Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis

المؤلفون المشاركون

Rashid, Nasir
Javed, Amna
Tiwana, Mohsin I.
Khan, Umar Shahbaz
Iqbal, Javaid

المصدر

BioMed Research International

العدد

المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-11، 11ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2018-05-20

دولة النشر

مصر

عدد الصفحات

11

التخصصات الرئيسية

الطب البشري

الملخص 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%.

نمط استشهاد جمعية علماء النفس الأمريكية (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

نمط استشهاد الجمعية الأمريكية للغات الحديثة (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

نمط استشهاد الجمعية الطبية الأمريكية (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

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1125242