Anticipatory Detection of Self-Paced Rehabilitative Movements in the Same Upper Limb From EEG Signals (original) (raw)
Related papers
Detection of self-paced reaching movement intention from EEG signals
Frontiers in Neuroengineering, 2012
Future neuroprosthetic devices, in particular upper limb, will require decoding and executing not only the user's intended movement type, but also when the user intends to execute the movement. This work investigates the potential use of brain signals recorded non-invasively for detecting the time before a self-paced reaching movement is initiated which could contribute to the design of practical upper limb neuroprosthetics. In particular, we show the detection of self-paced reaching movement intention in single trials using the readiness potential, an electroencephalography (EEG) slow cortical potential (SCP) computed in a narrow frequency range (0.1-1 Hz). Our experiments with 12 human volunteers, two of them stroke subjects, yield high detection rates prior to the movement onset and low detection rates during the non-movement intention period. With the proposed approach, movement intention was detected around 500 ms before actual onset, which clearly matches previous literature on readiness potentials. Interestingly, the result obtained with one of the stroke subjects is coherent with those achieved in healthy subjects, with single-trial performance of up to 92% for the paretic arm. These results suggest that, apart from contributing to our understanding of voluntary motor control for designing more advanced neuroprostheses, our work could also have a direct impact on advancing 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.
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.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014
Stroke can be a source of significant upper extremity dysfunction and affect the quality of life (QoL) in survivors. In this context, novel rehabilitation approaches employing robotic rehabilitation devices combined with brain-machine interfaces can greatly help in expediting functional recovery in these individuals by actively engaging the user during therapy. However, optimal training conditions and parameters for these novel therapeutic systems are still unknown. Here, we present preliminary findings demonstrating successful movement intent detection from scalp electroencephalography (EEG) during robotic rehabilitation using the MAHI Exo-II in an individual with hemiparesis following stroke. These findings have strong clinical implications for the development of closed-loop brain-machine interfaces to robotic rehabilitation systems.
Classification of different reaching movements from the same limb using EEG
Journal of Neural Engineering, 2017
Objective. Brain-computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation. Approach. Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared. Main results. Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier. Significance. Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices using EEG data. These results have important implications towards assistive and rehabilitative neuroprostheses control in paralyzed patients.
Applied Sciences
In this study, we aimed to evaluate the effectiveness of a brain robot in rehabilitation that combines motor imagery (MI), robotic motor assistance, and electrical stimulation. Thirteen in-patients with severe post-stroke hemiplegia underwent electroencephalography (EEG), measured according to the international 10–20 method, during MI. The dicephalus robotic system (DiC) was activated by detecting event-related desynchronization (ERD) using the Markov switching model (MSM) and relative power (RP) from the EEG of the motor cortex (C3 and C4). The reaction times (the time between ERD detection and DiC activation) of the MSM and RP were compared using Wilcoxon’s signed rank sum test. ERD was detected in all 13 and 12 patients with the MSM and RP, respectively. The DiC reaction time for the ERD detection process was significantly shorter for the MSM (13.02 ± 0.16 s) than for the RP (19.95 ± 7.45 s) (W = 9, p = 0.0037). The results of this study suggest that ERD responses can be detected...
EEG-Based Lower-Limb Movement Onset Decoding: Continuous Classification and Asynchronous Detection
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2018
Brain-machine interfaces have been used to incorporate the user intention to trigger robotic devices by decoding movement onset from electroencephalography. Active neural participation is crucial to promote brain plasticity thus to enhance the opportunity of motor recovery. This paper presents the decoding of lower-limb movement-related cortical potentials with continuous classification and asynchronous detection. We executed experiments in a customized gait trainer, where 10 healthy subjects performed self-initiated ankle plantar flexion. We further analyzed the features, evaluated the impact of the limb side, and compared the proposed framework with other typical decoding methods. No significant differences were observed between the left and right legs in terms of neural signatures of movement and classification performance. We obtained a higher true positive rate, lower false positives, and comparable latencies with respect to the existing online detection methods. This paper dem...
Advances in Science, Technology and Engineering Systems Journal
Improving the hand motor skills in post-stroke patients through rehabilitation based on movement intention derived signals from the brain in conjunction with robot-assistive technologies are explored. The experimental work is conducted using Electroencephalogram based Brain-Computer Interface (EEG-BCI) system and the AMADEO hand rehabilitation robotic device. Two protocols using visual-cues and then using a 2-Dimensional (2D) interactive game is presented on a computer screen to healthy subjects as well as post-stroke patients performing the hand movements. The movement intention signals during hand movement are detected through the Support Vector Machine (SVM) classifier. The intent signals produced at six distinct electrodes are investigated to determine electrodes contributing most to the SVM classifier's performance. Overall, the game protocol shows better classification results for both healthy and stroke patients compared to the visual-cues protocol. FC3 is found to be the most consistent electrode site for the detection of the motor intention of the hand for both protocols. In the experimental work, average classification accuracy for the visual-cues protocol of 67.56% for healthy subjects and 56.24% for stroke patients were obtained. For the game protocol, the classifier accuracy produced for healthy participants was 79.7% and for the post-stroke patients was 66.64%. The results confirm that the intention signal is more pronounced during more engaging activities, such as playing games, for both healthy and stroke subjects. Therefore, the effectiveness of rehabilitation therapy for post-stroke patients could be significantly enhanced using interactive and engaging exercise protocols.