Objective. Conventional neural signal analysis methods assume that features of interest are linear, time-invariant signals confined to well-delineated spectral bands. However, new evidence suggests that neural signals exhibit important non-stationary characteristics with ill-defined spectral distributions. These features pose a need for signal processing algorithms that can characterize temporal and spectral features of non-linear time series. This study compares the effectiveness of four algorithms in extracting neural information for use in decoding cortical signals: Fast Fourier Transform bandpass filtering (FFT), principal spectral component analysis (PSCA), wavelet analysis (WA), and empirical mode decomposition (EMD). Approach. Electrocorticographic signals were recorded from the motor and sensory cortex of two epileptic patients performing finger movements. Each signal processing algorithm was used to extract beta (10–30 Hz) and gamma (66–114 Hz) band power...

中文翻译：

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用于分析人体皮层信号的信号分解技术比较

客观的。传统的神经信号分析方法假设感兴趣的特征是线性的、时不变的信号，这些信号仅限于清晰描绘的光谱带。然而，新的证据表明，神经信号表现出重要的非平稳特征，具有不明确的光谱分布。这些特征需要能够表征非线性时间序列的时间和频谱特征的信号处理算法。本研究比较了四种算法在提取用于解码皮层信号的神经信息方面的有效性：快速傅里叶变换带通滤波 (FFT)、主谱分量分析 (PSCA)、小波分析 (WA) 和经验模态分解 (EMD)。方法。从两名癫痫患者进行手指运动的运动和感觉皮层记录皮质电信号。每种信号处理算法都用于提取 beta (10–30 Hz) 和 gamma (66–114 Hz) 波段功率...