Analysis Techniques for Weak Brain Activity Alain de Cheveigné CNRS / Université Paris Descartes / École normale supérieure / UCL Ear Institute AIM Isolate weak sources of brain activity within multichannel data (EEG, MEG, LFP, optical imaging). Motivation Refs: de Cheveigné, A. (2012). Quadratic component analysis, Neuroimage, 59, de Cheveigné, A. (2010). Time-shift denoising source separation, J. Neurosci. Methods 189, de Cheveigné, A. and Simon, J.Z. (2008) Denoising based on spatial filtering, J. Neurosci. Methods, 171, de Cheveigné, A. and Simon, J.Z. (2008) Sensor noise suppression, J. Neurosci. Methods. 168:, de Cheveigné, A. and Simon, J.Z. (2007) Denoising based on time-shift PCA, J. Neurosci. Methods. 165, de Cheveigné A,Simon JZ, 2007, Denoising based on spatial filtering, J Neurosci Methods 171, Särellä J, Valpola H, Denoising source separation. J. Mach. Learn. Res. 6, Brain activity involves billions of distinct sources. In principle, our view is limited by the number of sensors, electrodes or pixels. In practice, we can usually only resolve a handful of sources, typically one or two, such as the ubiquitous M100 of a sensory response. Strong activity is not necessarily the most interesting, and it is desirable to probe behind it to extract weaker sources. Linear techniques "peel away" strong sources of cortical or environmental noise, and isolate weak activity of interest. New Techniques TSPCA (Time Shift PCA) Same as regression, but time-shifted regressor produces optimal FIR filters. Use if an independent observation of noise is available. Handles convolutional mismatch between ref and data.. Toolbox: SNS (Sensor Noise Suppression) Partitions between channel-shared and channel-specific activity. Remove sensor noise (e.g. MEG), or isolate local activity (e.g. LFP). DSS (Denoising source separation) "Swiss army knife" of multichannel data analysis. Use to synthesize a spatial filter that optimizes a particular criterion (e.g. evoked activity, or specific spectral profile). TSDSS (Time-shift DSS) Use to synthesize optimal multichannel FIR filter (both spatial and spectral selective) to optimize some criterion (e.g. evoked activity). QCA (Quadratic Component Analysis) Similar to DSS, but optimizes quadratic forms (cross-products). Can isolate weak induced activity (power repeatable over trials). Covariance clustering Use when data switches between states (e.g. brain or noise sources not active all the time) channel ECoG in monkey. Beta band activity related to wake/slee p transitions. 440-channel MEG (Yokogawa system at ATR, Japan). Auditory onset response (28 repetitions). 160-channel MEG (Yokogawa system at Konan Hospital, Japan). Empty system. 275-channel MEG (CTF system at CENIR, Paris). Empty system. 9-channel small- animal MEG at UCL EI. Auditory response (6400 repetitions). Simulated data illustrating power of TSDSS. Data: 10 channels. Noise: mix of 5 independent sources with space/time correlation. Simulation. Data: 10 channels. Noise: mix of 9 independent gaussian sources. SNR= MEG data from study of Duncan et al, HBM 2010, visual task. QCA applied to 30-Hz highpass data reveals a narrowband component near 50 Hz, with time course similar to that obtained by beamformer in original study. original: Overall: Improvements can be large! 128-channel ECoG in monkey. delta band activity related to wake/sleep transitions. Black indicates first cluster (of two, color second). Clustering is applied to time series of cross- products of the first 30 principal components of the data.