Literature DB >> 29060545

Stroke lesion location influences the decoding of movement intention from EEG.

Eduardo Lopez-Larraz, Andreas M Ray, Thiago C Figueiredo, Carlos Bibian, Niels Birbaumer, Ander Ramos-Murguialday.   

Abstract

Recent studies have demonstrated the efficacy of brain-machine interfaces (BMI) for motor rehabilitation after stroke, especially for those patients with severe paralysis. However, a cerebro-vascular accident can affect the brain in many different manners, and lesions in diverse areas, even from significantly different volumes, can lead to similar or equal motor deficits. The location of the insult influences the way the brain activates when moving or attempting to move a paralyzed limb. Since the essence of a rehabilitative BMI is to precisely decode motor commands from the brain, it is crucial to characterize how lesion location affects the measured signals and if and how it influences BMI performance. This paper compares the performances of an electroencephalography (EEG)-based movement intention decoder in two groups of severely paralyzed chronic stroke patients: 14 with subcortical lesions and 14 with mixed (i.e., cortical and subcortical) lesions. We show that the lesion location influences the performance of the BMI when decoding the movement attempts of the paretic arm. The obtained results underline the need for further developments for a better individualization of BMI-based rehabilitative therapies for stroke patients.

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Year:  2017        PMID: 29060545     DOI: 10.1109/EMBC.2017.8037504

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  4 in total

1.  Event-related desynchronization during movement attempt and execution in severely paralyzed stroke patients: An artifact removal relevance analysis.

Authors:  Eduardo López-Larraz; Thiago C Figueiredo; Ainhoa Insausti-Delgado; Ulf Ziemann; Niels Birbaumer; Ander Ramos-Murguialday
Journal:  Neuroimage Clin       Date:  2018-10-04       Impact factor: 4.881

2.  Reconstructing Synergy-Based Hand Grasp Kinematics from Electroencephalographic Signals.

Authors:  Dingyi Pei; Parthan Olikkal; Tülay Adali; Ramana Vinjamuri
Journal:  Sensors (Basel)       Date:  2022-07-18       Impact factor: 3.847

Review 3.  Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation.

Authors:  Colin Simon; David A E Bolton; Niamh C Kennedy; Surjo R Soekadar; Kathy L Ruddy
Journal:  Front Neurosci       Date:  2021-07-02       Impact factor: 4.677

4.  On the design of EEG-based movement decoders for completely paralyzed stroke patients.

Authors:  Martin Spüler; Eduardo López-Larraz; Ander Ramos-Murguialday
Journal:  J Neuroeng Rehabil       Date:  2018-11-20       Impact factor: 4.262

  4 in total

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