Prieur-Coloma Yunier, Delisle-Rodriguez Denis, Mayeta-Revilla Leondry, Gurve Dharmendra, Reinoso-Leblanch Ramon A, Lopez-Delis Alberto, Bastos Teodiano, Krishnan Sri, da Rocha Adson F
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3848-3851. doi: 10.1109/EMBC44109.2020.9175263.
This work presents two brain-computer interfaces (BCIs) for shoulder pre-movement recognition using: 1) manual strategy for Electroencephalography (EEG) channels selection, and 2) subject-specific channels selection by applying non-negative factorization matrix (NMF). Besides, the proposed BCIs compute spatial features extracted from filtered EEG signals through Riemannian covariance matrices and a linear discriminant analysis (LDA) to discriminate both shoulder pre-movement and rest states. We studied on twenty-one healthy subjects different frequency ranges looking the best frequency band for shoulder pre-movement recognition. As a result, our BCI located automatically EEG channels on the contralateral moved limb, and enhancing the pre-movement recognition (ACC = 71.39 ± 12.68%, κ = 0.43 ± 0.25%). The ability of the proposed BCIs to select specific EEG locations more cortically related to the moved limb could benefit the neuro-rehabilitation process.
这项工作提出了两种用于肩部运动前识别的脑机接口（BCI），使用：1）脑电图（EEG）通道选择的手动策略，以及2）通过应用非负因式分解矩阵（NMF）进行特定受试者通道选择。此外，所提出的BCI通过黎曼协方差矩阵和线性判别分析（LDA）计算从滤波后的EEG信号中提取的空间特征，以区分肩部运动前和休息状态。我们对21名健康受试者在不同频率范围内进行了研究，以寻找用于肩部运动前识别的最佳频段。结果，我们的BCI自动定位对侧运动肢体上的EEG通道，并提高了运动前识别率（ACC = 71.39 ± 12.68%，κ = 0.43 ± 0.25%）。所提出的BCI选择与运动肢体在皮层上更相关的特定EEG位置的能力可能有益于神经康复过程。
