Simultaneous classification of motor imagery and SSVEP EEG signals

Quadcopter is an important way for the human to explore the physical world. The brain-computer interface (BCI) technology is used to control the quadcopter flight in order to help disabled persons communicate with the external world freely. In this study, a quadcopter control system using a hybrid BCI based on off-line optimization and enhanced human-machine interaction was designed to control the quadcopter flight in 3D physical space. The proposed system implemented the control of quadcopter moving up/down, forward/backward, left/right by six different SSVEP, and turning left/right by left-hand and right-hand motor imagery. Meanwhile, the optimization of the control system and the human-machine interaction enhancement improved practicability in real-time use. Five subjects participated in an on-line experiment to control the quadcopter flight in real-time. The average classification accuracy of EEG-based commands in the on-line experiment was 87.09±2.82% and information transfer rate (ITR) was 0.857±0.085 bits/min. The results demonstrated the feasibility of multidirectional control of quadcopter flight in 3D space by using hybrid BCI technology and revealed the practicality and operability of the hybrid BCI control system based on off-line optimization and human-machine interaction enhancement. INDEX TERMS Quadcopter control system, motor imagery, steady-state visual evoked potential, off-line optimization, human-machine interaction.

show abstract

“…Electrode impedances were kept under 10 k . To improve the SNR of scalp EEG, a 5-30Hz bandpass filter was applied in EEG recording [24].…”

Section: ) Data Acquisition Devicementioning

confidence: 99%

Quadcopter Control System Using a Hybrid BCI Based on Off-Line Optimization and Enhanced Human-Machine Interaction

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Finally, the output of neuron (a) in the third layer can be described as in (5) Figure.1 Multi-layer of neurons [13] The ANN for classification using Mean Square Error (MSE). The MSE measures the magnitude of the forecast errors as shown in (3).…”

Section: Ann Classificationmentioning

confidence: 99%

Parkinson Disease Detection Based on Voice and EMG Pattern Classification Method for Indonesian Case Study

Putri

Caesarendra

Pamanasari

et al. 2018

JEMMME

View full text Add to dashboard Cite

Parkinson disease (PD) detection using pattern recognition method has been presented in literatures. This paper present multi-class PD detection utilizing voice and electromyography (EMG) features of Indonesian subjects. The multi-class classification consists of healthy control, possible stage, probable stage and definite stage. These stages are based on Hughes scale used in Indonesia for PD. Voice signals were recorded from 15 people with Parkinson (PWP) and 8 healthy control subjects. Voice and EMG data acquistion were conducted in dr Kariadi General Hospital Semarang, Central Java, Indonesia. Twenty two features are used for voice signal feature extraction and twelve features are emploed for EMG signal. Artificial Neural Network is used as classification method. The results of voice classification show that accuracy for testing step of 94.4%. For EMG classification, the accuracy of testing of 71%.

show abstract

“…Linear Discriminant Analysis (LDA) is a very important classifier, which can be used for wide variety of problems. For instance, in different machine learning problems, such as pattern or face recognition, feature extraction and data dimensionality reduction [14].…”

Section: Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

“…The smallest Euclidean distance among all distances classified the test vector as belonging to class n [14].…”

Section: Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

Classifier testing for the brain-machine interface (BCI) based on Steady State Visually Evoked Potential (SSVEP)

Kubacki

Jakubowski

2017

ITM Web Conf.

View full text Add to dashboard Cite

The paper describes the research on the classifiers for brain-computer interface (BCI) based on Steady State Visually Evoked Potential (SSVEP). Authors presented research on the checking the usability of classifiers for recognizing an EEG signal during the stimulus. Three classifiers have been checked: Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and one based on Artificial Neural Network (ANN). First part is concentrated on brain-computer interfaces and classification of them. The second part describes algorithms of all using classifiers. In the next part, authors present test stand and how the experiment is built. The last part consists of results of these tests. The best was the classifier based on Artificial Neural Network -up to 95% of correct identified. The worst results were obtained from Support Vector Machine -about 70%.Corresponding with the Kolmogorov's theorem, ANN structure has two layers with n inputs, 2n+1 neurons in the hidden layer and one output neuron with a linear activation function ϕ. There are very few problems requiring more than two hidden layers.

show abstract

Simultaneous classification of motor imagery and SSVEP EEG signals

Cited by 10 publications

References 10 publications

Quadcopter Control System Using a Hybrid BCI Based on Off-Line Optimization and Enhanced Human-Machine Interaction

Quadcopter Control System Using a Hybrid BCI Based on Off-Line Optimization and Enhanced Human-Machine Interaction

Parkinson Disease Detection Based on Voice and EMG Pattern Classification Method for Indonesian Case Study

Classifier testing for the brain-machine interface (BCI) based on Steady State Visually Evoked Potential (SSVEP)

Contact Info

Product

Resources

About