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

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%.

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Electromyogram Whitening for Improved Classification Accuracy in Upper Limb Prosthesis Control

IEEE Transactions on Neural Systems and Rehabilitation Engineering ◽

10.1109/tnsre.2013.2243470 ◽

2013 ◽

Vol 21 (5) ◽

pp. 767-774 ◽

Cited By ~ 37

Author(s):

Lukai Liu ◽

Pu Liu ◽

Edward A. Clancy ◽

Erik Scheme ◽

Englehart

Keyword(s):

Upper Limb ◽

Classification Accuracy ◽

Upper Limb Prosthesis ◽

Prosthesis Control ◽

Limb Prosthesis

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Classification of Hand Movements Using MYO Armband on an Embedded Platform

Electronics ◽

10.3390/electronics10111322 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1322

Author(s):

Haider Ali Javaid ◽

Mohsin Islam Tiwana ◽

Ahmed Alsanad ◽

Javaid Iqbal ◽

Muhammad Tanveer Riaz ◽

...

Keyword(s):

Embedded System ◽

Upper Limb ◽

Hand Movement ◽

Hand Movements ◽

Support Vector ◽

K Nearest Neighbors ◽

Classification Approach ◽

Upper Limb Prosthesis ◽

Gesture Control ◽

Limb Prosthesis

The study proposed the classification and recognition of hand gestures using electromyography (EMG) signals for controlling the upper limb prosthesis. In this research, the EMG signals were measured through an embedded system by wearing a band of MYO gesture control. In order to observe the behavior of these change movements, the EMG data was acquired from 10 healthy subjects (five male and five females) performing four upper limb movements. After extracting EMG data from MYO, the supervised classification approach was applied to recognize the different hand movements. The classification was performed with a 5-fold cross-validation technique under the supervision of Quadratic discriminant analysis (QDA), support vector machine (SVM), random forest, gradient boosted, ensemble (bagged tree), and ensemble (subspace K-Nearest Neighbors) classifier. The execution of these classifiers shows the overall accuracy of 83.9% in the case of ensemble (bagged tree) which is higher than other classifiers. Additionally, in this research an embedded system-based classification approach of hand movement was used for designing an upper limb prosthesis. This approach is different than previous techniques as MYO is used with an external Bluetooth module and different libraries that make its movement and performance boundless. The results of this study also inferred the operations which were easy for hand recognition and can be used for developing a powerful, efficient, and flexible prosthetic design in the future.

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