E-SAT:基于极端学习机的自我关注方法,用于解码受试者特定任务中的运动图像脑电图。

Muhammad Ahmed Ahmed Abbasi, Hafza Faiza Abbasi, Xiaojun Yu, Muhammad Zulkifal Aziz, Nicole Tye June Yih Yih, Zeming Fan
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引用次数: 0

摘要

脑机接口(BCI)技术的进步实现了人脑与外部外围设备之间的直接通信,从而极大地改善了人们的生活。近年来,机器学习(ML)和深度学习(DL)模型的集成大大提高了 BCI 解码运动图像(MI)任务的性能。然而,这些现有模型仍存在一些局限性,例如训练时间长、对噪声或异常值的敏感性高,这在很大程度上阻碍了 BCI 的快速发展。为了解决这些问题,本文提出了一种新颖的基于极端学习机(ELM)的自我注意(E-SAT)机制,以提高针对特定对象的分类性能。具体而言,在 E-SAT 中,ELM 被用于提高自我注意模块在特征提取方面的泛化能力,以及优化模型的参数初始化过程。同时,还使用 ELM 对提取的特征进行分类,并使用基于 ELM 的端到端设置来评估 E-SAT 在不同 MI EEG 信号上的性能。通过对不同数据集(如 BCI Competition III 数据集 IV-a、IV-b 和 BCI Competition IV 数据集 1、2a、2b、3)进行广泛实验,验证了所提出的 E-SAT 策略的有效性。结果表明,在所有数据集上,E-SAT 的主题分类准确率分别达到 99.8%、99.1%、98.9%、75.8%、90.8% 和 95.4%,优于现有的几种最先进(SOTA)方法。实验结果不仅显示了 E-SAT 在特征提取方面的突出表现,还表明它有助于在其他九种鲁棒性特征提取中取得最佳结果。此外,本研究的结果还表明,E-SAT 在二元分类和多类分类任务中,以及在有噪声和无噪声数据集中都取得了卓越的性能。
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E-SAT: An extreme learning machine based self attention approach for decoding motor imagery EEG in subject-specific tasks.

The advancements in Brain-Computer Interface (BCI) have substantially evolved people's lives by enabling direct communication between the human brain and external peripheral devices. In recent years, the integration of machine larning (ML) and deep learning (DL) models have considerably imrpoved the performances of BCIs for decoding the motor imagery (MI) tasks. However, there still exist several limitations, e.g., extensive training time and high sensitivity to noises or outliers with those existing models, which largely hinder the rapid developments of BCIs. To address such issues, this paper proposes a novel extreme learning machine (ELM) based self-attention (E-SAT) mechanism to enhance subject-specific classification performances. Specifically, for E-SAT, ELM is employed both to imrpove self-attention module generalization ability for feature extraction and to optimize the model's parameter initialization process. Meanwhile, the extracted features are also classified using ELM, and the end-to-end ELM based setup is used to evaluate E-SAT performances on different MI EEG signals. Extensive experiments with different datasets, such as BCI Competition III Dataset IV-a, IV-b and BCI Competition IV Datasets 1,2a,2b,3, are conducted to verify the effectiveness of proposed E-SAT strategy. Results show that E-SAT outperforms several state-of-the-art (SOTA) existing methods in subject-specific classification on all the datasets, with an average classification accuracy of 99.8%,99.1%,98.9%,75.8%, 90.8%, and 95.4%, being achieved for each datasets, respectively. The experimental results not only show outstanding performance of E-SAT in feature extractions, but also demonstrate that it helps achieves the best results among nine other robust ones. In addition, results in this study also demonstrate that E-SAT achieves exceptional performance in both binary and multi-class classification tasks, as well as for noisy and non-noisy datatsets. .

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