Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks

The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of motor imagery EEG types with high accuracy, a classification methodology using the wavelet packet decomposition (WPD) and the proposed deep residual convolutional networks (DRes-CNN) is proposed. Firstly, EEG waveforms are segmented into sub-signals. Then the EEG signal features are obtained through the WPD algorithm, and some selected wavelet coefficients are retained and reconstructed into EEG signals in their respective frequency bands. Subsequently, the reconstructed EEG signals were utilized as input of the proposed deep residual convolutional networks to classify EEG signals. Finally, EEG types of motor imagination are classified by the DRes-CNN classifier intelligently. The datasets from BCI Competition were used to test the performance of the proposed deep learning classifier. Classification experiments show that the average recognition accuracy of this method reaches 98.76%. The proposed method can be further applied to the BCI system of motor imagination control.

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Motor Imagery EEG Classification Using Capsule Networks

Sensors ◽

10.3390/s19132854 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2854 ◽

Cited By ~ 16

Author(s):

Kwon-Woo Ha ◽

Jin-Woo Jeong

Keyword(s):

Motor Imagery ◽

Learning Approaches ◽

Eeg Signals ◽

Left Hand ◽

Computer Interfaces ◽

Conventional Machine ◽

Short Time ◽

Electroencephalogram Eeg ◽

Target Data

Various convolutional neural network (CNN)-based approaches have been recently proposed to improve the performance of motor imagery based-brain-computer interfaces (BCIs). However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery electroencephalogram (EEG), the measured signals, even from the same person, are not consistent and can be significantly distorted. To overcome these limitations, we propose to apply a capsule network (CapsNet) for learning various properties of EEG signals, thereby achieving better and more robust performance than previous CNN methods. The proposed CapsNet-based framework classifies the two-class motor imagery, namely right-hand and left-hand movements. The motor imagery EEG signals are first transformed into 2D images using the short-time Fourier transform (STFT) algorithm and then used for training and testing the capsule network. The performance of the proposed framework was evaluated on the BCI competition IV 2b dataset. The proposed framework outperformed state-of-the-art CNN-based methods and various conventional machine learning approaches. The experimental results demonstrate the feasibility of the proposed approach for classification of motor imagery EEG signals.

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Application of Convolutional Neural Network Method in Brain Computer Interface

Journal of Physics Conference Series ◽

10.1088/1742-6596/2078/1/012044 ◽

2021 ◽

Vol 2078 (1) ◽

pp. 012044

Author(s):

Lingzhi Chen ◽

Wei Deng ◽

Chunjin Ji

Keyword(s):

Pattern Recognition ◽

Recognition Accuracy ◽

Brain Computer Interface ◽

Computer Interface ◽

Eeg Signals ◽

Network Method ◽

Electroencephalogram Eeg ◽

The Brain ◽

Bci System

Abstract Pattern Recognition is the most important part of the brain computer interface (BCI) system. More and more profound learning methods were applied in BCI to increase the overall quality of pattern recognition accuracy, especially in the BCI based on Electroencephalogram (EEG) signal. Convolutional Neural Networks (CNN) holds great promises, which has been extensively employed for feature classification in BCI. This paper will review the application of the CNN method in BCI based on various EEG signals.

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Motor Imagery Classification Using EEG Signals for Brain-Computer Interface Applications

Early Detection of Neurological Disorders Using Machine Learning Systems - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-5225-8567-1.ch013 ◽

2019 ◽

pp. 241-251

Author(s):

Subrota Mazumdar ◽

Rohit Chaudhary ◽

Suruchi Suruchi ◽

Suman Mohanty ◽

Divya Kumari ◽

...

