Towards physiology-informed data augmentation for EEG-based BCIs

• URL: http://arxiv.org/abs/2203.14392v1
• Date: Sun, 27 Mar 2022 20:59:40 GMT
• Title: Towards physiology-informed data augmentation for EEG-based BCIs
• Authors: Oleksandr Zlatov and Benjamin Blankertz
• Abstract summary: We suggest a novel technique for augmenting the training data by generating new data from the data set at hand. In this manuscript, we explain the method and show first preliminary results for participant-independent motor-imagery classification.
• Score: 24.15108821320151
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Most EEG-based Brain-Computer Interfaces (BCIs) require a considerable amount of training data to calibrate the classification model, owing to the high variability in the EEG data, which manifests itself between participants, but also within participants from session to session (and, of course, from trial to trial). In general, the more complex the model, the more data for training is needed. We suggest a novel technique for augmenting the training data by generating new data from the data set at hand. Different from existing techniques, our method uses backward and forward projection using source localization and a head model to modify the current source dipoles of the model, thereby generating inter-participant variability in a physiologically meaningful way. In this manuscript, we explain the method and show first preliminary results for participant-independent motor-imagery classification. The accuracy was increased when using the proposed method of data augmentation by 13, 6 and 2 percentage points when using a deep neural network, a shallow neural network and LDA, respectively.

Related papers

• Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models [69.06149482021071]
  We propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives.
  arXiv  Detail & Related papers  (2024-06-20T02:20:23Z)
• BEND: Bagging Deep Learning Training Based on Efficient Neural Network Diffusion [56.9358325168226]
  We propose a Bagging deep learning training algorithm based on Efficient Neural network Diffusion (BEND) Our approach is simple but effective, first using multiple trained model weights and biases as inputs to train autoencoder and latent diffusion model. Our proposed BEND algorithm can consistently outperform the mean and median accuracies of both the original trained model and the diffused model.
  arXiv  Detail & Related papers  (2024-03-23T08:40:38Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy [55.014926694758195]
  Entropy and mutual information in neural networks provide rich information on the learning process. We leverage data geometry to access the underlying manifold and reliably compute these information-theoretic measures. We show that they form noise-resistant measures of intrinsic dimensionality and relationship strength in high-dimensional simulated data.
  arXiv  Detail & Related papers  (2023-12-04T01:32:42Z)
• Neuro-GPT: Towards A Foundation Model for EEG [0.04188114563181615]
  We propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model. Foundation model is pre-trained on a large-scale data set using a self-supervised task that learns how to reconstruct masked EEG segments. Experiments demonstrate that applying a foundation model can significantly improve classification performance compared to a model trained from scratch.
  arXiv  Detail & Related papers  (2023-11-07T07:07:18Z)
• Improving self-supervised pretraining models for epileptic seizure detection from EEG data [0.23624125155742057]
  This paper presents various self-supervision strategies to enhance the performance of a time-series based Diffusion convolution neural network (DCRNN) model. The learned weights in the self-supervision pretraining phase can be transferred to the supervised training phase to boost the model's prediction capability.
  arXiv  Detail & Related papers  (2022-06-28T17:15:49Z)
• The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition [27.795763107984286]
  We present a novel automatic data augmentation model, which approximates the distribution of the input data and samples new data from this distribution. Our results show that the augmentation strategy is fast to train and can improve classification accuracy for both neural networks and state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-05-27T11:07:09Z)
• Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface [4.8276709243429]
  Cross-subject application of EEG-based brain-computer interface (BCI) has always been limited by large individual difference and complex characteristics that are difficult to perceive. We propose a cross-subject EEG classification framework with a generative adversarial networks (GANs) based method named common spatial GAN (CS-GAN) Our framework provides a promising way to deal with the cross-subject problem and promote the practical application of BCI.
  arXiv  Detail & Related papers  (2021-02-08T10:37:03Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models [13.56090099952884]
  We propose three methods for augmenting EEG training data to enhance the performance of emotion recognition models. For the full usage strategy, all of the generated data are augmented to the training dataset without judging the quality of the generated data. The experimental results demonstrate that the augmented training datasets produced by our methods enhance the performance of EEG-based emotion recognition models.
  arXiv  Detail & Related papers  (2020-06-04T21:23:09Z)
• Deep transfer learning for improving single-EEG arousal detection [63.52264764099532]
  Two datasets do not contain exactly the same setup leading to degraded performance in single-EEG models. We train a baseline model and replace the first two layers to prepare the architecture for single-channel electroencephalography data. Using a fine-tuning strategy, our model yields similar performance to the baseline model and was significantly better than a comparable single-channel model.
  arXiv  Detail & Related papers  (2020-04-10T16:51:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.