Quantifying EEG signal frequency without FFT: a PCA and Poincaré section approach

Saffiera, Cut Amalia and Sase, Takumi and Aini, Qurutol and Rahardja, Untung and Abdul Rahman, Abdul Wahab (2026) Quantifying EEG signal frequency without FFT: a PCA and Poincaré section approach. In: 7th International Conference on Biomedical Engineering. ICOBE 2025, 10–11 September 2025, Kuala Lumpur, Malaysia.

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Abstract

Electroencephalography (EEG) frequency analysis is crucial for understanding brain oscillations, as represented by the alpha band at rest. While the fast Fourier transform (FFT) is standard for quantifying frequency, its assumption of linearity may overlook the nonlinear nature of EEG signals. To address this, we propose a dynamical system-based approach combining principal component analysis (PCA) and the Poincaré section for quantifying EEG frequency without Fourier transform. PCA is used to convert a space spanned by multi-channel EEG signals into a lower-dimensional space while preserving dominant oscillations, and a Poincaré section is placed in this space to estimate frequencies by counting crossings. We applied this method to resting-state EEG data and found it can capture cyclic patterns on a (PC2, PC3)-plane and estimate instantaneous frequencies. The optimal time window of 10 s, as indicated by a clear alpha peak in the frequency histogram and relatively low Kullback–Leibler divergence to the FFT power spectrum (0.221 for PC2 and 0.275 for PC3), suggests that the proposed PCA–Poincaré framework can offer real-time estimates of EEG frequency and be useful in neurofeedback.

Item Type:	Proceeding Paper (Plenary Papers)
Uncontrolled Keywords:	Electroencephalography; Frequency analysis; Principal Component Analysis; Poincaré section; Nonlinear dynamics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr. Takumi Sase
Date Deposited:	20 Oct 2026 10:31
Last Update:	20 Oct 2026 10:31
Queue Number:	2026-10-Q5710
URI:	http://irep.iium.edu.my/id/eprint/131862
Indexed In:	SCOPUS, Google Scholar

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