Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space
Joint Authors
Athanasiou, Alkinoos
Kugiumtzis, Dimitris
Pandria, Niki
Xygonakis, Ioannis
Bamidis, Panagiotis D.
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-01
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Brain-Computer Interface (BCI) is a rapidly developing technology that aims to support individuals suffering from various disabilities and, ultimately, improve everyday quality of life.
Sensorimotor rhythm-based BCIs have demonstrated remarkable results in controlling virtual or physical external devices but they still face a number of challenges and limitations.
Main challenges include multiple degrees-of-freedom control, accuracy, and robustness.
In this work, we develop a multiclass BCI decoding algorithm that uses electroencephalography (EEG) source imaging, a technique that maps scalp potentials to cortical activations, to compensate for low spatial resolution of EEG.
Spatial features were extracted using Common Spatial Pattern (CSP) filters in the cortical source space from a number of selected Regions of Interest (ROIs).
Classification was performed through an ensemble model, based on individual ROI classification models.
The evaluation was performed on the BCI Competition IV dataset 2a, which features 4 motor imagery classes from 9 participants.
Our results revealed a mean accuracy increase of 5.6% with respect to the conventional application method of CSP on sensors.
Neuroanatomical constraints and prior neurophysiological knowledge play an important role in developing source space-based BCI algorithms.
Feature selection and classifier characteristics of our implementation will be explored to raise performance to current state-of-the-art.
American Psychological Association (APA)
Xygonakis, Ioannis& Athanasiou, Alkinoos& Pandria, Niki& Kugiumtzis, Dimitris& Bamidis, Panagiotis D.. 2018. Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1130840
Modern Language Association (MLA)
Xygonakis, Ioannis…[et al.]. Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1130840
American Medical Association (AMA)
Xygonakis, Ioannis& Athanasiou, Alkinoos& Pandria, Niki& Kugiumtzis, Dimitris& Bamidis, Panagiotis D.. Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1130840
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130840