Feature domain-specific movement intention detection for stroke rehabilitation with brain-computer interfaces

2016 ◽  
Author(s):  
J. T. Hadsund ◽  
M. B. Sorensen ◽  
A. C. Royo ◽  
I. K. Niazi ◽  
H. Rovsing ◽  
...  
Keyword(s):  
Stroke Rehabilitation ◽  
Brain Computer Interfaces ◽  
Domain Specific ◽  
Computer Interfaces ◽  
Movement Intention

Brain-computer interfaces for stroke rehabilitation: summary of the 2016 BCI Meeting in Asilomar

2018 ◽  
Vol 5 (2-3) ◽  
pp. 41-57
Author(s):  
Christoph Guger ◽  
José del R. Millán ◽  
Donatella Mattia ◽  
Junichi Ushiba ◽  
Surjo R. Soekadar ◽  
...  
Keyword(s):  
Stroke Rehabilitation ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

scholarly journals EEG Headset Evaluation for Detection of Single-Trial Movement Intention for Brain-Computer Interfaces

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2804 ◽  
Author(s):  
Mads Jochumsen ◽  
Hendrik Knoche ◽  
Troels Wesenberg Kjaer ◽  
Birthe Dinesen ◽  
Preben Kidmose
Keyword(s):  
Motor Cortex ◽  
Signal To Noise Ratio ◽  
Brain Computer Interfaces ◽  
Single Trial ◽  
Signal To Noise ◽  
Computer Interfaces ◽  
Continuous Eeg ◽  
Good For ◽  
Movement Intention ◽  
Healthy Participants

Brain–computer interfaces (BCIs) can be used in neurorehabilitation; however, the literature about transferring the technology to rehabilitation clinics is limited. A key component of a BCI is the headset, for which several options are available. The aim of this study was to test four commercially available headsets’ ability to record and classify movement intentions (movement-related cortical potentials—MRCPs). Twelve healthy participants performed 100 movements, while continuous EEG was recorded from the headsets on two different days to establish the reliability of the measures: classification accuracies of single-trials, number of rejected epochs, and signal-to-noise ratio. MRCPs could be recorded with the headsets covering the motor cortex, and they obtained the best classification accuracies (73%−77%). The reliability was moderate to good for the best headset (a gel-based headset covering the motor cortex). The results demonstrate that, among the evaluated headsets, reliable recordings of MRCPs require channels located close to the motor cortex and potentially a gel-based headset.

Brain-Computer Interfaces With Multi-Sensory Feedback for Stroke Rehabilitation: A Case Study

2017 ◽  
Vol 41 (11) ◽  
pp. E178-E184 ◽  
Author(s):  
Danut C. Irimia ◽  
Woosang Cho ◽  
Rupert Ortner ◽  
Brendan Z. Allison ◽  
Bogdan E. Ignat ◽  
...  
Keyword(s):  
Stroke Rehabilitation ◽  
Sensory Feedback ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

scholarly journals Self-Paced Online vs. Cue-Based Offline Brain–Computer Interfaces for Inducing Neural Plasticity

2019 ◽  
Vol 9 (6) ◽  
pp. 127 ◽  
Author(s):  
Mads Jochumsen ◽  
Muhammad Samran Navid ◽  
Rasmus Wiberg Nedergaard ◽  
Nada Signal ◽  
Usman Rashid ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Neural Plasticity ◽  
Stroke Rehabilitation ◽  
Cortical Plasticity ◽  
Cortical Excitability ◽  
Motor Evoked Potentials ◽  
Brain Computer Interfaces ◽  
Post Intervention ◽  
Computer Interfaces ◽  
The Absolute

