Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis
Joint Authors
Rashid, Nasir
Javed, Amna
Tiwana, Mohsin I.
Khan, Umar Shahbaz
Iqbal, Javaid
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-05-20
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals.
These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes.
This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals.
This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement).
Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal.
The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers.
Mu (commonly known as alpha waves) and Beta Rhythms (8–30 Hz) containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.
American Psychological Association (APA)
Rashid, Nasir& Iqbal, Javaid& Javed, Amna& Tiwana, Mohsin I.& Khan, Umar Shahbaz. 2018. Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis. BioMed Research International،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1125242
Modern Language Association (MLA)
Rashid, Nasir…[et al.]. Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis. BioMed Research International No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1125242
American Medical Association (AMA)
Rashid, Nasir& Iqbal, Javaid& Javed, Amna& Tiwana, Mohsin I.& Khan, Umar Shahbaz. Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1125242
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1125242