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

BioMed Research International

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

Medicine

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