The paper by P.P. Mitra and B. Pesaran from Bell Laboratories, Lucent Technologies, focuses on the analysis and visualization of dynamic brain imaging data generated by modern techniques such as functional Magnetic Resonance Imaging (fMRI), intrinsic and extrinsic contrast optical imaging, and magnetoencephalography (MEG). The authors develop techniques for separating signal from noise and characterizing the signal, primarily using multivariate time series analysis and the multitaper spectral framework. They detail specific protocols for analyzing fMRI, optical imaging, and MEG data, emphasizing the importance of frequency-based representations and short, moving analysis windows to account for non-stationarity in the data. Key contributions include the development of a space-frequency singular value decomposition (SVD) for characterizing image data and an algorithm based on multitaper methods for removing periodic physiological artifacts. The paper also discusses the challenges and considerations in preprocessing, data representation, and signal extraction from multichannel neural data, particularly in the context of dynamic brain imaging.The paper by P.P. Mitra and B. Pesaran from Bell Laboratories, Lucent Technologies, focuses on the analysis and visualization of dynamic brain imaging data generated by modern techniques such as functional Magnetic Resonance Imaging (fMRI), intrinsic and extrinsic contrast optical imaging, and magnetoencephalography (MEG). The authors develop techniques for separating signal from noise and characterizing the signal, primarily using multivariate time series analysis and the multitaper spectral framework. They detail specific protocols for analyzing fMRI, optical imaging, and MEG data, emphasizing the importance of frequency-based representations and short, moving analysis windows to account for non-stationarity in the data. Key contributions include the development of a space-frequency singular value decomposition (SVD) for characterizing image data and an algorithm based on multitaper methods for removing periodic physiological artifacts. The paper also discusses the challenges and considerations in preprocessing, data representation, and signal extraction from multichannel neural data, particularly in the context of dynamic brain imaging.