Abstract TP144: EEG-Based Brain Computer Interface Therapy for the Restoration of Distal Upper Extremity Motor Function

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Alexander Remsik ◽  
Hemali Advani ◽  
Shruti Rajan ◽  
Kieth Dodd ◽  
Tyler Jacobson ◽  
...  
Keyword(s):  
Upper Extremity ◽  
Motor Function ◽  
Visual Stimulation ◽  
Brain Computer Interface ◽  
Hand Movement ◽  
Performance Outcomes ◽  
Computer Interface ◽  
Non Invasive ◽  
Task Accuracy ◽  
Post Therapy

Introduction: This current study is part of a larger, on-going clinical trial (NCT02098265), which evaluates non-invasive electroencephalographic (EEG) based brain-computer interface(BCI) therapy for restoration of distal upper extremity motor function in stroke survivors. We acquired data for 8 participants (mean age= 64 years) presenting with varying levels of upper-extremity motor deficits and chronicity since stroke. EEG based BCI task-related performance outcomes were compared with distal extremity behavioral testing on the 9-Hole Peg Test - 9HPT) to illustrate the relationship of BCI training performance on behavioral performance. Methods: EEG data is acquired with BCI2000, a 16 channel recording system (g.LADYbird-g. GAMMAsys-g. USBamp, Guger Technologies, Graz, Austria) with electrodes positioned according to the standard 10-20 system over the sensorimotor cortex at C3 & C4. Participants performed a hand movement task; randomly cued to move either their affected or unaffected hand depending on the appearance of virtual target and cursor on the screen. 9HPT Data was selected from two BCI conditions: 1) BCI with visual stimulus only, and 2) BCI with visual stimulation plus functional electrical stimulation (FES) of the impaired arm, and tongue stimulation (TS) at four time-points: baseline (prior to BCI therapy), mid-point of BCI therapy, post-therapy, and one month post-therapy. Results: BCI visual plus stimulus for the unaffected side was found to best relate to 9HPT scores (p= 0.0928) with a trend to significance. BCI visual plus stimulus for the affected side was not shown to correlate with 9HPT scores (p= 0.2655). The results suggest that 9HPT scores better relate with average task accuracy when the TS and FES adjuvants are incorporated (BCI stimulus vs 9H PT unaffected p= 0.09284, BCI stimulus vs 9H PT affected p= 0.26553) compared to the BCI visual only task (BCI visual vs 9HPT unaffected p=0.2702, BCI visual vs 9HPT affected (p= 0.89276). Conclusion: Non-invasive EEG-based BCI therapy may be suitable for the restoration of distal upper extremity motor function but average task accuracy does not appear to be a valid indicator of motor improvement. This finding suggests that visual only BCI systems are inadequate for motor rehabilitation.

Brain–Computer Interface With Functional Electrical Stimulation Training for Upper-Extremity Rehabilitation Performance Outcomes

2017 ◽  
Vol 71 (4_Supplement_1) ◽  
pp. 7111515250p1
Author(s):  
Samantha Evander Elmore ◽  
Laura Kiekhoefer ◽  
Jessica Abrams ◽  
Rebecca Vermilyea ◽  
Dorothy Farrar-Edwards ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Upper Extremity ◽  
Functional Electrical Stimulation ◽  
Brain Computer Interface ◽  
Performance Outcomes ◽  
Computer Interface ◽  
Upper Extremity Rehabilitation

scholarly journals A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke

2016 ◽  
Vol 13 (5) ◽  
pp. 445-454 ◽  
Author(s):  
Alexander Remsik ◽  
Brittany Young ◽  
Rebecca Vermilyea ◽  
Laura Kiekhoefer ◽  
Jessica Abrams ◽  
...  
Keyword(s):  
Upper Extremity ◽  
Motor Function ◽  
Brain Computer Interface ◽  
Computer Interface

scholarly journals Effect of brain-computer interface training based on non-invasive electroencephalography using motor imagery on functional recovery after stroke - a systematic review and meta-analysis

BMC Neurology ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Antje Kruse ◽  
Zorica Suica ◽  
Jan Taeymans ◽  
Corina Schuster-Amft
Keyword(s):  
Upper Extremity ◽  
Motor Function ◽  
Motor Imagery ◽  
Brain Function ◽  
Conventional Therapy ◽  
Brain Computer Interface ◽  
Meta Analysis ◽  
Motor Recovery ◽  
Computer Interface ◽  
Function Recovery

