13,064 research outputs found
Comparison of movement related cortical potential in healthy people and amyotrophic lateral sclerosis patients
No full textObjective: To understand the brain motor functions and neurophysiological changes due to motor disorder by comparing electroencephalographic data between healthy people and amyotrophic lateral sclerosis (ALS) patients. Methods: The movement related cortical potential (MRCP) was recorded from seven healthy subjects and four ALS patients. They were asked to imagine right wrist extension at two speeds (fast and slow). The peak negativity (RN) and rebound rate (RR) were extracted from MRCP for comparison. Results: The statistical analysis has showed that there was no significant difference in RN between the healthy and the ALS subjects. However, the healthy subjects presented faster RR than ALS during both fast and slow movement imagination. Conclusions: The weaker RR of ALS patients might reflect the impairment of motor output pathways or the degree of motor degeneration. Significance: The comparison between healthy people and ALS patients provides a way to explain the movement disorder through brain electrical signal. In addition, the characteristics of MRCP could be used to monitor and guide brain plasticity in patients
Author Correction: Evaluation of skin cancer resection guide using hyper‑realistic in‑vitro phantom fabricated by 3D printing
Full text linkThe original version of this Article contained an error in the spelling of the author Taehun Kim which was incorrectly given as Teahun Kim. The original Article has been corrected
Enhanced low-latency detection of motor intention from EEG for closed-loop brain-computer interface applications
No full textIn recent years, the detection of voluntary motor intentions from electroencephalogram (EEG) has been used for triggering external devices in closed-loop brain-computer interface (BCI) research. Movement-related cortical potentials (MRCP), a type of slow cortical potentials, have been recently used for detection. In order to enhance the efficacy of closed-loop BCI systems based on MRCPs, a manifold method called Locality Preserving Projection, followed by a linear discriminant analysis (LDA) classifier (LPP-LDA) is proposed in this paper to detect MRCPs from scalp EEG in real time. In an online experiment on nine healthy subjects, LPP-LDA statistically outperformed the classic matched filter approach with greater true positive rate (79 +/- 11% versus 68 +/- 10%; p = 0.007) and less false positives (1.4 +/- 0.8/min versus 2.3 +/- 1.1/min; p = 0.016). Moreover, the proposed system performed detections with significantly shorter latency (315 +/- 165 ms versus 460 +/- 123 ms; p = 0.013), which is a fundamental characteristics to induce neuroplastic changes in closed-loop BCIs, following the Hebbian principle. In conclusion, the proposed system works as a generic brain switch, with high accuracy, low latency, and easy online implementation. It can thus be used as a fundamental element of BCI systems for neuromodulation and motor function rehabilitation.EU [247935]; China Scholarship Council [201204910155
A brain–computer interface for single-trial detection of gait initiation from movement related cortical potentials
No full textObjective: Applications of brain-computer interfacing (BCI) in neurorehabilitation have received increasing attention. The intention to perform a motor task can be detected from scalp EEG and used to control rehabilitation devices, resulting in a patient-driven rehabilitation paradigm. In this study, we present and validate a BCI system for detection of gait initiation using movement related cortical potentials (MRCP). Methods: The templates of MRCP were extracted from 9-channel scalp EEG during gait initiation in 9 healthy subjects. Independent component analysis (ICA) was used to remove artifacts, and the Laplacian spatial filter was applied to enhance the signal-to-noise ratio of MRCP. Following these pre-processing steps, a matched filter was used to perform single-trial detection of gait initiation. Results: ICA preprocessing was shown to significantly improve the detection performance. With ICA pre-processing, across all subjects, the true positive rate (TPR) of the detection was 76.9 +/- 8.97%, and the false positive rate was 2.93 +/- 1.09 per minute. Conclusion: The results demonstrate the feasibility of detecting the intention of gait initiation from EEG signals, on a single trial basis. Significance: The results are important for the development of new gait rehabilitation strategies, either for recovery/replacement of function or for neuromodulation. (C) 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.EU project BETTER [247935
Detecting and classifying movement-related cortical potentials associated with hand movements in healthy subjects and stroke patients from single-electrode, single-trial EEG
Full text linkObjective. To detect movement intention from executed and imaginary palmar grasps in healthy subjects and attempted executions in stroke patients using one EEG channel. Moreover, movement force and speed were also decoded. Approach. Fifteen healthy subjects performed motor execution and imagination of four types of palmar grasps. In addition, five stroke patients attempted to perform the same movements. The movements were detected from the continuous EEG using a single electrode/channel overlying the cortical representation of the hand. Four features were extracted from the EEG signal and classified with a support vector machine (SVM) to decode the level of force and speed associated with the movement. The system performance was evaluated based on both detection and classification. Main results. similar to 75% of all movements (executed, imaginary and attempted) were detected 100 ms before the onset of the movement. similar to 60% of the movements were correctly classified according to the intended level of force and speed. When detection and classification were combined, similar to 45% of the movements were correctly detected and classified in both the healthy and stroke subjects, although the performance was slightly better in healthy subjects. Significance. The results indicate that it is possible to use a single EEG channel for detecting movement intentions that may be combined with assistive technologies. The simple setup may lead to a smoother transition from laboratory tests to the clinic.Danish Technical Research Counci
