulate the features such as signal’s acquisition, processing, and enhancement and different electrode montages used for EEG data recoding from different studies that used MRCPs to predict the upcoming real or imaginary movement. An authentic identification of human movement intention, accompanying the knowledge of the limb engaged in the performance and its direction of movement, has a potential implication in the control of external devices. This information could be helpful in development of a proficient patient-driven rehabilitation tool based on brain-computer interfaces (BCIs). Such a BCI paradigm with shorter response time appears more natural to the amputees and can al...
How capable is non-invasive EEG data of predicting the next movement? A mini review
Frontiers in human neuroscience, 2013
In this study we summarize the features that characterize the pre-movements and pre-motor imageries (before imagining the movement) electroencephalography (EEG) data in humans from both Neuroscientists' and Engineers' point of view. We demonstrate what the brain status is before a voluntary movement and how it has been used in practical applications such as brain computer interfaces (BCIs). Usually, in BCI applications, the focus of study is on the after-movement or motor imagery potentials. However, this study shows that it is possible to develop BCIs based on the before-movement or motor imagery potentials such as the Bereitschaftspotential (BP). Using the pre-movement or pre-motor imagery potentials, we can correctly predict the onset of the upcoming movement, its direction and even the limb that is engaged in the performance. This information can help in designing a more efficient rehabilitation tool as well as BCIs with a shorter response time which appear more natural ...
Self-paced movement intention detection from human brain signals: Invasive and non-invasive EEG
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2012
Neural signatures of humans' movement intention can be exploited by future neuroprosthesis. We propose a method for detecting self-paced upper limb movement intention from brain signals acquired with both invasive and noninvasive methods. In the first study with scalp electroencephalograph (EEG) signals from healthy controls, we report single trial detection of movement intention using movementrelated potentials (MRPs) in a frequency range between 0.1 to 1 Hz. Movement intention can be detected above chance level (p<0.05) on average 460 ms before the movement onset with low detection rate during the non-movement intention period. Using intracranial EEG (iEEG) from one epileptic subject, we detect movement intention as early as 1500 ms before movement onset with accuracy above 90% using electrodes implanted in the bilateral supplementary motor area (SMA). The coherent results obtained with non-invasive and invasive method and its generalization capabilities across different days of recording, strengthened the theory that self-paced movement intention can be detected before movement initiation for the advancement in robot-assisted neurorehabilitation.
Detecting the intention to move upper limbs from electroencephalographic brain signals
Early decoding of motor states directly from the brain activity is essential to develop brain-machine interfaces (BMI) for natural motor control of neuroprosthetic devices. Hence, this study aimed to investigate the detection of movement information before the actual movement occurs. This information could be useful to provide early control signals to drive BMI-based rehabilitation and motor assisted devices, thus providing a natural and active rehabilitation therapy. In this work, elec-troencephalographic (EEG) brain signals from six healthy right-handed participants were recorded during self-initiated reaching movements of the upper limbs. The analysis of these EEG traces showed that significant event-related desynchronization is present before and during the execution of the movements, predominantly in the motor-related α and β frequency bands and in electrodes placed above the motor cortex. This oscillatory brain activity was used to continuously detect the intention to move the limbs, i.e., to identify the motor phase prior to the actual execution of the reaching movement. The results showed, first, significant classification between relax and movement intention, and second, significant detection of movement intention prior to the onset of the executed movement. On the basis of these results, detection of movement intention could be used in BMI settings to reduce the gap between mental motor processes and the actual movement performed by an assisted or rehabilitation robotic device.
Detection of movement-related cortical potentials based on subject-independent training
Medical & Biological Engineering & Computing, 2013
To allow a routinely use of brain-computer interfaces (BCI), there is a need to reduce or completely eliminate the time-consuming part of the individualized training of the user. In this study, we investigate the possibility of avoiding the individual training phase in the detection of movement intention in asynchronous BCIs based on movement-related cortical potential (MRCP). EEG signals were recorded during ballistic ankle dorsiflexions executed (ME) or imagined (MI) by 20 healthy subjects, and attempted by five stroke subjects. These recordings were used to identify a template (as average over all subjects) for the initial negative phase of the MRCPs, after the application of an optimized spatial filtering used for pre-processing. Using this template, the detection accuracy (mean ± SD) calculated as true positive rate (estimated with leave-one-out procedure) for ME was 69 ± 21 and 58 ± 11 % on single trial basis for healthy and stroke subjects, respectively. This performance was similar to that obtained using an individual template for each subject, which led to accuracies of 71 ± 6 and 55 ± 12 % for healthy and stroke subjects, respectively. The detection accuracy for the MI data was 65 ± 22 % with the average template and 60 ± 13 % with the individual template. These results indicate the possibility of detecting movement intention without an individual training phase and without a significant loss in performance.
An MEG-based brain–computer interface (BCI
Neuroimage, 2007
Brain-Computer Interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography (EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG, and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor μ and β rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant μ-rhythm self control within 32 minutes of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.
Classification of Movement and Inhibition Using a Hybrid BCI
Brain-computer interfaces (BCIs) are an emerging technology that are capable of turning brain electrical activity into commands for an external device. Motor imagery (MI)—when a person imagines a motion without executing it—is widely employed in BCI devices for motor control because of the endogenous origin of its neural control mechanisms, and the similarity in brain activation to actual movements. Challenges with translating a MI-BCI into a practical device used outside laboratories include the extensive training required, often due to poor user engagement and visual feedback response delays; poor user flexibility/freedom to time the execution/inhibition of their movements, and to control the movement type (right arm vs. left leg) and characteristics (reaching vs. grabbing); and high false positive rates of motion control. Solutions to improve sensorimotor activation and user performance of MI-BCIs have been explored. Virtual reality (VR) motor-execution tasks have replaced simpler visual feedback (smiling faces, arrows) and have solved this problem to an extent. Hybrid BCIs (hBCIs) implementing an additional control signal to MI have improved user control capabilities to a limited extent. These hBCIs either fail to allow the patients to gain asynchronous control of their movements, or have a high false positive rate. We propose an immersive VR environment which provides visual feedback that is both engaging and immediate, but also uniquely engages a different cognitive process in the patient that generates event-related potentials (ERPs). These ERPs provide a key executive function for the users to execute/inhibit movements. Additionally, we propose signal processing strategies and machine learning algorithms to move BCIs toward developing long-term signal stability in patients with distinctive brain signals and capabilities to control motor signals. The hBCI itself and the VR environment we propose would help to move BCI technology outside laboratory environments for motor rehabilitation in hospitals, and potentially for controlling a prosthetic.