Programming an offline-analyzer of motor imagery signals via python language

Brain Computer Interface (BCI) systems control the user's environment via his/her brain signals. Brain signals related to motor imagery (MI) have become a widespread method employed by the BCI community. Despite the large number of references describing the MI signal treatment, there is not eno...

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Bibliographic Details
Published in:2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2011; pp. 7861 - 7864
Main Authors: Alonso-Valerdi, L. M., Sepulveda, F.
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.01.2011
Subjects:
Adult
Computer languages
Electrodes
Electroencephalography
Electroencephalography - instrumentation
Feature extraction
Female
Humans
Imagery (Psychotherapy) - instrumentation
Male
Motor Activity - physiology
Programming Languages
Signal Processing, Computer-Assisted
Software
Support Vector Machine
Support vector machines
Training
ISBN:9781424441211, 1424441218
ISSN:1094-687X, 1557-170X, 2694-0604, 2694-0604
Online Access:Get full text
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Summary:Brain Computer Interface (BCI) systems control the user's environment via his/her brain signals. Brain signals related to motor imagery (MI) have become a widespread method employed by the BCI community. Despite the large number of references describing the MI signal treatment, there is not enough information related to the available programming languages that could be suitable to develop a specific-purpose MI-based BCI. The present paper describes the development of an offline-analysis system based on MI-EEG signals via open-source programming languages, and the assessment of the system using electrical activity recorded from three subjects. The analyzer recognized at least 63% of the MI signals corresponding to three classes. The results of the offline analysis showed a promising performance considering that the subjects have never undergone MI trainings.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISBN:9781424441211
1424441218
ISSN:1094-687X
1557-170X
2694-0604
2694-0604
DOI:10.1109/IEMBS.2011.6091937