Abstract Background Training with brain-computer interface (BCI) technology in the rehabilitation of patients after a stroke is rapidly developing. Numerous RCT investigated the effects of BCI training (BCIT) on recovery of motor and brain function in patients after stroke. Methods A systematic literature search was performed in Medline, IEEE Xplore Digital Library, Cochrane library, and Embase in July 2018 and was repeated in March 2019. RCT or controlled clinical trials that included BCIT for improving motor and brain recovery in patients after a stroke were identified. Data were meta-analysed using the random-effects model. Standardized mean difference (SMD) with 95% confidence (95%CI) and 95% prediction interval (95%PI) were calculated. A meta-regression was performed to evaluate the effects of covariates on the pooled effect-size. Results In total, 14 studies, including 362 patients after ischemic and hemorrhagic stroke (cortical, subcortical, 121 females; mean age 53.0+/− 5.8; mean time since stroke onset 15.7+/− 18.2 months) were included. Main motor recovery outcome measure used was the Fugl-Meyer Assessment. Quantitative analysis showed that a BCI training compared to conventional therapy alone in patients after stroke was effective with an SMD of 0.39 (95%CI: 0.17 to 0.62; 95%PI of 0.13 to 0.66) for motor function recovery of the upper extremity. An SMD of 0.41 (95%CI: − 0.29 to 1.12) for motor function recovery of the lower extremity was found. BCI training enhanced brain function recovery with an SMD of 1.11 (95%CI: 0.64 to 1.59; 95%PI ranging from 0.33 to 1.89). Covariates such as training duration, impairment level of the upper extremity, and the combination of both did not show significant effects on the overall pooled estimate. Conclusion This meta-analysis showed evidence that BCI training added to conventional therapy may enhance motor functioning of the upper extremity and brain function recovery in patients after a stroke. We recommend a standardised evaluation of motor imagery ability of included patients and the assessment of brain function recovery should consider neuropsychological aspects (attention, concentration). Further influencing factors on motor recovery due to BCI technology might consider factors such as age, lesion type and location, quality of performance of motor imagery, or neuropsychological aspects. Trial Registration PROSPERO registration: CRD42018105832.

scholarly journals Combined action observation- and motor imagery-based brain computer interface (BCI) for stroke rehabilitation: a case report

2021 ◽  
Author(s):  
Nuttawat Rungsirisilp ◽  
Yodchanan Wongsawat
Keyword(s):  
Physical Therapy ◽  
Upper Extremity ◽  
Motor Function ◽  
Motor Imagery ◽  
Stroke Rehabilitation ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Action Observation ◽  
Activity Patterns ◽  
Computer Interface

Abstract Introduction: Upper extremity impairment is a problem usually found in poststroke patients, and it is seldom completely improved even following conventional physical therapy. Motor imagery (MI) and action observation (AO) therapy are mental practices that may regain motor function in poststroke patients, especially when integrating them with brain-computer interface (BCI) technology. However, previous studies have always investigated the effects of an MI- or AO-based BCI for stroke rehabilitation separately. Therefore, in this study, we aimed to propose the effectiveness of a combined AO and MI (AOMI)-based BCI with functional electrical stimulation (FES) feedback to improve upper limb functions and alter brain activity patterns in chronic stroke patients.Case presentation: A 53-year-old male who was 12 years post stroke was left hemiparesis and unable to produce any wrist and finger extension.Intervention: The participant was given an AOMI-based BCI with FES feedback 3 sessions per week for 4 consecutive weeks, and he did not receive any conventional physical therapy during the intervention. The Fugl-Meyer Assessment of Upper Extremity (FMA-UE) and active range of motion (AROM) of wrist extension were used as clinical assessments, and the laterality coefficient (LC) value was applied to explore the altered brain activity patterns affected by the intervention.Outcomes: The FMA-UE score improved from 34 to 46 points, and the AROM of wrist extension was increased from 0 degrees to 20 degrees. LC values in the alpha band tended to be positive whereas LC values in the beta band seemed to be slightly negative after the intervention.Conclusion: An AOMI-based BCI with FES feedback training may be a promising strategy that could improve motor function in poststroke patients; however, its efficacy should be studied in a larger population and compared to that of other therapeutic methods.Trial registration: Thai Clinical Trial Registry: TCTR20200821002. Registered 17 August 2020, http://www.thaiclinicaltrials.org

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

Pilot Study on Using Innovative Counting Peaks Method for Assessment Purposes of the EEG Data Recorded from a Single-Channel Non-Invasive Brain-Computer Interface

2021 ◽  
Author(s):  
Natalia Browarska ◽  
Jaroslaw Zygarlicki ◽  
Mariusz Pelc ◽  
Michal Niemczynowicz ◽  
Malgorzata Zygarlicka ◽  
...  
Keyword(s):  
Pilot Study ◽  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Non Invasive ◽  
Eeg Data

scholarly journals EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018 ◽  
Vol 8 (11) ◽  
pp. 199 ◽  
Author(s):  
Rodrigo Ramele ◽  
Ana Villar ◽  
Juan Santos
Keyword(s):  
Brain Computer Interface ◽  
Waveform Analysis ◽  
Computer Interface ◽  
Clinical Tool ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Public Dataset ◽  
Imaging Sensor ◽  
Clinical Eeg

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

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