用迁移学习方法解码想象语音脑电图神经信号

IF 1.1 Q3 PHYSICS, MULTIDISCIPLINARY
Nrushingh Charan Mahapatra, Prachet Bhuyan
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引用次数: 0

摘要

利用脑机接口从脑电波中产生想象的语音,有可能帮助有说话困难或无声交流困难的个体。由于相关测量脑电波的多样性和隐蔽语音数据库的数量有限,已经观察到隐蔽语音的解码效果有限。因此,用于学习和推理的传统机器学习算法具有挑战性,而真正的替代方案之一可能是利用学习迁移。本研究的主要目标是创建一个新的深度学习(DL)解码框架想象演讲脑电图(EEG)信号的任务使用学习和转移到传输源的模型学习任务的想象演讲脑电图数据集对模型训练的目标任务想象的另一次讲话中,脑电图数据集,本质上cross-task学习迁移的区别的特征源任务目标任务的想象演讲。实验使用两个不同的开放获取脑电图数据集,FEIS和KaraOne,记录了来自多个个体的神经信号的想象语音类别。根据所提出的迁移学习,目标FEIS模型和目标KaraOne模型的多类分类总体准确率分别为89.01%和82.35%。实验结果表明,跨任务深度迁移学习设计通过将源任务学习应用于目标任务学习,对想象语音脑电信号进行了可靠的分类。研究结果表明,采用迁移学习对多类想象语音进行统一分类的策略是可行的,从而为未来研究跨任务想象语音分类知识的可用性以推广新的想象语音提示开辟了可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decoding of imagined speech electroencephalography neural signals using transfer learning method
Abstract The use of brain-computer interfaces to produce imagined speech from brain waves has the potential to assist individuals with difficulty producing speech or communicating silently. The decoding of covert speech has been observed to have limited efficacy due to the diverse nature of the associated measured brain waves and the limited number of covert speech databases. As a result, traditional machine learning algorithms for learning and inference are challenging, and one of the real alternatives could be to leverage transfer of learning. The main goals of this research were to create a new deep learning (DL) framework for decoding imagined speech electroencephalography (EEG) signals tasks using transfer learning and to transfer the model learning of the source task of an imagined speech EEG dataset to the model training on the target task of another imagined speech EEG dataset, essentially the cross-task learning transfer of discriminative characteristics of the source task to the target task of imagined speech. The experiment was carried out using two distinct open-access EEG datasets, FEIS and KaraOne, that recorded the imagined speech classes of neural signals from multiple individuals. The target FEIS model and the target KaraOne model for multiclass classification exhibit overall accuracy of 89.01% and 82.35%, respectively, according to the proposed transfer learning. The experiment results indicate that the cross-task deep transfer learning design reliably classifies the imagined speech EEG signals by applying the source task learning to the target task learning. The findings suggest the feasibility of a consistent strategy for classifying multiclass imagined speech with transfer learning, which could thereby open up the possibility of future investigation into cross-task imagined speech classification knowledge usability for generalization of new imagined speech prompts.
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来源期刊
Journal of Physics Communications
Journal of Physics Communications PHYSICS, MULTIDISCIPLINARY-
CiteScore
2.60
自引率
0.00%
发文量
114
审稿时长
10 weeks
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