Domain adaptation for epileptic EEG classification using adversarial learning and Riemannian manifold

Peng, Peizhen; Xie, Liping; Zhang, Kanjian; Zhang, Jinxia; Yang, Lu; Wei, Haikun

doi:10.1016/j.bspc.2022.103555

Cited by 20 publications

(5 citation statements)

References 72 publications

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“…Satarupa et al [9] . [10 ]. proposed a domain adaptation approach, in which adversarial learning and the Riemannian manifold were used to extract the feature space between different epilepsy patients, and the common feature space between different patients was learned by constructing an adversarial autoencoder, and test on the CHB-MIT dataset yielded a sensitivity of 86.4%.…”

Section: Comparison Of Different Seizure Detection Methodsmentioning

confidence: 99%

Deep domain adaptation-based seizure detection method

Qiu¹

2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

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Electroencephalogram (EEG) seizure examination is an important part of epilepsy diagnosis and evaluation. In this paper, a Deep domain adaptation-based seizure detection method is proposed for the automatic detection of epilepsy by transfer learning. First, a Butterworth bandpass filter is used for denoising and the EEG signals in the frequency of 0.5-50Hz are extracted. The filtered EEG signals were then sliced in 4-second segments. In the second step, the EEG signal is Pretreatment processed, and the two-dimensional spectrogram signal is obtained by short-time Fourier variation in a segment every 4 seconds at the same time. Scalp EEG at CHB-MIT was tested for a total of 915.34h, including 29.06 minutes of seizure data. The average sensitivity and specificity were 96.77% and 96.48%, respectively.

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Section: Comparison Of Different Seizure Detection Methodsmentioning

confidence: 99%

Deep domain adaptation-based seizure detection method

Qiu¹

2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

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“…The covariance matrix is usually a symmetric positive definite (SPD) matrix, while the space of the SPD matrix is not a linear Euclidean space but a nonlinear Riemannian manifold. Therefore, an SPD matrix cannot perform Euclidean operations directly and can be operated in two ways, the affine-invariant Riemannian framework and the log-Euclidean Riemannian framework [ 43 , 44 , 45 ]. Both frameworks have good theoretical foundations, but the computational cost of the former is higher than that of the latter.…”

Section: Methodsmentioning

confidence: 99%

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals

et al. 2022

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Electroencephalography (EEG) records the electrical activity of the brain, which is an important tool for the automatic detection of epileptic seizures. It is certainly a very heavy burden to only recognize EEG epilepsy manually, so the method of computer-assisted treatment is of great importance. This paper presents a seizure detection algorithm based on variational modal decomposition (VMD) and a deep forest (DF) model. Variational modal decomposition is performed on EEG recordings, and the first three variational modal functions (VMFs) are selected to construct the time–frequency distribution of the EEG signals. Then, the log−Euclidean covariance matrix (LECM) is computed to represent the EEG properties and form EEG features. The deep forest model is applied to complete the EEG signal classification, which is a non-neural network deep model with a cascade structure that performs feature learning through the forest. In addition, to improve the classification accuracy, postprocessing techniques are performed to generate the discriminant results by moving average filtering and adaptive collar expansion. The algorithm was evaluated on the Bonn EEG dataset and the Freiburg long−term EEG dataset, and the former achieved a sensitivity and specificity of 99.32% and 99.31%, respectively. The mean sensitivity and specificity of this method for the 21 patients in the Freiburg dataset were 95.2% and 98.56%, respectively, with a false detection rate of 0.36/h. These results demonstrate the superior performance advantage of our algorithm and indicate its great research potential in epilepsy detection.

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“…It specifically addresses the challenge of covariance matrices residing in different regions of the manifold, which commonly arises when data is gathered from multiple subjects and/or sessions. Previous applications of this method in the field of EEG include emotion recognition (Wang et al 2021) and seizure detection and prediction (Peng et al 2022).…”

Section: Parallel Transportmentioning

confidence: 99%

Improving EEG-based decoding of the locus of auditory attention through domain adaptation ^*

Wilroth,

Bernhardsson,

Heskebeck

et al. 2023

J. Neural Eng.

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Objective. This paper presents a novel domain adaptation (DA) framework to enhance the accuracy of electroencephalography (EEG)-based auditory attention classification, specifically for classifying the direction (left or right) of attended speech. The framework aims to improve the performances for subjects with initially low classification accuracy, overcoming challenges posed by instrumental and human factors. Limited dataset size, variations in EEG data quality due to factors such as noise, electrode misplacement or subjects, and the need for generalization across different trials, conditions and subjects necessitate the use of DA methods. By leveraging DA methods, the framework can learn from one EEG dataset and adapt to another, potentially resulting in more reliable and robust classification models. Approach. This paper focuses on investigating a DA method, based on parallel transport, for addressing the auditory attention classification problem. The EEG data utilized in this study originates from an experiment where subjects were instructed to selectively attend to one of the two spatially separated voices presented simultaneously. Main results. Significant improvement in classification accuracy was observed when poor data from one subject was transported to the domain of good data from different subjects, as compared to the baseline. The mean classification accuracy for subjects with poor data increased from 45.84% to 67.92%. Specifically, the highest achieved classification accuracy from one subject reached 83.33%, a substantial increase from the baseline accuracy of 43.33%. Significance. The findings of our study demonstrate the improved classification performances achieved through the implementation of DA methods. This brings us a step closer to leveraging EEG in neuro-steered hearing devices.

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Domain adaptation for epileptic EEG classification using adversarial learning and Riemannian manifold

Cited by 20 publications

References 72 publications

Deep domain adaptation-based seizure detection method

Deep domain adaptation-based seizure detection method

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals

Improving EEG-based decoding of the locus of auditory attention through domain adaptation ^*

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Domain adaptation for epileptic EEG classification using adversarial learning and Riemannian manifold

Cited by 20 publications

References 72 publications

Deep domain adaptation-based seizure detection method

Deep domain adaptation-based seizure detection method

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals

Improving EEG-based decoding of the locus of auditory attention through domain adaptation *

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Improving EEG-based decoding of the locus of auditory attention through domain adaptation ^*