Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task.

Biomedizinische Technik. Biomedical engineering Pub Date : 2023-11-08 Print Date: 2024-04-25 DOI:10.1515/bmt-2023-0407

K Venu, P Natesan

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引用次数: 0

Abstract

Objectives: To design and develop an approach named HC + SMA-SSA scheme for classifying motor imagery task.

Methods: The offered model employs a new method for classifying motor imagery task. Initially, down sampling is deployed to pre-process the incoming signal. Subsequently, "Modified Stockwell Transform (ST) and common spatial pattern (CSP) based features are extracted". Then, optimal channel selection is made by a novel hybrid optimization model named as Spider Monkey Assisted SSA (SMA-SSA). Here, "Long Short Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BI-GRU)" models are used for final classification, whose outcomes are averaged at the end. At last, the improvement of SMA-SSA based model is proven over different metrics.

Results: A superior sensitivity of 0.939 is noted for HC + SMA-SSA that was higher over HC with no optimization and proposed with traditional ST.

Conclusions: The proposed method achieved effective classification performance in terms of performance measures.

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混合优化辅助脑电通道选择用于基于深度学习模型的运动图像任务分类。

目的：设计并开发一种用于运动图像任务分类的HC+SMA-SSA方案。方法：所提供的模型采用了一种新的运动图像任务分类方法。最初，部署下采样来预处理传入信号。随后，“提取了基于改进的Stockwell变换（ST）和公共空间模式（CSP）的特征”。然后，采用一种新的混合优化模型——蜘蛛猴辅助SSA（SMA-SSA）进行信道优化选择。这里，“长短期记忆（LSTM）和双向门控递归单元（BI-GRU）”模型用于最终分类，其结果在最后取平均值。最后，在不同的度量上验证了基于SMA-SSA模型的改进。结果：HC+SMA-SSA的灵敏度为0.939，高于未进行优化的HC和传统ST。结论：所提出的方法在性能指标方面取得了有效的分类性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Biomedizinische Technik. Biomedical engineering

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