A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery

• URL: http://arxiv.org/abs/2008.11227v1
• Date: Tue, 25 Aug 2020 18:23:50 GMT
• Title: A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery
• Authors: Ce Zhang, Azim Eskandarian
• Abstract summary: Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram (EEG) motor imagery (MI) This study modifies the conventional CSP algorithm to improve the multi-class MI classification accuracy and ensure the computation process is efficient.
• Score: 164.93739293097605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram (EEG) motor imagery (MI). This study modifies the conventional CSP algorithm to improve the multi-class MI classification accuracy and ensure the computation process is efficient. The EEG MI data is gathered from the Brain-Computer Interface (BCI) Competition IV. At first, a bandpass filter and a time-frequency analysis are performed for each experiment trial. Then, the optimal EEG signals for every experiment trials are selected based on the signal energy for CSP feature extraction. In the end, the extracted features are classified by three classifiers, linear discriminant analysis (LDA), na\"ive Bayes (NVB), and support vector machine (SVM), in parallel for classification accuracy comparison. The experiment results show the proposed algorithm average computation time is 37.22% less than the FBCSP (1st winner in the BCI Competition IV) and 4.98% longer than the conventional CSP method. For the classification rate, the proposed algorithm kappa value achieved 2nd highest compared with the top 3 winners in BCI Competition IV.

Related papers

• Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding [2.0721229324537833]
  We propose a novel decoding architecture network with a dual-branch temporal-spectral-spatial transformer (Dual-TSST) Our proposed Dual-TSST performs superiorly in various tasks, which achieves the promising EEG classification performance of average accuracy of 80.67%. This study provides a new approach to high-performance EEG decoding, and has great potential for future CNN-Transformer based applications.
  arXiv  Detail & Related papers  (2024-09-05T05:08:43Z)
• A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems [0.14999444543328289]
  This study aims to enhance BCI applications for individuals with motor impairments by comparing the effectiveness of tripolar EEG (tEEG) with conventional EEG. The goal is to determine which EEG technology is more effective in processing and translating grasp related neural signals.
  arXiv  Detail & Related papers  (2024-01-31T23:35:44Z)
• Improved Motor Imagery Classification Using Adaptive Spatial Filters Based on Particle Swarm Optimization Algorithm [4.93693103484175]
  This paper proposes an adaptive spatial filter solving method based on particle swarm optimization algorithm (PSO) A training and testing framework based on filter bank and spatial filters (FBCSP-ASP) is designed for MI EEG signal classification. The classification accuracy of the proposed method has reached 74.61% and 81.19% on datasets 2a and 2b, respectively.
  arXiv  Detail & Related papers  (2023-10-29T23:53:37Z)
• EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification [79.7946379395238]
  We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption.
  arXiv  Detail & Related papers  (2023-10-24T02:37:49Z)
• Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG [56.155331323304]
  Deep learning based electroencephalogram channels' feature level fusion is carried out in this work. Channel selection, fusion, and classification procedures were optimized by two optimization algorithms.
  arXiv  Detail & Related papers  (2021-12-18T14:17:49Z)
• EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification [123.93460670568554]
  This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based motor imagery (MI) classification. The proposed CNN model, namely EEG-Inception, is built on the backbone of the Inception-Time network. The proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing.
  arXiv  Detail & Related papers  (2021-01-24T19:03:10Z)
• Improving EEG Decoding via Clustering-based Multi-task Feature Learning [27.318646122939537]
  Machine learning provides a promising technique to optimize EEG patterns toward better decoding accuracy. Existing algorithms do not effectively explore the underlying data structure capturing the true EEG sample distribution. We propose a clustering-based multi-task feature learning algorithm for improved EEG pattern decoding.
  arXiv  Detail & Related papers  (2020-12-12T13:31:53Z)
• Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading [59.32570888309133]
  A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs) Non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. A mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
  arXiv  Detail & Related papers  (2020-10-18T20:22:05Z)
• NOMA in UAV-aided cellular offloading: A machine learning approach [59.32570888309133]
  A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs) Non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. A mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
  arXiv  Detail & Related papers  (2020-10-18T17:38:48Z)

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.