Keyword(s):

Motor Imagery ◽

Nearest Neighbor ◽

Brain Computer Interface ◽

Computer Interface ◽

Test Cases ◽

Multiple Channels ◽

Eeg Signals ◽

Brain Computer Interfacing ◽

Electroencephalogram Eeg

In this chapter, a nearest neighbor (k-NN)-based method for efficient classification of motor imagery using EEG for brain-computer interfacing (BCI) applications has been proposed. Electroencephalogram (EEG) signals are obtained from multiple channels from brain. These EEG signals are taken as input features and given to the k-NN-based classifier to classify motor imagery. More specifically, the chapter gives an outline of the Berlin brain-computer interface that can be operated with minimal subject change. All the design and simulation works are carried out with MATLAB software. k-NN-based classifier is trained with data from continuous signals of EEG channels. After the network is trained, it is tested with various test cases. Performance of the network is checked in terms of percentage accuracy, which is found to be 99.25%. The result suggested that the proposed method is accurate for BCI applications.

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Classification of Sleep Apnea Based on Sub-Band Decomposition of EEG Signals

Diagnostics ◽

10.3390/diagnostics11091571 ◽

2021 ◽

Vol 11 (9) ◽

pp. 1571

Author(s):

Rajeswari Jayaraj ◽

Jagannath Mohan

Keyword(s):

Support Vector Machine ◽

Sleep Apnea ◽

Support Vector Machine Classifier ◽

Wavelet Packet ◽

Brain Perfusion ◽

Support Vector ◽

Eeg Signals ◽

Data Format ◽

Electroencephalogram Eeg

To classify between normal and sleep apnea subjects based on sub-band decomposition of electroencephalogram (EEG) signals. This study comprised 159 subjects obtained from the ISRUC (Institute of System and Robotics—University of Coimbra), Sleep-EDF (European Data Format), and CAP (Cyclic Alternating Pattern) Sleep database, which consists of normal and sleep apnea subjects. The wavelet packet decomposition method was incorporated to categorize the EEG signals into five frequency bands, namely, alpha, beta, delta, gamma, and theta. Entropy and energy (non-linear) for all bands was calculated and as a result, 10 features were obtained for each EEG signal. The ratio of EEG bands included four parameters, including heart rate, brain perfusion, neural activity, and synchronization. In this study, a support vector machine with kernels and random forest classifiers was used for classification. The performance measures demonstrated that the improved results were obtained from the support vector machine classifier with a kernel polynomial order 2. The accuracy (90%), sensitivity (100%), and specificity (83%) with 14 features were estimated using the data obtained from ISRUC database. The proposed study is feasible and seems to be accurate in classifying the subjects with sleep apnea based on the extracted features from EEG signals using a support vector machine classifier.

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Testing the Self-Similarity Exponent to Feature Extraction in Motor Imagery Based Brain Computer Interface Systems

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127415400234 ◽

2015 ◽

Vol 25 (14) ◽

pp. 1540023

Author(s):

Germán Rodríguez-Bermúdez ◽

Miguel Ángel Sánchez-Granero ◽

Pedro J. García-Laencina ◽

Manuel Fernández-Martínez ◽

José Serna ◽

...

Keyword(s):

Feature Extraction ◽

Motor Imagery ◽

Information Acquisition ◽

Brain Computer Interface ◽

Computer Interface ◽

Eeg Signals ◽

Self Similarity ◽

Self Similar ◽

Bci System

A Brain Computer Interface (BCI) system is a tool not requiring any muscle action to transmit information. Acquisition, preprocessing, feature extraction (FE), and classification of electroencephalograph (EEG) signals constitute the main steps of a motor imagery BCI. Among them, FE becomes crucial for BCI, since the underlying EEG knowledge must be properly extracted into a feature vector. Linear approaches have been widely applied to FE in BCI, whereas nonlinear tools are not so common in literature. Thus, the main goal of this paper is to check whether some Hurst exponent and fractal dimension based estimators become valid indicators to FE in motor imagery BCI. The final results obtained were not optimal as expected, which may be due to the fact that the nature of the analyzed EEG signals in these motor imagery tasks were not self-similar enough.

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