IntroductionIn recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their non-stationarity and the substantial presence of noise commonly found in recordings, making it difficult to design highly effective decoding algorithms. These algorithms are vital for controlling devices in neurorehabilitation tasks, as they activate the patient's motor cortex and contribute to their recovery.MethodsThis study proposes a novel approach for decoding MI during pedalling tasks using EEG signals. A widespread approach is based on feature extraction using Common Spatial Patterns (CSP) followed by a linear discriminant analysis (LDA) as a classifier. The first approach covered in this work aims to investigate the efficacy of a task-discriminative feature extraction method based on CSP filter and LDA classifier. Additionally, the second alternative hypothesis explores the potential of a spectro-spatial Convolutional Neural Network (CNN) to further enhance the performance of the first approach. The proposed CNN architecture combines a preprocessing pipeline based on filter banks in the frequency domain with a convolutional neural network for spectro-temporal and spectro-spatial feature extraction.Results and discussionTo evaluate the approaches and their advantages and disadvantages, EEG data has been recorded from several able-bodied users while pedalling in a cycle ergometer in order to train motor imagery decoding models. The results show levels of accuracy up to 80% in some cases. The CNN approach shows greater accuracy despite higher instability.

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探索基于脑电图的运动意象解码：使用空间特征和光谱空间深度学习模型 IFNet 的双重方法

简介近年来，从脑电图（EEG）信号中解码运动想象（MI）已成为脑机接口（BMI）和神经康复的研究热点。然而，脑电图信号由于其非平稳性和录音中常见的大量噪声而带来了挑战，使得设计高效的解码算法变得困难。这些算法对于控制神经康复任务中的设备至关重要，因为它们激活患者的运动皮层并有助于其康复。方法本研究提出了一种使用 EEG 信号在踩踏任务期间解码 MI 的新方法。一种广泛使用的方法是基于使用通用空间模式 (CSP) 进行特征提取，然后使用线性判别分析 (LDA) 作为分类器。这项工作中涉及的第一种方法旨在研究基于 CSP 滤波器和 LDA 分类器的任务判别性特征提取方法的有效性。此外，第二个替代假设探索了光谱空间卷积神经网络（CNN）进一步增强第一种方法性能的潜力。所提出的 CNN 架构将基于频域滤波器组的预处理管道与用于谱时和谱空间特征提取的卷积神经网络相结合。结果和讨论为了评估这些方法及其优缺点，记录了 EEG 数据几位身体健全的用户在自行车测力计上踩踏，以训练运动想象解码模型。结果显示，在某些情况下，准确率高达 80%。尽管不稳定性较高，但 CNN 方法仍显示出更高的准确性。