A Contrastive Learning Based Convolutional Neural Network for ERP Brain-Computer Interfaces

• URL: http://arxiv.org/abs/2407.04738v1
• Date: Tue, 2 Jul 2024 08:20:52 GMT
• Title: A Contrastive Learning Based Convolutional Neural Network for ERP Brain-Computer Interfaces
• Authors: Yuntian Cui, Xinke Shen, Dan Zhang, Chen Yang,
• Abstract summary: Cross-subject ERP signal detection has been challenging due to the complexity of ERP signal components. This brief proposes a contrastive learning training framework and an Inception module to extract multi-scale temporal and spatial features.
• Score: 3.8300351196425146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: ERP-based EEG detection is gaining increasing attention in the field of brain-computer interfaces. However, due to the complexity of ERP signal components, their low signal-to-noise ratio, and significant inter-subject variability, cross-subject ERP signal detection has been challenging. The continuous advancement in deep learning has greatly contributed to addressing this issue. This brief proposes a contrastive learning training framework and an Inception module to extract multi-scale temporal and spatial features, representing the subject-invariant components of ERP signals. Specifically, a base encoder integrated with a linear Inception module and a nonlinear projector is used to project the raw data into latent space. By maximizing signal similarity under different targets, the inter-subject EEG signal differences in latent space are minimized. The extracted spatiotemporal features are then used for ERP target detection. The proposed algorithm achieved the best AUC performance in single-trial binary classification tasks on the P300 dataset and showed significant optimization in speller decoding tasks compared to existing algorithms.

Related papers

• Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning [52.904670248426626]
  This paper studies an over-the-air federated edge learning (Air-FEEL) system with integrated sensing, communication, and computation.<n>We derive a low-complexity I SCC algorithm by alternately optimizing the batch size control and the network resource allocation.
  arXiv  Detail & Related papers  (2025-08-21T02:46:46Z)
• DNN-Based Precoding in RIS-Aided mmWave MIMO Systems With Practical Phase Shift [56.04579258267126]
  This paper investigates maximizing the throughput of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with obstructed direct communication paths.<n>A reconfigurable intelligent surface (RIS) is employed to enhance transmissions, considering mmWave characteristics related to line-of-sight (LoS) and multipath effects.<n>Deep neural network (DNN) is developed to facilitate faster codeword selection.
  arXiv  Detail & Related papers  (2025-07-03T17:35:06Z)
• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  In neuromorphic computing, spiking neural networks (SNNs) perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have demonstrated that embedding a few bits of payload in each spike exchanged between the spiking neurons can further enhance inference accuracy. This paper investigates a wireless neuromorphic split computing architecture employing multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification [1.7465786776629872]
  We propose a Dynamic Domain Adaptation Based Deep Learning Network (DADL-Net) First, the EEG data is mapped to the three-dimensional geometric space and its temporal-spatial features are learned through the 3D convolution module. The accuracy rates of 70.42% and 73.91% were achieved on the OpenBMI and BCIC IV 2a datasets.
  arXiv  Detail & Related papers  (2023-09-21T01:34:00Z)
• ETLP: Event-based Three-factor Local Plasticity for online learning with neuromorphic hardware [105.54048699217668]
  We show a competitive performance in accuracy with a clear advantage in the computational complexity for Event-Based Three-factor Local Plasticity (ETLP) We also show that when using local plasticity, threshold adaptation in spiking neurons and a recurrent topology are necessary to learntemporal patterns with a rich temporal structure.
  arXiv  Detail & Related papers  (2023-01-19T19:45:42Z)
• An intertwined neural network model for EEG classification in brain-computer interfaces [0.6696153817334769]
  The brain computer interface (BCI) is a nonstimulatory direct and occasionally bidirectional communication link between the brain and a computer or an external device. We present a deep neural network architecture specifically engineered to provide state-of-the-art performance in multiclass motor imagery classification.
  arXiv  Detail & Related papers  (2022-08-04T09:00:34Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems [82.2513703281725]
  Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices. We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
  arXiv  Detail & Related papers  (2020-03-18T05:56:27Z)

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.