Journal Article10.1088/1741-2552/ad200e
Data augmentation for invasive brain-computer interfaces based on stereo-electroencephalography (SEEG).
Xiaolong Wu,Guangye Li,Xin Gao,Benjamin Metcalfe,Liang Chen +4 more
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TL;DR: This paper demonstrated that a generative model that preserves temporal dependence is superior in data generation and boosting deep learning performance for SEEG signals, the first time that DA methods are applied to invasive BCIs based on SEEG.
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Abstract: 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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Citations
Robust Detection of Brain Stimulation Artifacts in iEEG Using Autoencoder-Generated Signals and ResNet Classification
Jeremy Saal,Ankit N. Khambhati,Edward F. Chang,Prasad Shirvalkar +3 more
- 02 Oct 2024
TL;DR: Researchers developed a supervised method using autoencoder-generated signals and ResNet classification to detect stimulation-induced noise in iEEG recordings, achieving high accuracy and precision with AUC values >0.99 on real data from five participants.
Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia
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TL;DR: Optimizing rare disease gait classification through data balancing and generative AI: ctGAN significantly improved classification performance compared with the original dataset and traditional data augmentation methods.
References
•Proceedings Article
Attention is All you Need
Ashish Vaswani,Noam Shazeer,Niki Parmar,Jakob Uszkoreit,Llion Jones,Aidan N. Gomez,Lukasz Kaiser,Illia Polosukhin +7 more
- 12 Jun 2017
TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.
Attention Is All You Need
Ashish Vaswani,Noam Shazeer,Niki Parmar,Jakob Uszkoreit,Llion Jones,Aidan N. Gomez,Łukasz Kaiser,Illia Polosukhin +7 more
- 01 Jan 2017
Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
51.8K
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
- 08 Dec 2014
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
•Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
•Posted Content
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy,Lucas Beyer,Alexander Kolesnikov,Dirk Weissenborn,Xiaohua Zhai,Thomas Unterthiner,Mostafa Dehghani,Matthias Minderer,Georg Heigold,Sylvain Gelly,Jakob Uszkoreit,Neil Houlsby +11 more
TL;DR: Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.