Finding tau rhythms in EEG: an independent component analysis (ICA) approach

Poster Session F - Tuesday, April 16, 2024, 8:00 – 10:00 am EDT, Sheraton Hall ABC

Matthew G. Wisniewski¹ (mgwisniewski@ksu.edu), Chelsea N. Joyner¹, Alexandria C. Zakrzewski¹, Scott Makeig²; ¹Kansas State University, ²Swartz Center for Computational Neuroscience, University of California, San Diego

Tau rhythms are largely defined by sound-responsive alpha band oscillations generated within superior temporal gyri. We demonstrate that independent component analysis (ICA) decomposition can effectively identify tau sources and can be used to study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to 3-sec duration complex acoustic stimuli while their EEG was recorded (64 channels). Each subjects' data was split into 60 parallel processing pipelines entailing use of five levels of high-pass filtering (passbands of 0.1 Hz, 0.5 Hz, 1 Hz, 2 Hz, and 4 Hz), three levels of low-pass filtering (25 Hz, 50 Hz, and 100 Hz), and four different ICA decomposition algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi-model AMICA [mAMICA]). Tau-related independent component (IC) effective source processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the alpha band. These "tau IC" sources showed alpha suppression during sound presentation that was not seen in other commonly observed alpha-producing IC clusters. The best performing combination of filters and ICA model choice identified at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between EEG tau IC source dynamics and tau dynamics reported in MEG and intracranial data. These results suggest that using relatively aggressive high-pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in nearly all subjects.

Topic Area: METHODS: Electrophysiology