Zhang Beibei, Bi Ning, Zhang Chao, Gao Xiangping, Lv Zhao
School of Computer Science and Technology, Anhui University, Hefei 230601, China.
Key Lab of Intelligent Computing and Signal Processing, Anhui University, Hefei 230039, China.
Biomed Tech (Berl). 2019 May 27;64(3):309-324. doi: 10.1515/bmt-2018-0018.
Human activity recognition (HAR) is a research hotspot in the field of artificial intelligence and pattern recognition. The electrooculography (EOG)-based HAR system has attracted much attention due to its good realizability and great application potential. Focusing on the signal processing method of the EOG-HAR system, we propose a robust EOG-based saccade recognition using the multi-channel convolutional independent component analysis (ICA) method. To establish frequency-domain observation vectors, short-time Fourier transform (STFT) is used to process time-domain EOG signals by applying the sliding window technique. Subsequently, we apply the joint approximative diagonalization of eigenmatrix (JADE) algorithm to separate the mixed signals and choose the "clean" saccadic source to extract features. To address the problem of permutation ambiguity in a case with a six-channel condition, we developed a constraint direction of arrival (DOA) algorithm that can automatically adjust the order of eye movement sources according to the constraint angle. Recognition experiments of four different saccadic EOG signals (i.e. up, down, left and right) were conducted in a laboratory environment. The average recognition ratios over 13 subjects were 95.66% and 97.33% under the between-subjects test and the within-subjects test, respectively. Compared with "bandpass filtering", "wavelet denoising", "extended infomax algorithm", "frequency-domain JADE algorithm" and "time-domain JADE algorithm, the recognition ratios obtained relative increments of 4.6%, 3.49%, 2.85%, 2.81% and 2.91% (within-subjects test) and 4.91%, 3.43%, 2.21%, 2.24% and 2.28% (between-subjects test), respectively. The experimental results revealed that the proposed algorithm presents robust classification performance in saccadic EOG signal recognition.
人类活动识别（HAR）是人工智能和模式识别领域的一个研究热点。基于眼电图（EOG）的HAR系统因其良好的可实现性和巨大的应用潜力而备受关注。针对EOG-HAR系统的信号处理方法，我们提出了一种基于多通道卷积独立成分分析（ICA）方法的稳健的基于EOG的扫视识别方法。为了建立频域观测向量，使用短时傅里叶变换（STFT）通过应用滑动窗口技术来处理时域EOG信号。随后，我们应用特征矩阵联合近似对角化（JADE）算法来分离混合信号，并选择“干净”的扫视源来提取特征。为了解决六通道条件下的排列模糊问题，我们开发了一种约束到达方向（DOA）算法，该算法可以根据约束角度自动调整眼动源的顺序。在实验室环境中对四种不同的扫视EOG信号（即向上、向下、向左和向右）进行了识别实验。在受试者间测试和受试者内测试中，13名受试者的平均识别率分别为95.66%和97.33%。与“带通滤波”、“小波去噪”、“扩展信息最大化算法”、“频域JADE算法”和“时域JADE算法”相比，在受试者内测试中获得的识别率相对增量分别为4.6%、3.49%、2.85%、2.81%和2.91%，在受试者间测试中分别为4.91%、3.43%、2.21%、2.24%和2.28%。实验结果表明，所提出的算法在扫视EOG信号识别中具有稳健的分类性能。