Brain–computer interfaces (BCIs), operated in a cue-based (offline) or self-paced (online) mode, can be used for inducing cortical plasticity for stroke rehabilitation by the pairing of movement-related brain activity with peripheral electrical stimulation. The aim of this study was to compare the difference in cortical plasticity induced by the two BCI modes. Fifteen healthy participants participated in two experimental sessions: cue-based BCI and self-paced BCI. In both sessions, imagined dorsiflexions were extracted from continuous electroencephalogram (EEG) and paired 50 times with the electrical stimulation of the common peroneal nerve. Before, immediately after, and 30 min after each intervention, the cortical excitability was measured through the motor-evoked potentials (MEPs) of tibialis anterior elicited through transcranial magnetic stimulation. Linear mixed regression models showed that the MEP amplitudes increased significantly (p < 0.05) from pre- to post- and 30-min post-intervention in terms of both the absolute and relative units, regardless of the intervention type. Compared to pre-interventions, the absolute MEP size increased by 79% in post- and 68% in 30-min post-intervention in the self-paced mode (with a true positive rate of ~75%), and by 37% in post- and 55% in 30-min post-intervention in the cue-based mode. The two modes were significantly different (p = 0.03) at post-intervention (relative units) but were similar at both post timepoints (absolute units). These findings suggest that immediate changes in cortical excitability may have implications for stroke rehabilitation, where it could be used as a priming protocol in conjunction with another intervention; however, the findings need to be validated in studies involving stroke patients.

Detection of Movement Intention in EEG-Based Brain-Computer Interfaces Using Fourier-Based Synchrosqueezing Transform

2021 ◽  
Author(s):  
Nedime Karakullukcu ◽  
Bülent Yilmaz
Keyword(s):  
Statistical Significance ◽  
Feature Selection Method ◽  
Support Vector ◽  
Brain Computer Interfaces ◽  
Motor Impairments ◽  
Computer Interfaces ◽  
Synchrosqueezing Transform ◽  
Feature Extractor ◽  
Processing Effort ◽  
Movement Intention

Patients with motor impairments need caregivers’ help to initiate the operation of brain-computer interfaces (BCI). This study aims to identify and characterize movement intention using multichannel electroencephalography (EEG) signals as a means to initiate BCI systems without extra accessories/methodologies. We propose to discriminate the resting and motor imagery (MI) states with high accuracy using Fourier-based synchrosqueezing transform (FSST) as a feature extractor. FSST has been investigated and compared with other popular approaches in 28 healthy subjects for a total of 6657 trials. The accuracy and f-measure values were obtained as 99.8% and 0.99, respectively, when FSST was used as the feature extractor and singular value decomposition (SVD) as the feature selection method and support vector machines as the classifier. Moreover, this study investigated the use of data that contain certain amount of noise without any preprocessing in addition to the clean counterparts. Furthermore, the statistical analysis of EEG channels with the best discrimination (of resting and MI states) characteristics demonstrated that F4-Fz-C3-Cz-C4-Pz channels and several statistical features had statistical significance levels, [Formula: see text], less than 0.05. This study showed that the preparation of the movement can be detected in real-time employing FSST-SVD combination and several channels with minimal pre-processing effort.

P29 Neural correlates of post-stroke rehabilitation based on brain-computer interfaces: Preliminary outlook

2020 ◽  
Vol 131 (4) ◽  
pp. e192-e193
Author(s):  
K.A. Grigoryan ◽  
V. Nikulin ◽  
A. Anwander ◽  
K. Pine ◽  
N. Weiskopf ◽  
...  
Keyword(s):  
Stroke Rehabilitation ◽  
Neural Correlates ◽  
Brain Computer Interfaces ◽  
Post Stroke ◽  
Computer Interfaces

A Study of Frontal Signals in Brain Computer Interfaces: Interpretation of EEG & FNIRS

2020 ◽  
pp. 136-142
Author(s):  
S. Srilekha ◽  
B. Vanathi
Keyword(s):  
Infrared Spectroscopy ◽  
Healthy Subject ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Conditions ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
Computer Interfaces ◽  
Rehabilitation Devices

This paper focuses on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) comparison to help the rehabilitation patients. Both methods have unique techniques and placement of electrodes. Usage of signals are different in application based on the economic conditions. This study helps in choosing the signal for the betterment of analysis. Ten healthy subject datasets of EEG & FNIRS are taken and applied to plot topography separately. Accuracy, Sensitivity, peaks, integral areas, etc are compared and plotted. The main advantages of this study are to prompt their necessities in the analysis of rehabilitation devices to manage their life as a typical individual.

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