Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification

• URL: http://arxiv.org/abs/2106.10633v1
• Date: Sun, 20 Jun 2021 06:22:16 GMT
• Title: Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification
• Authors: Michela C. Massi, Francesca Ieva
• Abstract summary: We introduce an algorithm for subject-independent channel selection of EEG recordings. It exploits channel-specific 1D-Convolutional Neural Networks (1D-CNNs) as feature extractors in a supervised fashion to maximize class separability. After training, the algorithm can be exploited by transferring only the parametrized subgroup of selected channel-specific 1D-CNNs to new signals from new subjects.
• Score: 0.3553493344868413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: EEG technology finds applications in several domains. Currently, most EEG systems require subjects to wear several electrodes on the scalp to be effective. However, several channels might include noisy information, redundant signals, induce longer preparation times and increase computational times of any automated system for EEG decoding. One way to reduce the signal-to-noise ratio and improve classification accuracy is to combine channel selection with feature extraction, but EEG signals are known to present high inter-subject variability. In this work we introduce a novel algorithm for subject-independent channel selection of EEG recordings. Considering multi-channel trial recordings as statistical units and the EEG decoding task as the class of reference, the algorithm (i) exploits channel-specific 1D-Convolutional Neural Networks (1D-CNNs) as feature extractors in a supervised fashion to maximize class separability; (ii) it reduces a high dimensional multi-channel trial representation into a unique trial vector by concatenating the channels' embeddings and (iii) recovers the complex inter-channel relationships during channel selection, by exploiting an ensemble of AutoEncoders (AE) to identify from these vectors the most relevant channels to perform classification. After training, the algorithm can be exploited by transferring only the parametrized subgroup of selected channel-specific 1D-CNNs to new signals from new subjects and obtain low-dimensional and highly informative trial vectors to be fed to any classifier.

Related papers

• Optimizing Brain-Computer Interface Performance: Advancing EEG Signals Channel Selection through Regularized CSP and SPEA II Multi-Objective Optimization [13.981941979747305]
  RCSP exhibits efficacy in the discrimination and classification of EEG signals. We employ a state-of-the-art approach in the selection of a subset of channels from a multichannel EEG signal with MI tasks.
  arXiv  Detail & Related papers  (2024-04-26T00:04:41Z)
• Novel entropy difference-based EEG channel selection technique for automated detection of ADHD [9.974570957415212]
  This paper presents an automated approach for ADHD detection using the proposed entropy difference (EnD)-based encephalogram (EEG) channel selection approach. In the proposed approach, we selected the most significant EEG channels for the accurate identification of ADHD. Our proposed approach yielded the highest accuracy of 99.29% using the public database.
  arXiv  Detail & Related papers  (2024-04-15T06:31:12Z)
• Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets [53.367212596352324]
  We propose an unsupervised approach leveraging EEG signal physics. We map EEG channels to fixed positions using field, source-free domain adaptation. Our method demonstrates robust performance in brain-computer interface (BCI) tasks and potential biomarker applications.
  arXiv  Detail & Related papers  (2024-03-07T16:17:33Z)
• End-to-end learnable EEG channel selection with deep neural networks [72.21556656008156]
  We propose a framework to embed the EEG channel selection in the neural network itself. We deal with the discrete nature of this new optimization problem by employing continuous relaxations of the discrete channel selection parameters. This generic approach is evaluated on two different EEG tasks.
  arXiv  Detail & Related papers  (2021-02-11T13:44:07Z)
• Learning from Heterogeneous EEG Signals with Differentiable Channel Reordering [51.633889765162685]
  CHARM is a method for training a single neural network across inconsistent input channels. We perform experiments on four EEG classification datasets and demonstrate the efficacy of CHARM.
  arXiv  Detail & Related papers  (2020-10-21T12:32:34Z)
• Deep Learning Based Antenna Selection for Channel Extrapolation in FDD Massive MIMO [54.54508321463112]
  In massive multiple-input multiple-output (MIMO) systems, the large number of antennas would bring a great challenge for the acquisition of the accurate channel state information. We utilize the neural networks (NNs) to capture the inherent connection between the uplink and downlink channel data sets and extrapolate the downlink channels from a subset of the uplink channel state information. We study the antenna subset selection problem in order to achieve the best channel extrapolation and decrease the data size of NNs.
  arXiv  Detail & Related papers  (2020-09-03T13:38:52Z)
• Selection of Proper EEG Channels for Subject Intention Classification Using Deep Learning [12.497603617622906]
  Brain signals could be used to control devices to assist individuals with disabilities. Different approaches have tried to reduce the number of channels before sending them to a classifier. We are proposing a deep learning-based method for selecting an informative subset of channels that produce high classification accuracy.
  arXiv  Detail & Related papers  (2020-07-24T20:40:10Z)
• Data-Driven Symbol Detection via Model-Based Machine Learning [117.58188185409904]
  We review a data-driven framework to symbol detection design which combines machine learning (ML) and model-based algorithms. In this hybrid approach, well-known channel-model-based algorithms are augmented with ML-based algorithms to remove their channel-model-dependence. Our results demonstrate that these techniques can yield near-optimal performance of model-based algorithms without knowing the exact channel input-output statistical relationship.
  arXiv  Detail & Related papers  (2020-02-14T06:58:27Z)
• DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection [98.43451011898212]
  In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging. We propose a data-driven implementation of the iterative soft interference cancellation (SIC) algorithm which we refer to as DeepSIC. DeepSIC learns to carry out joint detection from a limited set of training samples without requiring the channel to be linear.
  arXiv  Detail & Related papers  (2020-02-08T18:31:00Z)

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.