EEG-based Motor Imagery Classification Using Convolutional Neural Network

Lee, David; 박상훈,; Lee, Hee-Jae; Lee, Sang-Goog

doi:10.14801/jkiit.2017.15.6.103

Cited by 4 publications

(9 citation statements)

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“…The SVM was chosen as the representative classical machine learning approach owing to its outstanding performance among the methods. Also, we re-implemented the CNN model [34] in Table 6, which was described in Section 2.3.2 as a CNN baseline model.…”

Section: Resultsmentioning

confidence: 99%

“…A deep learning-based approach such as a CNN generally works well for image understanding and classification; therefore, various attempts have been made to extract 2D image representations from 1D raw signals to solve a time-series classification task with CNNs [34,35]. A common and popular method for this is the short-time Fourier transform (STFT) algorithm, which translates time domain signals into time-frequency domain signals.…”

Section: Methodsmentioning

confidence: 99%

“…Lee et al [34] developed a CNN architecture for decoding two-class motor imagery signals with STFT spectrogram images. First, the STFT is performed with an overlap size of 100 and window size of 128 to obtain 3D data array of size 3 × 65 × 14 from raw EEG signals.…”

Section: Methodsmentioning

confidence: 99%

“…That is, the STFT spectrogram images from three electrode channels resulting in 3 × 10 × 14 vectors are used as input. The CNN architecture proposed in [34] is comprised of three layers. The first layer consists of 37, 6 × 1 2D convolutional kernels with 2D max pooling.…”

Section: Methodsmentioning

confidence: 99%

“…Recent CNN-based approaches for MI EEG classification achieved results that were comparable to or better than those obtained using classical CSP-based approaches, even though they do not require extra steps such as feature engineering [24,32,34]. However, as described in the Introduction, CNNs also have limitations in that their performance is compromised on distorted and unstable data.…”

Section: Methodsmentioning

confidence: 99%

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Motor Imagery EEG Classification Using Capsule Networks

Jeong

2019

Sensors

107

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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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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Motor Imagery EEG Classification Using Capsule Networks

Jeong

2019

Sensors

107

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Collective Almost Synchronization Modeling Used for Motor Imagery EEG Classification

Phuong Nguyen,

Phan,

Hayashi

et al. 2023

Preprint

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Classification based on feature extraction is a crucial technique to develop Brain Computer Interface (BCI) systems. The human brain can be considered as a dynamical system, and its behavior measured by EEG signals can be modeled by a group of nonlinear oscillators. Exploring the dynamical nature of EEG signals along with model based approach may improve classification accuracy in BCI. This study proposes a novel feature extraction method for the classification of Motor Imagery (MI) EEG using a dynamical network model operating in a special collective state, so called Collective Almost Synchronization (CAS). The CAS, the nonlinear oscillators set to operate in a weakly coupled regime, can be used to model an EEG. Purpose of this study is to investigate the performance of the CAS model to identify features for the classification of MI states. To achieve this goal, a linear regression method is used and linear coefficients are extracted as feature vectors. Our approach boils down to identifying patterns in the MI-EEG by associating them to the coefficients of a linear regression (or weights of an output function) constructed to model the MI-EEG signals from simulated time-series generated by a dynamical neural network. The dataset 2b from BCI Competition-IV was used to evaluate the performance of the proposed method. Results indicate that the CAS-based classification method is more robust in extracting distinguishable features from EEG signals as compared with other state-of-the-art methods. The proposed method achieved better performance on two-class MI classification. Moreover, the method developed in this study for MI classification across subjects is effective with 74.03% of the accuracy.

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Decoding Two-Class Motor Imagery EEG with Capsule Networks

Jeong

2019

2019 IEEE International Conference on Big Data and Smart Computing (BigComp)

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EEG-based Motor Imagery Classification Using Convolutional Neural Network

Cited by 4 publications

References 0 publications

Motor Imagery EEG Classification Using Capsule Networks

Motor Imagery EEG Classification Using Capsule Networks

Collective Almost Synchronization Modeling Used for Motor Imagery EEG Classification

Decoding Two-Class Motor Imagery EEG with Capsule Networks

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