Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, UNL, CONICET, Santa Fe, Argentina. Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland. Facultad de Ingeniería, Universidad Nacional de Entre Ríos (FI-UNER), Oro Verde, Entre Ríos, Argentina.
J Neural Eng. 2019 Feb;16(1):016019. doi: 10.1088/1741-2552/aaf046. Epub 2019 Jan 9.
Motor imagery brain-computer interfaces (MI-BCIs) based on electroencephalography (EEG), a promising technology to provide assistance and support rehabilitation of neurological patients with sensorimotor impairments, require a reliable and adaptable subject-specific model to efficiently decode motor intention. The most popular EEG feature extraction algorithm for MI-BCIs is the common spatial patterns (CSP) method, but its performance strongly depends on the predefined frequency band and time segment length for analyzing the EEG signal.
In this work, a novel method for efficiently decoding motor intention for EEG-based BCIs performing multiple frequency band analysis in multiple EEG segments is presented. This decoding algorithm uses raw multichannel EEG data which are decomposed into specific [Formula: see text] temporal and [Formula: see text] frequency bands. Features are extracted at each [Formula: see text]-[Formula: see text] band by using CSP. Feature selection and classification are simultaneously performed by means of a fast procedure, based on elastic-net regression, which allows for the inclusion of a priori discriminative information into the model. The effectiveness of the proposed method is tested off-line on two public EEG-based MI-BCI datasets and on a self-acquired dataset in two configurations: multiple temporal windows and single temporal window.
The experimental results show that the proposed multiple time-frequency band method yields overall accuracy improvements of up to [Formula: see text] (average accuracy of 84.8%) as compared to the best current state-of-the-art methods based on filter bank analysis and CSP for MI detection. Also, classification variability is reduced, making the proposed method more robust to intra-subject EEG fluctuations.
This paper presents a novel approach for improving motor intention detection by automatically selecting subject-specific spatio-temporal-spectral features, especially when MI has to be detected against rest condition. This technique contributes to the further advancement and application of EEG-based MI-BCIs for assistance and neurorehabilitation therapy.
基于脑电图（EEG）的运动想象脑-机接口（MI-BCI）是一种有前途的技术，可以为感觉运动障碍的神经患者提供辅助和支持康复，需要一个可靠且可适应的特定于主题的模型来有效地解码运动意图。用于 MI-BCI 的最流行的 EEG 特征提取算法是共空间模式（CSP）方法，但它的性能强烈依赖于用于分析 EEG 信号的预定义频带和时间段长度。
在这项工作中，提出了一种用于有效地解码基于 EEG 的 BCI 的运动意图的新方法，该方法在多个 EEG 段中执行多个频带分析。该解码算法使用原始多通道 EEG 数据，将其分解为特定的[公式：见文本]时间和[公式：见文本]频带。通过使用 CSP 在每个[公式：见文本]-[公式：见文本]带中提取特征。通过基于弹性网回归的快速过程同时进行特征选择和分类，这允许将先验判别信息纳入模型。该方法的有效性在两个公开的基于 EEG 的 MI-BCI 数据集和一个自行采集的数据集上进行了离线测试，在两种配置下进行：多个时间窗口和单个时间窗口。
实验结果表明，与基于滤波器组分析和 CSP 的 MI 检测的当前最佳最新方法相比，所提出的多时间频带方法总体准确率提高了高达[公式：见文本]（平均准确率为 84.8%）。此外，还降低了分类的可变性，使所提出的方法对个体内 EEG 波动更具鲁棒性。
本文提出了一种通过自动选择特定于主题的时空光谱特征来提高运动意图检测的新方法，特别是在必须在休息状态下检测 MI 时。该技术有助于 EEG 基 MI-BCI 的进一步发展和应用，以提供辅助和神经康复治疗。
