ArticleClinical neurophysiology practice2026
The impact of Independent Component Analysis on TMS-evoked potentials: a within-subject comparison across motor and prefrontal areas.
Article in Clinical neurophysiology practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Independent component analysis (ICA) is a common method to remove artifacts and improve signal quality in transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) data. However, the impact of applying different rounds of ICA for TMS-EEG datasets are lacking. Here, we investigated the impact of applying zero, one, or two rounds of ICA on TMS-evoked potentials (TEPs) in the motor cortex and prefrontal cortex using a within-subject design. Methods: Twenty-three healthy participants received 51 single-pulses over the left primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC), with simultaneous EEG-recording. Data were preprocessed three times: with zero, one, or two rounds of ICA. We compared the TEP-amplitudes and local mean field potential-area under the curve (LMFP-AUC). Results: After M1 stimulation, preprocessing two ICA rounds resulted in significantly more N40-peaks identified, and smaller P30 (3.84 µV ± 0.78) and P60 (3.08 µV ± 0.96) amplitudes compared to 0 ICA rounds. After DLPFC stimulation, zero ICA rounds led to significantly fewer identified P30 (n = 4) and N40-peaks (n = 5), and larger P60 (5.63 µV ± 1.34) and P180-amplitudes (8.62 µV ± 0.76) compared to 0 ICA. The N100-component remained stable across ICA conditions for both brain areas. Discussion: Our results showed a large impact of ICA, compared to zero ICA rounds, particularly after DLPFC stimulation; likely due to increased eye-blink artifacts. We therefore recommend implementing eye-blink artefact removal during the first round of ICA. In M1, the addition of an ICA round only impacted the early-TEPs. This study is the first to directly compare ICA-effects on both motor and non-motor TMS-EEG data using a within-subject design.
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