Literature DB >> 31247558

Prognostic and Monitory EEG-Biomarkers for BCI Upper-Limb Stroke Rehabilitation.

Ravikiran Mane, Effie Chew, Kok Soon Phua, Kai Keng Ang, Neethu Robinson, A P Vinod, Cuntai Guan.   

Abstract

With the availability of multiple rehabilitative interventions, identifying the one that elicits the best motor outcome based on the unique neuro-clinical profile of the stroke survivor is a challenging task. Predicting the potential of recovery using biomarkers specific to an intervention hence becomes important. To address this, we investigate intervention-specific prognostic and monitory biomarkers of motor function improvements using quantitative electroencephalography (QEEG) features in 19 chronic stroke patients following two different upper extremity rehabilitative interventions viz. Brain-computer interface (BCI) and transcranial direct current stimulation coupled BCI (tDCS-BCI). Brain symmetry index was found to be the best prognostic QEEG for clinical gains following BCI intervention ( r = -0.80 , p = 0.02 ), whereas power ratio index (PRI) was observed to be the best predictor for tDCS-BCI ( r = -0.96 , p = 0.004 ) intervention. Importantly, statistically significant between-intervention differences observed in the predictive capabilities of these features suggest that intervention-specific biomarkers can be identified. This approach can be further pursued to distinctly predict the expected response of a patient to available interventions. The intervention with the highest predicted gains may then be recommended to the patient, thereby enabling a personalized rehabilitation regime.

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Year:  2019        PMID: 31247558     DOI: 10.1109/TNSRE.2019.2924742

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  15 in total

1.  Using Multiple Decomposition Methods and Cluster Analysis to Find and Categorize Typical Patterns of EEG Activity in Motor Imagery Brain-Computer Interface Experiments.

Authors:  Alexander Frolov; Pavel Bobrov; Elena Biryukova; Mikhail Isaev; Yaroslav Kerechanin; Dmitry Bobrov; Alexander Lekin
Journal:  Front Robot AI       Date:  2020-07-30

2.  The Prognostic Utility of Electroencephalography in Stroke Recovery: A Systematic Review and Meta-Analysis.

Authors:  Amanda A Vatinno; Annie Simpson; Viswanathan Ramakrishnan; Heather S Bonilha; Leonardo Bonilha; Na Jin Seo
Journal:  Neurorehabil Neural Repair       Date:  2022-03-20       Impact factor: 3.919

3.  EEG Microstate-Specific Functional Connectivity and Stroke-Related Alterations in Brain Dynamics.

Authors:  Zexuan Hao; Xiaoxue Zhai; Dandan Cheng; Yu Pan; Weibei Dou
Journal:  Front Neurosci       Date:  2022-05-11       Impact factor: 5.152

4.  Brain oscillatory activity as a biomarker of motor recovery in chronic stroke.

Authors:  Andreas M Ray; Thiago D C Figueiredo; Eduardo López-Larraz; Niels Birbaumer; Ander Ramos-Murguialday
Journal:  Hum Brain Mapp       Date:  2019-11-28       Impact factor: 5.038

5.  An Inter- and Intra-Subject Transfer Calibration Scheme for Improving Feedback Performance of Sensorimotor Rhythm-Based BCI Rehabilitation.

Authors:  Lei Cao; Shugeng Chen; Jie Jia; Chunjiang Fan; Haoran Wang; Zhixiong Xu
Journal:  Front Neurosci       Date:  2021-01-28       Impact factor: 4.677

Review 6.  Discussion on the Rehabilitation of Stroke Hemiplegia Based on Interdisciplinary Combination of Medicine and Engineering.

Authors:  Xiaowei Sun; Ke Xu; Yuqing Shi; Hongtao Li; Ruobing Li; Siyu Yang; Hong Jin; Chuwen Feng; Baitao Li; Chunyue Xing; Yuanyuan Qu; Qingyong Wang; Yinghua Chen; Tiansong Yang
Journal:  Evid Based Complement Alternat Med       Date:  2021-03-17       Impact factor: 2.629

Review 7.  Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases.

Authors:  Dalin Yang; Yong-Il Shin; Keum-Shik Hong
Journal:  Front Neurosci       Date:  2021-03-26       Impact factor: 4.677

Review 8.  Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges.

Authors:  Kostas Nizamis; Alkinoos Athanasiou; Sofia Almpani; Christos Dimitrousis; Alexander Astaras
Journal:  Sensors (Basel)       Date:  2021-03-16       Impact factor: 3.576

9.  Analysis of Prognostic Risk Factors Determining Poor Functional Recovery After Comprehensive Rehabilitation Including Motor-Imagery Brain-Computer Interface Training in Stroke Patients: A Prospective Study.

Authors:  Qiong Wu; Yunxiang Ge; Di Ma; Xue Pang; Yingyu Cao; Xiaofei Zhang; Yu Pan; Tong Zhang; Weibei Dou
Journal:  Front Neurol       Date:  2021-06-10       Impact factor: 4.003

10.  Brain Functional Changes in Stroke Following Rehabilitation Using Brain-Computer Interface-Assisted Motor Imagery With and Without tDCS: A Pilot Study.

Authors:  Mengjiao Hu; Hsiao-Ju Cheng; Fang Ji; Joanna Su Xian Chong; Zhongkang Lu; Weimin Huang; Kai Keng Ang; Kok Soon Phua; Kai-Hsiang Chuang; Xudong Jiang; Effie Chew; Cuntai Guan; Juan Helen Zhou
Journal:  Front Hum Neurosci       Date:  2021-07-16       Impact factor: 3.169

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