Controlling the artificial hand using the mind is a dream for many people who had lost their limbs. Brain-Computer Interface (BCI) technology is hoped in making these things happen by connecting commands and responses to the brain as information in the control system. However, the complexity of the EEG signal becomes a challenge in realizing. The use of a deep learning-based classification model is expected to be a solution for classifying the hand movements imagined by the user as an input to the electric artificial hand control system. The main aim of this study is to classify EEG signals from the human brain in real-time using a non-invasive EEG headset for two different hand operations: rest and grip. OpenBCI Ultracortex Mark IV Headset was used in this study. This study proposes a solution for the classification of rest and grip hand movement by exploiting a Long Short-Term Memory (LSTM) network and Convolutional Neural Network (CNN) to learn the electroencephalogram (EEG) time-series information. EEG signals were recorded from 1 healthy subject via brain waves at specific locations on the scalp, at points F3, Fz, F4, FC1, FC2, C3, CZ, C3. A wide range of time-domain features are extracted from the EEG signals and used to train an LSTM and CNN to perform the classification task. This headset can capture brain waves that include artefacts such as limb movement, heartbeat, blink, and many more. Raw EEG from the headset was processed for event detection. Raw EEG from the headset was filtered using Butterworth bandpass filtering to separate the signal data into a new dataset containing alpha, beta, and both ranges. The results of this study indicate that the classification model using the CNN technique for the classification of two types of hand movements is able to achieve an accuracy of 95.45% at the highest, while the LSTM technique can achieve an accuracy of 93.64 %. Detected events were then used to trigger control signals to a prosthetic hand controlled by microcontroller.
Skip Nav Destination
,
,
Article navigation
16 August 2022
THE 6TH BIOMEDICAL ENGINEERING’S RECENT PROGRESS IN BIOMATERIALS, DRUGS DEVELOPMENT, AND MEDICAL DEVICES: Proceedings of the 6th International Symposium of Biomedical Engineering (ISBE) 2021
7–8 July 2021
Depok, Indonesia
Research Article|
August 16 2022
Analysis of motor imagery data from EEG device to move prosthetic hands by using deep learning classification Available to Purchase
Agung Shamsuddin Saragih;
Agung Shamsuddin Saragih
a)
1
Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia
, Kampus UI Depok, West Java 16424 Indonesia
a)Corresponding author: [email protected]
Search for other works by this author on:
Hadyan Nasran Basyiri;
Hadyan Nasran Basyiri
b)
1
Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia
, Kampus UI Depok, West Java 16424 Indonesia
Search for other works by this author on:
Muhammad Yusuf Raihan
Muhammad Yusuf Raihan
c)
1
Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia
, Kampus UI Depok, West Java 16424 Indonesia
Search for other works by this author on:
Agung Shamsuddin Saragih
1,a)
Hadyan Nasran Basyiri
1,b)
Muhammad Yusuf Raihan
1,c)
1
Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia
, Kampus UI Depok, West Java 16424 Indonesia
AIP Conf. Proc. 2537, 050009 (2022)
Citation
Agung Shamsuddin Saragih, Hadyan Nasran Basyiri, Muhammad Yusuf Raihan; Analysis of motor imagery data from EEG device to move prosthetic hands by using deep learning classification. AIP Conf. Proc. 16 August 2022; 2537 (1): 050009. https://doi.org/10.1063/5.0098178
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
The implementation of reflective assessment using Gibbs’ reflective cycle in assessing students’ writing skill
Lala Nurlatifah, Pupung Purnawarman, et al.
Effect of coupling agent type on the self-cleaning and anti-reflective behaviour of advance nanocoating for PV panels application
Taha Tareq Mohammed, Hadia Kadhim Judran, et al.
Design of a 100 MW solar power plant on wetland in Bangladesh
Apu Kowsar, Sumon Chandra Debnath, et al.
Related Content
Smart home based on BCI for disabled people: A state-of-the-art review
AIP Conf. Proc. (October 2024)
Data acquisition instrument for EEG based on embedded system
AIP Conf. Proc. (February 2017)
Data acquisition system of 16-channel EEG based on ATSAM3X8E ARM Cortex-M3 32-bit microcontroller and ADS1299
AIP Conf. Proc. (July 2017)
Design of BCI motor imagery classification using WPT-CSP and CNN
AIP Conf. Proc. (August 2022)
Robotic head-neck brace control through EEG signals and BCI sensors using the Savitzky-Golay filter
AIP Conf. Proc. (March 2025)