Automatic epileptic EEG classification based on differential entropy and attention model

Zhang, Jian; Wei, Zuochen; Zou, Junzhong; Fu, Hao

doi:10.1016/j.engappai.2020.103975

Cited by 29 publications

(13 citation statements)

References 23 publications

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“…Channel-wise attention mechanisms have been adopted in EEG analyses because spatial information in EEG signals collected from multiple channels plays an important role in predicting brain status, such as emotion [ 29 , 30 ]. In seizure detection, because ictal signals are observed predominantly in channels near the focal area, the channel-wise attention mechanisms not only result in a high classification performance but also provide information on the contribution of each channel [ 31 , 32 , 33 ]. Furthermore, attention mechanisms have proved to be efficient in patient-independent seizure detection by exploring the significance of each channel in the classification of different patients [ 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the studies on the classification of epilepsy with EEG have aimed to distinguish seizure-related activity (EEGs from the ictal period) from non-seizure activity (EEGs from the interictal period or non-seizure patients) [ 8 , 14 , 15 , 16 , 17 , 33 , 34 , 35 ]. Although other studies have successfully predicted epilepsy using preictal EEGs, these models were built on the within-patient paradigm, implying that they are likely to perform poorly for a new set of data [ 37 , 38 , 39 , 40 , 41 , 42 , 43 ].…”

Section: Introductionmentioning

confidence: 99%

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Deep Convolutional Gated Recurrent Unit Combined with Attention Mechanism to Classify Pre-Ictal from Interictal EEG with Minimized Number of Channels

Kim

2022

JPM

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The early prediction of epileptic seizures is important to provide appropriate treatment because it can notify clinicians in advance. Various EEG-based machine learning techniques have been used for automatic seizure classification based on subject-specific paradigms. However, because subject-specific models tend to perform poorly on new patient data, a generalized model with a cross-patient paradigm is necessary for building a robust seizure diagnosis system. In this study, we proposed a generalized model that combines one-dimensional convolutional layers (1D CNN), gated recurrent unit (GRU) layers, and attention mechanisms to classify preictal and interictal phases. When we trained this model with ten minutes of preictal data, the average accuracy over eight patients was 82.86%, with 80% sensitivity and 85.5% precision, outperforming other state-of-the-art models. In addition, we proposed a novel application of attention mechanisms for channel selection. The personalized model using three channels with the highest attention score from the generalized model performed better than when using the smallest attention score. Based on these results, we proposed a model for generalized seizure predictors and a seizure-monitoring system with a minimized number of EEG channels.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Gated Recurrent Unit Combined with Attention Mechanism to Classify Pre-Ictal from Interictal EEG with Minimized Number of Channels

Kim

2022

JPM

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“…The DE is an extension of the Shannon entropy and has been widely applied for building the EEG-based ER systems [ 40 ]. In this study, we compute the DE for each classical band defined as follows,

…”

Section: Methodsmentioning

confidence: 99%

Manifold Feature Fusion with Dynamical Feature Selection for Cross-Subject Emotion Recognition

et al. 2021

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Affective computing systems can decode cortical activities to facilitate emotional human–computer interaction. However, personalities exist in neurophysiological responses among different users of the brain–computer interface leads to a difficulty for designing a generic emotion recognizer that is adaptable to a novel individual. It thus brings an obstacle to achieve cross-subject emotion recognition (ER). To tackle this issue, in this study we propose a novel feature selection method, manifold feature fusion and dynamical feature selection (MF-DFS), under transfer learning principle to determine generalizable features that are stably sensitive to emotional variations. The MF-DFS framework takes the advantages of local geometrical information feature selection, domain adaptation based manifold learning, and dynamical feature selection to enhance the accuracy of the ER system. Based on three public databases, DEAP, MAHNOB-HCI and SEED, the performance of the MF-DFS is validated according to the leave-one-subject-out paradigm under two types of electroencephalography features. By defining three emotional classes of each affective dimension, the accuracy of the MF-DFS-based ER classifier is achieved at 0.50–0.48 (DEAP) and 0.46–0.50 (MAHNOBHCI) for arousal and valence emotional dimensions, respectively. For the SEED database, it achieves 0.40 for the valence dimension. The corresponding accuracy is significantly superior to several classical feature selection methods on multiple machine learning models.

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“…The features extracted in the time domain are the Hjorth feature [12], fractal dimension feature [13] and higher-order crossing feature [14]. The features used in the frequency domain are power spectral density (PSD) [15], spectral entropy (SE) [16] and differential entropy [17]. Wavelets and a short-time Fourier transform (STFT) [18] have been used to extract the time-frequency domain features.…”

Section: Introductionmentioning

confidence: 99%

An overlapping sliding window and combined features based emotion recognition system for EEG signals

Garg

Patro

Behera

et al. 2021

ACI

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PurposeThe purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.Design/methodology/approachClassical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.FindingsThe two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.Originality/valueThe present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

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Automatic epileptic EEG classification based on differential entropy and attention model

Cited by 29 publications

References 23 publications

Deep Convolutional Gated Recurrent Unit Combined with Attention Mechanism to Classify Pre-Ictal from Interictal EEG with Minimized Number of Channels

Deep Convolutional Gated Recurrent Unit Combined with Attention Mechanism to Classify Pre-Ictal from Interictal EEG with Minimized Number of Channels

Manifold Feature Fusion with Dynamical Feature Selection for Cross-Subject Emotion Recognition

An overlapping sliding window and combined features based emotion recognition system for EEG signals

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