Classification of Hand Movements From EEG Using a Deep Attention-Based LSTM Network

Zhang, Guangyi; Davoodnia, Vandad; Sepas-Moghaddam, Alireza; Zhang, Yaoxue; Etemad, Ali

doi:10.1109/jsen.2019.2956998

Cited by 154 publications

(85 citation statements)

References 45 publications

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“…In this paper, we aimed to evaluate the impact of attention mechanisms, when added on well-established DL models in the classification of EEG. We compared three DL architectures: the brand-new InstaGATs, the LSTM+Att [26] and a CNNs+Att [27]. We used these models to classify three different EEG datasets, including normal and abnormal patterns, with further distinction between artifactual and pathological abnormalities.…”

Section: Discussionmentioning

confidence: 99%

“…As the next model, we referred to the network presented in [26], and we implemented the Long Short-Term Memory with Attention (LSTM+Att). Compared to its original version with 3-layer LSTM architecture, we focused on a 2-layer LSTM, to be consistent with the other models considered in this study.…”

Section: Lstm With Attentionmentioning

confidence: 99%

“…Eleven well-established time-domain and frequency-domain features were computed from each EEG frame of each individual EEG channel. Particularly, in the time domain, we extracted the mean, the variance, the zero-crossings, the area under the curve, the skewness, the kurtosis, and the peak-to-peak distance (as listed in [26]). Then, in the frequency domain, we computed the spectral power applying Welch in well-known and clinically relevant frequency bands, i.e., the delta (δ) band corresponding to (0.5, 4) Hz, the theta (θ) band to (4, 8) Hz, the alpha (α) band to (8,12) Hz, and the beta (β) band to (12,30) Hz.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Attention-based Deep LearningModels for EEG Classification

Cisotto

Zanga

Chlebus

et al. 2021

Preprint

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Deep Learning (DL) has recently shown promising classification performance in Electroencephalography (EEG) in many different scenarios. However, the complex reasoning of such models often prevent the user to explain their classification abilities. Attention, one of the most recent and influential ideas in DL, allows the models to learn which portions of the data are relevant to the final classification output. In this work, we compared three attention-enhanced DL models, the brand-new InstaGATs , an LSTM with attention and a CNN with attention. We used these models to classify normal and abnormal, including artifactual and pathological, EEG patterns in three different datasets. We achieved the state of the art in all classification problems, regardless the large variability of the datasets and the simple architecture of the attention-enhanced models. Additionally, we proved that, depending on how the attention mechanism is applied and where the attention layer is located in the model, we can alternatively leverage the information contained in the time, frequency or space domain of the EEG dataset. Therefore, attention represents a promising strategy to evaluate the quality of the EEG information, and its relevance for classification, in different real-world scenarios.

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Section: Discussionmentioning

confidence: 99%

Section: Lstm With Attentionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Attention-based Deep LearningModels for EEG Classification

Cisotto

Zanga

Chlebus

et al. 2021

Preprint

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“…DL has been proved to be very efficient in many complex biomedical tasks [ 22 , 23 , 24 , 25 ], especially in EEG signal aspects. A long short-term memory (LSTM) network works well with time-series information due to its structural dependency [ 26 , 27 ], and the attention mechanism (AM) [ 28 , 29 ] has the ability to focus on the abnormal signals of EEG. In addition, convolutional neural networks (CNNs) can detect and extract relevant features automatically [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation

et al. 2021

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Surgical intervention or the control of drug-refractory epilepsy requires accurate analysis of invasive inspection intracranial EEG (iEEG) data. A multi-branch deep learning fusion model is proposed to identify epileptogenic signals from the epileptogenic area of the brain. The classical approach extracts multi-domain signal wave features to construct a time-series feature sequence and then abstracts it through the bi-directional long short-term memory attention machine (Bi-LSTM-AM) classifier. The deep learning approach uses raw time-series signals to build a one-dimensional convolutional neural network (1D-CNN) to achieve end-to-end deep feature extraction and signal detection. These two branches are integrated to obtain deep fusion features and results. Resampling is employed to split the imbalanced epileptogenic and non-epileptogenic samples into balanced subsets for clinical validation. The model is validated over two publicly available benchmark iEEG databases to verify its effectiveness on a private, large-scale, clinical stereo EEG database. The model achieves high sensitivity (97.78%), accuracy (97.60%), and specificity (97.42%) on the Bern–Barcelona database, surpassing the performance of existing state-of-the-art techniques. It is then demonstrated on a clinical dataset with an average intra-subject accuracy of 92.53% and cross-subject accuracy of 88.03%. The results suggest that the proposed method is a valuable and extremely robust approach to help researchers and clinicians develop an automated method to identify the source of iEEG signals.

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“…al. [7] proposed the use of a Long Short-Term Memory (LSTM) network to classify limb movements from EEG data. A novel approach proposed by Bashivan et.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Multi-Channel Eeg Classification with Gramian Temporal Generative Adversarial Networks

Kan

Povinelli

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Deep learning's requirements for large amounts of training data remains a challenge for researchers and developers. Generative Adversarial Network (GAN) is commonly used in medical image analysis to generate novel training images to help resolve this issue. While deep learning has many clinical applications in radiology, its applications in medical time series data such as electroencephalogram (EEG) are usually constrained to 1 dimension. Hence, there are few available GAN architectures that effectively synthesize single and multi-channel EEG. In this paper, we propose a novel method to synthesize multi-channel EEG in the form of Gramian Angular Field (GAF) images with a Gramian Temporal Generative Adversarial Network (GT-GAN). The proposed network is capable of generating realistic GAF images and enhances EEG anomaly detection accuracy in residual learning frameworks.

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Classification of Hand Movements From EEG Using a Deep Attention-Based LSTM Network

Cited by 154 publications

References 45 publications

Comparison of Attention-based Deep LearningModels for EEG Classification

Comparison of Attention-based Deep LearningModels for EEG Classification

Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation

Enhancing Multi-Channel Eeg Classification with Gramian Temporal Generative Adversarial Networks

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