E2SGAN: EEG-to-SEEG translation with generative adversarial networks

Hu, Mengqi; Chen, Jin; Jiang, Shize; Ji, Wendi; Mei, Shuhao; Chen, Liang; Wang, Xiaoling

doi:10.3389/fnins.2022.971829

Cited by 4 publications

(3 citation statements)

References 44 publications

(55 reference statements)

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“…Another study used a similar random selection strategy to enhance the classification of seizure onset zone (SOZ) [71]. It was even possible to augment invasive data using noninvasive data, as demonstrated in a recent study [34],…”

Section: Gan On Eeg Signalsmentioning

confidence: 99%

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Data augmentation for invasive brain–computer interfaces based on stereo-electroencephalography (SEEG)

Wu,

Zhang,

et al. 2024

J. Neural Eng.

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Abstract—Objective: : Deep learning is increasingly used for Brain-computer interfaces (BCIs). However, the available brain data is sparse, especially for invasive BCIs, which can dramatically deteriorate deep learning performance. Data augmentation methods (DA), such as generative models, can help to address this issue. However, existing studies on brain signals relied on convolutional neural networks (CNNs) and ignored the temporal dependence. This paper tried to enhance the generative model by capturing the temporal relationship from a time-series perspective.Methods: A conditional generative network (cTGAN) based on the transformer model is proposed, and tested on the Stereo- electroencephalography (SEEG) data which was recorded from eight epileptic patients performing five different movements. Three other commonly-used DA methods were also implemented: noise injection (NI), variational autoencoder (VAE) and conditional Wasserstein GAN with gradient penalty (cWGANGP). Artificial SEEG data was generated, and various metrics were used to compare the data quality, including visual inspection, Cosine similarity (CS), Jensen-Shannon distance (JSD) and the effect on the performance of a deep learning-based classifier.Result: Both the proposed cTGAN and the cWGANGP methods were able to generate realistic data, while NI and VAE output inferior samples when visualised as raw sequences and in a lower dimensional space. The cTGAN generated the best samples in terms of cosine similarity and Jensen-Shannon distance and outperformed cWGANGP significantly in enhancing the performance of a deep learning-based classifier (each of them yielding a significant improvement of 6% and 3.4%, respectively).Conclusion: This paper demonstrated that a generative model that preserves temporal dependence is superior in data generation and boosting deep learning performance for SEEG signals.Significance: This is the first time that DA methods are applied to invasive BCIs based on SEEG. In addition, this study demonstrated the advantages of the model that preserves the temporal dependence from a time-series perspective.

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Section: Gan On Eeg Signalsmentioning

confidence: 99%

“…Recently, DA has been applied to non-invasive BCIs utilising EEG [28][29][30][31][32][33][34], and even to an invasive spike-based study involving animals [35]. However, to the best of our knowledge, no study has explored DA in the context of invasive BCIs with human subjects.…”

Section: Introductionmentioning

confidence: 99%

Data augmentation for invasive brain–computer interfaces based on stereo-electroencephalography (SEEG)

Wu,

Zhang,

et al. 2024

J. Neural Eng.

Self Cite

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“…Deep neural networks comprised of residual networks (ResNet) and long short-term memory (LSTM) were utilized in this task to map EEG to mesoscale neural activity, achieving a mean correlation of 0.83 comparing the generated signals to the ground-truth [ 56 ]. Other studies explored using autoencoders, vanilla GANs, and conditional GANs to map EEG to invasively recorded intracranial EEG [ 57 , 58 ]. Results reported by Abdi-Sargezeh et al demonstrated the utility of using GANs to detect interictal epileptiform discharges with an accuracy reaching 76%, outperforming previous approaches [ 59 ].…”

Section: Introductionmentioning

confidence: 99%

On the role of generative artificial intelligence in the development of brain-computer interfaces

Eldawlatly

2024

BMC biomed eng

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Since their inception more than 50 years ago, Brain-Computer Interfaces (BCIs) have held promise to compensate for functions lost by people with disabilities through allowing direct communication between the brain and external devices. While research throughout the past decades has demonstrated the feasibility of BCI to act as a successful assistive technology, the widespread use of BCI outside the lab is still beyond reach. This can be attributed to a number of challenges that need to be addressed for BCI to be of practical use including limited data availability, limited temporal and spatial resolutions of brain signals recorded non-invasively and inter-subject variability. In addition, for a very long time, BCI development has been mainly confined to specific simple brain patterns, while developing other BCI applications relying on complex brain patterns has been proven infeasible. Generative Artificial Intelligence (GAI) has recently emerged as an artificial intelligence domain in which trained models can be used to generate new data with properties resembling that of available data. Given the enhancements observed in other domains that possess similar challenges to BCI development, GAI has been recently employed in a multitude of BCI development applications to generate synthetic brain activity; thereby, augmenting the recorded brain activity. Here, a brief review of the recent adoption of GAI techniques to overcome the aforementioned BCI challenges is provided demonstrating the enhancements achieved using GAI techniques in augmenting limited EEG data, enhancing the spatiotemporal resolution of recorded EEG data, enhancing cross-subject performance of BCI systems and implementing end-to-end BCI applications. GAI could represent the means by which BCI would be transformed into a prevalent assistive technology, thereby improving the quality of life of people with disabilities, and helping in adopting BCI as an emerging human-computer interaction technology for general use.

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The increasing instance of negative emotion reduce the performance of emotion recognition

Wang,

Zhao,

Pei

et al. 2023

Front. Hum. Neurosci.

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IntroductionEmotion recognition plays a crucial role in affective computing. Recent studies have demonstrated that the fuzzy boundaries among negative emotions make recognition difficult. However, to the best of our knowledge, no formal study has been conducted thus far to explore the effects of increased negative emotion categories on emotion recognition.MethodsA dataset of three sessions containing consistent non-negative emotions and increased types of negative emotions was designed and built which consisted the electroencephalogram (EEG) and the electrocardiogram (ECG) recording of 45 participants.ResultsThe results revealed that as negative emotion categories increased, the recognition rates decreased by more than 9%. Further analysis depicted that the discriminative features gradually reduced with an increase in the negative emotion types, particularly in the θ, α, and β frequency bands.DiscussionThis study provided new insight into the balance of emotion-inducing stimuli materials.

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E2SGAN: EEG-to-SEEG translation with generative adversarial networks

Cited by 4 publications

References 44 publications

Data augmentation for invasive brain–computer interfaces based on stereo-electroencephalography (SEEG)

Data augmentation for invasive brain–computer interfaces based on stereo-electroencephalography (SEEG)

On the role of generative artificial intelligence in the development of brain-computer interfaces

The increasing instance of negative emotion reduce the performance of emotion recognition

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