Peripheral electrical stimulation triggered by self-paced detection of motor intention enhances motor evoked potentials
No full textThis 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
Factors of Influence on the Performance of a Short-Latency Non-Invasive Brain Switch: Evidence in Healthy Individuals and Implication for Motor Function Rehabilitation
Full text linkBrain-computer interfacing (BCI) has recently been applied as a rehabilitation approach for patients with motor disorders, such as stroke. In these closed-loop applications, a brain switch detects the motor intention from brain signals, e.g. scalp EEG, and triggers a neuroprosthetic device, either to deliver sensory feedback or to mimic real movements, thus re-establishing the compromised sensory-motor control loop and promoting neural plasticity. In this context, single trial detection of motor intention with short latency is a prerequisite. The performance of the event detection from EEG recordings is mainly determined by three factors: the type of motor imagery (e.g., repetitive, ballistic), the frequency band (or signal modality) used for discrimination (e.g., alpha, beta, gamma, and MRCP, i.e. movement-related cortical potential), and the processing technique (e.g., time-series analysis, sub-band power estimation). In this study, we investigated single trial EEG traces during movement imagination on healthy individuals, and provided a comprehensive analysis of the performance of a short-latency brain switch when varying these three factors. The morphological investigation showed a cross-subject consistency of a prolonged negative phase in MRCP, and a delayed beta rebound in sensory-motor rhythms during repetitive tasks. The detection performance had the greatest accuracy when using ballistic MRCP with time-series analysis. In this case, the true positive rate was ~70% for a detection latency of ~200 ms. The results presented here are of practical relevance for designing BCI systems for motor function rehabilitation
Detection and classification of movement-related cortical potentials associated with task force and speed
Full text linkObjective. In this study, the objective was to detect movement intentions and extract different levels of force and speed of the intended movement from scalp electroencephalography (EEG). We then estimated the performance of the closed loop system. Approach. Cued movements were detected from continuous EEG recordings using a template of the initial phase of the movement-related cortical potential in 12 healthy subjects. The temporal features, extracted from the movement intention, were classified with an optimized support vector machine. The system performance was evaluated when combining detection with classification. Main results. The system detected 81% of the movements and correctly classified 75 +/- 9% and 80 +/- 10% of these at the point of detection when varying the force and speed, respectively. When the detector was combined with the classifier, the system detected and correctly classified 64 +/- 13% and 67 +/- 13% of these movements. The system detected and incorrectly classified 21 +/- 7% and 16 +/- 9% of the movements. The movements were detected 317 +/- 73 ms before the movement onset. Significance. The results indicate that it is possible to detect movement intentions with limited latencies, and extract and classify different levels of force and speed, which may be combined with assistive technologies for patient-driven neurorehabilitation.Danish Technical Research Counci
Detection of movement intention from single-trial movement-related cortical potentials
No full textDetection 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
Endogenous sensory discrimination and selection by a fast brain switch for a high transfer rate brain-computer interface
No full textIn this study, we present a novel multi-class brain-computer interface (BCI) for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input. The user discriminated these choices by his/her endogenous sensory ability and selected the desired choice with an intuitive motor task. This selection was detected by a fast brain switch based on real-time detection of movement-related cortical potentials from scalp EEG. We demonstrated the feasibility of such a system with a four-class BCI, yielding a true positive rate of ∼ 80% and ∼ 70%, and an information transfer rate of ∼ 7 bits/min and ∼ 5 bits/min, for the movement and imagination selection command, respectively. Furthermore, when the system was extended to eight classes, the throughput of the system was improved, demonstrating the capability of accommodating a large number of classes. Combining the endogenous sensory discrimination with the fast brain switch, the proposed system could be an effective, multi-class, gaze-independent BCI system for communication and control applications.</p
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